Patent Application
20240259465
Edge computing, at a general level, refers to the transition of compute and storage resources closer to endpoint devices (e.g., consumer computing devices, user equipment, etc.), in order to optimize total cost of ownership, reduce application latency, improve service capabilities, and improve compliance with compute security or data privacy requirements. Edge computing may, in some scenarios, provide a cloud-like distributed service that offers orchestration and management for applications among many types of storage and compute resources.
Some implementations of edge computing have been referred to as the “edge cloud” or the “fog,” as powerful computing resources previously available only in large remote data centers are moved closer to endpoints and made available for use by consumers at the “edge” of the network. The use of edge computing, and the many flavors of distributed or centralized cloud computing, have led to a variety of technical issues involving security, reliability, and resource usage.
In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. Some embodiments are illustrated by way of example, and not limitation, in the figures of the accompanying drawings in which:
In the following description, methods, configurations, and related apparatuses are disclosed for implementation in a cloud environment or cloud-to-edge (C2E) framework. As used herein, “cloud-to-edge” generally refers to functionality to move workloads and capabilities that were traditionally located in a cloud computing setting towards distributed edge computing locations. Such functionality is particularly applicable to the deployment and execution of elastic workloads (WLs), which involve workloads that are distributed across multiple nodes, migrated, and dynamically coalesced.
Edge computing has introduced scenarios involving elastic workloads where a traditional monolithic workload, which may run on a single Edge node, may be decomposed into two or more sub-workloads that are distributed across multiple Edge nodes. The distributed workload may be partially or fully consolidated or decomposed even further to accommodate the changing resource dynamics of edge-cloud deployments for both stationary and mobile users and user equipment, or stationary and mobile edge nodes. These dynamics creates an environment for elastic edge computing capabilities that include dynamic binding of workloads, resources, and compute.
The use of elastic WLs is designed to provide flexibility to accommodate distribution and dynamism inherent in edge computing. A dynamic C2E framework is needed to ensure that trust within a complex elastic WL infrastructure is preserved throughout the many types of WL configurations and use cases.
Existing container-as-a-service (CaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS) capabilities expect that trust is established statically at an initial deployment of a WL, and such capabilities generally will not re-evaluate trust during WL execution. The dynamics of an elastic WL in an C2E deployment, however, change the WL from being a monolithic WL to a distributed WL having dynamic properties that distribute the WL processing across multiple nodes. This is further complicated because nodes may dynamically migrate to other hosting environments at lower framework layers, resulting in broken trust semantics.
The systems and methods described herein implement an elastic WL framework with security and trust capabilities at operational layers involving workload execution environments (e.g., containers, virtual machines, etc.), platforms, and infrastructure, and these layers' associated services (Container-as-a-Service (CaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS)). The following systems and methods introduce a Trust Binding Manager to actively monitor and apply trust bindings between the artifacts at respective layers that require consistent trust properties, while responding to dynamic conditions that otherwise will break trust properties. This enables elastic WL frameworks to establish and preserve intended trust properties of a WL throughout the WL execution and lifecycle despite the occurrence of dynamic changes in resources, location, and data sources/sinks.
A trusted C2E services framework for elastic workloads is adapted as follows to ensure various “anything-as-a-service” (X-aaS) capabilities in a services framework. This is used to establish and maintain trust within the respective framework layers, e.g., CaaS, PaaS, and IaaS, and between C2E framework layering, e.g., CaaS to PaaS, and PaaS to IaaS. The Trust Binding Manager component is used to establish and maintain trust properties for an elastic WL that is implemented using a trusted C2E framework. The C2E framework integrates a Trust Binding Manager component that enables creation, simulation, deployment, and maintenance of trusted elastic WLs following a WL lifecycle.
The CaaS layer 102 creates a uniform abstraction for describing a workload that is independent of a particular platform. A distributed WL may partition the WL into logical sub-workloads that are related by a workflow model where the partial results from one sub-workload may be input to another sub-workload. The workflow may divide WL computations into execution operations that are in a particular sequence or in a particular concurrence. An individual sub-workload should have the same trust semantics as the monolithic WL. Monolithic WL trust may be established as part of an SLA (service level agreement) between the WL tenant and a WL service provider. Elastic WL execution may result in a distributed WL having multiple sub-workloads hosted by many nodes. The WL partitioning and execution workflow may introduce challenges to trust where the expected trust agreed to initially as part of an SLA agreement may disappear due to differences in platform and infrastructure options introduced by PaaS and IaaS framework layers.
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The systems and methods described herein provide CaaS, PaaS, and IaaS layers with trusted computing capabilities that ensures the security and trust properties of the workload are represented and enforced at appropriate CaaS, PaaS, and IaaS layers. With use of the aforementioned trusted C2E capabilities, security and trust can be provided even if every infrastructure layer has an X-aaS abstraction.
The IaaS layers 500 are adapted to provide trusted computing capabilities, using features of a trusted IaaS environment 510 that are supplied by trusted hardware. The relevant trusted computing capabilities include but are not limited to: HW roots of trust, trusted execution environments, edge attestation, attestation evidence collector/lead attester (e.g., “PaRoT”), pre-attested functions, trusted telemetry collection, update management, appraisal policies, evidence policies, attestation ecosystem roles, and interfaces for the underlying trust, management, and telemetry capabilities.
Trust in the container begins as part of workload authoring where the WL author supplies security intents and other intents metadata 602 that describes the parameters of trust such as expected attestation values and results. Workload authoring may also include elastic WL properties that aid in decomposing a monolithic WL into distributable parts using metadata in the form of WL intents, security intents, and data intents. This metadata may describe expected or allowed WL, module, and data composition/decomposition points as well as data ingress/egress behavior for WL nodes that optimize for remote data hosting with data flow ingress/egress. Additionally, data sensitivities are described by metadata that include policies that describe safe HW and SW environments where sensitive data may be safely and confidentially manipulated such as Intel® SGX/TDX, ARM® TrustZone, AMD® SEV, etc.
The trust binding manager 606 ensures that the binding occurs following an expected binding procedure, e.g., website-for-the-iaas-platform.com/proc-X, using an expected container socket, e.g., ‘socket-A@Workload-A’, with an expected isolation factor, e.g., ‘0.88’. The binding operation may produce a token that is given to WL A and presented to an IaaS resource hosting a processing unit (e.g., a networked infrastructure processing unit or “IPU”) where it is evaluated upon resource access to verify that the binding operation has taken place. The token may expire to ensure the binding operation is periodically reestablished or the token is dynamically regenerated if either the resource environment or the container environment changes in a way that affects trust.
Elastic WL updates are more sophisticated than traditional cloud or edge WL updates because elastic WLs may be partitioned and distributed dynamically after the orchestrator commissions the WL for execution. Elastic WLs possess metadata that describes the various ways that a WL may be partitioned and distributed such that the global execution objective remains the same after elastic partitioning and distributed hosting. Nevertheless, elastic WLs are subject to updates and patches that correct bugs, close security holes, or provide efficiency, reliability, resiliency, and availability improvements. Updates will comprehend the dynamically applied partitioning and distribution functions that have been applied or updates will likely fail. Otherwise, the partitioning and distribution optimizations may need to be backed out, and then the update can be applied before reapplying the partitioning and distribution operations, which incurs significant deployment cost.
The systems and methods described herein address gaps in workload software and firmware updates, particularly through the application of attestation and stronger platform integrity management. The broader industry is demanding these capabilities (e.g., attested workloads) as part of a “Zero-Trust Architecture”, see SP 800-207, Zero Trust Architecture, published by the NIST Computer Security Resource Center (CSRC) which defines a set of requirements and principles for trustworthy enterprise, edge and cloud deployments. In particular, the systems and methods described herein solve the problem of uncoordinated and disruptive updates for elastic WLs by leveraging elastic WL metadata that describes the ways in which a WL may be partitioned and distributed. The metadata is used to design WL update images that align with current deployments. The most appropriate update image is delivered to the currently deployed WL fragment for application/installation. The approaches include elastic WL metadata that is used to construct, distribute, and apply WL updates. The approaches include use of an elastic WL Distribution Manager (WDM), Workload Update Manager (WUM), Pod Update Manager (PUM), and other infrastructure.
If a WL—or pod of containers that implements the WL—is part of an elastic deployment (e.g., replica set), the systems and methods described herein use an API to temporarily disable the workload deployment following the approach: “do not try to adjust number of active copies.” If the instance on the machine that needs an upgrade is paused, the deployment operator would require new resources to recreate the pod elsewhere. After the WL/Pod is upgraded, the WL/Pod in that machine is restarted. This approach saves time, overhead and cost, whereas downloading the pod image elsewhere, allocating resources, setting up IP connectivity, etc. is significant. A respective infrastructure node has a local PUM, and this local PUM interacts with a WUM that coordinates application of updates across the cluster of elastic WL containers and nodes.
A respective K8S Pod Manager may be used to manage and track multiple WL fragments hosted by a common IaaS partition. For example, an IaaS Node X 821 may host fragments (0, 1) of a WL while Node Y 822 hosts fragments (2, 3) and Node Z 823 hosts fragments (n, n+1) and so forth.
It is understood that the Elastic Infrastructure Node (EIN) may support multi-tenancy where different tenants operate their own pod of containers. Examples described herein also depict a single-tenant scenario.
Distributed workloads in an elastic WL are images that are in various stages of a deployment lifecycle involving creation, distribution, resource binding, execution, update, coalescence, and retirement. Elastic WLs have elasticity parameters that are described by metadata (e.g., where WL node replicas can exist and where several nodes serve as redundant nodes that may perform parallel execution of a node). Additionally, pipelined execution can exist where the input of one node in an elastic WL cluster is satisfied by the output of another cluster node forming a chain of operations that execute in sequence. Elastic WL data objects may be assigned to one or more WL nodes for shared or exclusive access. A hierarchical lock structure may be used to guarantee sequential and parallel executions can occur simultaneously while still maintaining overall integrity of the elastic workload.
A WL node may have multiple execution states, (e.g., ready to run, running, blocked on hosting resource, blocked on input, blocked on output, blocked on partitioning, frozen, zombie, etc.). The WL node's image therefore can be copied, replicated, stored, encrypted, etc. to comply with node lifecycle, security, resiliency, and durability requirements.
An elastic WL has various lifecycle and execution states that can be represented as one or more file images. A “pod” filesystem therefore can be used that allows WL images to be manipulated by a distributed files system interface (e.g., IPFS, GFS, HDFS, Cefph) where a distributed filesystem hierarchy contains WL images are serialized Pods, Containers, Workloads, Distributed Workloads, etc. that map to objects in an Elastic Workload Filesystem (EWFS). As used herein, an EWFS refers to any cloud- or edge-distributed filesystem containing serialized WL images. The EWFS configuration may be described by metadata such as SWID or CoSWID that models a filesystem abstraction, where a distributed filesystem is also described by a filesystem abstraction. Here, different nodes in an elastic cluster can a namespace in a filesystem hierarchy and the various elastic workload lifecycle states may be represented as files or sub-directories of the EWFS filesystem namespace. The EWFS metadata may be used to specify an expected deployment configuration, deployment status context, deployment lifecycle archive, and elastic workload data distribution model.
The elastic WL configuration enables performance improvements while being cyber-resilient. Elastic WL telemetry and AI/ML algorithms may add greater context for leveraging WL archives that quantify WL lifecycle overhead (e.g., pause/restart vs. decommission/recommission). For example, the elastic WL equivalent of a suspend-resume state “S3” in an operating system process suspend/resume may record resource utilization, latency, network bandwidth and so forth. Thus, the overhead that is associated with WL lifecycle transitions can be analyzed, when these measurements are available for analysis and optimization of WL microservices.
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Traditional workload simulation environments create a test framework that employs test automation controls. These test automation controls are designed to exercise a workload given a synthetic data set and prescribed test case functions and deployment parameters. Workload execution may be instrumented and logged to validate expected intermediate results. Workloads operate with an expectation of ready access to resources.
This existing approach is insufficient for testing elastic workloads in simulation due to elastic resource requirements and competition for resources as workloads fragment, proliferate, and compete for limited resources. Elastic workloads are comprised of both executable flow and data sources or data sinks where workflow logic targets data repositories for sourcing or syncing persistent data. The systems and methods described herein provide a simulation environment that addresses these challenges.
As stated, existing workload simulation solutions do not exercise the functionality and resource management limitations anticipated when deploying elastic workloads. The problem with deploying elastic workloads into production networks that do not simulate and test resource starvation, oversubscription, and availability given workload elasticity dynamics means that the production network is at risk of workload failure, slow down, security, or resiliency events. Such risks are particularly possible as new or updated elastic workloads are introduced to Edge and Cloud-connected Edge networks, resulting in increased operational costs, failure to meet service level agreements, damage to resources, and even physical harm.
The implementation described herein leverages existing workload simulation building blocks with a simulated infrastructure resource supply and elastic intents. These intents can be used to enable multiple systems under test to compete for a simulated finite set of resources as elastic intents are used to direct workloads to fragment and distribute across the simulated resource infrastructure. Multiple distribution and coalescence scenarios can be exercised while also manipulating resource availability profiles that fully exploit the effects of elasticity for a given set of elastic workloads. This also enables testing of elastic workload behaviors in simulation before subjecting them to production deployments where risks of misbehavior are multiplied.
Simulation of elastic workload operations prior to real-world deployment is important to ensure robust continuous operation. Such simulation requires a representation of all resources for the target system under test (SUT) environments such that they may bind to the elastic workload nodes that may further be distributed and fragmented as part of workload elasticity. The resource simulation framework specifies resource allocation profiles and WL node assignment and how resources are bound to WL nodes. Binding may further require attestation that ensures WL security sensitivity requirements can be met by a resource allocation and assignment.
Simulation is also important for critical infrastructure deployments to ensure that use of new or modified WLs do not introduce failures. A key component to the improved implementation is the use of metadata that describes the simulation environment, the simulated workload, and the resource profile. The simulation environment orchestrates the orchestrator, resource managers and other control surfaces in the elastic workload environment. The simulation environment also leverages the workload metadata that specifies security, resiliency, and distribution intent (called ‘intents’) that is used for normal operation of an elastic workload.
The WLs 1201, 1202, 1203 may be unrelated in terms of their collaboration objectives, or they may be in competition for assets (e.g., goods and services—not just resources). For example, WL A 1201 in a supplier network might estimate a supply of 100 cameras is needed, while WL B 1202 may require 200 cameras. If WL C 1203, a camera supplier, can produce 250 cameras, there is a net deficit of 50 cameras. If the test objective is to determine whether WL A 1201 behavior can dynamically adapt to a new change in available resources, WL goods and services, etc., then the simulation environment will accommodate multiple simultaneous workloads.
Simulation itself is a workload that may iteratively model the most basic and important resource bindings, followed by resource fragmentation and re-binding as elastic WLs fragments are distributed. This modeling is followed by resource starvation, sharing, over subscription, failover, and so forth. Although Kubernetes (K8S) testing frameworks consider CPU, memory, and disk as important resources to track and manage, existing K8S testing frameworks do not fully support advanced distributed resource contention scenarios or scenarios.
The resource simulation framework is highly scalable to use common resource behavior profiles that model and simulate starvation, distribution, fragmentation, oversubscription, failover etc., including with models that can cover a spectrum of use cases and deployments. Additionally, resource profiles may be used in the following framework to model multiple trusted resources as a way to exercise the full flexibility of elastic intents (especially where WL fragmentation and distribution might not stop at a single pod/cluster). Such WL fragmentation and distribution may result in multiple clusters that organically form around data lakes, availability of acceleration farms, or mobile trajectories common to Multi-access Edge Computing (MEC).
In further examples, the resource simulation framework may monitor resource utilization across various WL clusters to eliminate clusters that do not meet KPIs or latency requirements (e.g., such as due to data lake access latency, set to a high bar policy). Additionally, the WL KPI test metrics (e.g., CPU utilization) may be combined with cybersecurity attributes (e.g., redundancy), including to incorporate a toolbox for cyber-resilience testing (e.g., Mitre's ‘Att&ck’ toolbox) and produce an audit report containing a cybersecurity gap analysis. Toolbox extensibility may include Digital Twin (DT) support where platform implementations that support DT features may instantiate mirror copies of a WL node resulting in additional pressure on overall resources.
In still further examples, the resource simulation framework may define a simulation pod “SimPods” abstraction where an elastic WL is fragmented and distributed to form a ‘pod’—similar in concept to a K8S pod—where pod behavior is the focus of simulation. The simulation objective then can be performed to optimize resource allocation and assignment according to the SimPod.
Elastic C2E workloads may be continuously integrated and deployed (e.g., with continuous integration and continuous delivery/continuous deployment (CI/CD) software practices). Security vulnerability events (e.g., Common Vulnerabilities and Exposures (CVEs)) may be reported that render portions of attested elastic workloads unsecure despite having contradictory attestation results. CVE detection, correction, and redeployment of elastic workloads is challenging due to the dynamic nature of elastic workloads because the elastic workload can be distributed across multiple nodes, migrated, and coalesced again dynamically independent of workload orchestrator knowledge. The following implementation uses a cloud-edge cluster manager and late binding of workloads and workload data to execution resources to implement workload elasticity properties. This implementation enables CVE notifications and re-attestations to be applied as part of a dynamic ‘top-half’ and ‘bottom-half’ binding operation.
Existing solutions rely on CVE notifications to software supply chain developers and operators who then review software inventories to determine if any deployed products are affected by a CVE. This approach requires careful records and diligence on behalf of the software vendors to determine if a released binary actually is affected by a CVE. This approach does not scale because elastic workloads are dynamically partitioned according to metadata that is embedded in the workload, making it nearly impossible for an operator to predict which deployed nodes are affected by a CVE at any given time.
The implementation described herein integrates attestation with elastic workload deployments using orchestrators for top-half workload deployments, and cluster resource managers that provide bottom-half resource management. In the bottom-half resource management, the binding of resources to a workload (or elastic workload fragment) is subject to attestation and CVE assessment. The elastic workload is designed using a metadata scheme that identifies the possible ways that the workload might be dissected during deployment to optimize available resources. The metadata may be used to assess a CVE and determine which workload fragment in a deployment is affected.
Workload migration and update mechanisms are repurposed to seamlessly apply CVE-driven countermeasures that patch/update workload fragments in replica or digital twins that may undergo a loss of operational integrity as a result of applying the countermeasure. The replica workload fragment may be used to migrate a functioning workload fragment from a vulnerable node to a non-vulnerable node seamlessly and without requiring the distributed workload to be coalesced. This enables elastic workloads that have been partitioned and optimized for maximum resource efficiency to remain operational in their optimized form, while still being able to detect and apply CVE resolutions.
The top half 1302 is bound to bottom half 1304 resources as part of a C2E Cluster deployment. Initial deployment may allocate a set of top half nodes that are scheduled to run using bottom half resource nodes. Binding activity may involve attestation of the resources by the top half 1302 prior to binding to a bottom half 1304 or attestation (e.g., WL/data provenance evaluation) of the top-half workload and data by a bottom-half node prior to binding. Binding may be facilitated by orchestration or resource manager nodes.
After binding, the resultant cloud-edge node may begin processing the workload. Distributed workloads may produce output that is consumed by a peer cloud-edge node originating from the same monolithic workload. Peer cloud-edge nodes that act as consumers or producers of other peers may attest the peer prior to consuming WL inputs from a peer or prior to sending WL outputs to a peer. Mutual attestation ensures the peers are indeed peers and that they are bound to resources that satisfy an overarching and consistently secure workload policy.
The top-half update package 1504 also may contain updated workload code, settings, configuration parameters, quality-of-service policies, key performance indicators, metrics, data source, data sink parameters, etc. It may contain attestation information in the form of reference values, endorsed values, device identity certificates and other information used to attest the workload and to verify peer workloads.
An orchestration agent or cluster resource manager may be used to perform migration operations to update a deployed C2E cluster node. For example, a deployed workload may be modified to add, remove, or change workload behavior. Similarly, deployed resources may be updated to install or replace hardware modules, device drivers, system software, framework software.
When updates are applied, they may invalidate current attestation and trust contexts. Hence, application of an update can affect the risk management posture of the workload or operational resources even if there is no change to behavior or data. Some or all of the operational prerequisites (such as attestation) may need to be redone after applying an update. This process of updating and re-attesting an updated C2E cluster node is referred to as Continuous Evaluation (CE). When various components of a C2E cluster node are attested separately (e.g., workload, operating system, firmware, hardware, etc.), an update applied to one component may invalidate the attestation for the entire C2E cluster node. However, with component-level attestation, an update to one component may cause a re-evaluation of that component and a new attestation to be issued for the updated component. This new attestation may be integrated with other component-level attestations for the C2E cluster node. Whether workload migration is required may be controlled by attestation, security, trust, operational, or other intents.
If the workload has availability requirements that disallows halting the workload to apply the update and re-initialize the trust context, but retains the requirement to maintain robust trust context, the C2E cluster node may employ an update migration method designed to minimize workload disruption. Prior to the update package being installed, a new cluster node is provisioned, and the update package applied to the new node. In the case where a top-half workload node is to be updated, a new workload node may be provisioned and updated. Similarly, a bottom-half resource node may be provisioned, and update applied. The newly provisioned node may be attested and verified according to attestation procedures.
The update process involves migrating the currently executing workload (or resource) node to the newly provisioned node. There are two forms of migration, remote and local. Remote migration involves C2E nodes that are hosted on different physical platforms, while local migration is applied to logical or virtual contexts on the same physical platform.
Similarly, an update to the bottom-half component 1620 may proactively allocate a migration target 1614 that is a clone of the provisioned resource 1612 to which the update is applied (with update operations 1618). After the update operations 1618, the bottom-half target may be attested to account for measurement changes resulting from applying the update. The top-half migration target 1610 and the bottom-half migration target 1614 are re-bound to re-establish an attested trust relationship between the two halves (depicted by diagonal lines in
After re-attestation occurs, the connection state may need to be reestablished between top and bottom half entities. Information consumed by the top-half from the bottom-half is contingent on the trust dependency context. If the trust context is disrupted, the data and control planes are also disrupted. Reestablishment of a control/data plane context may include reestablishment of connection state. For example, if a Transport Layer Security (TLS) or Security Protocol and Data Model (SPDM) connection existed before, the session keys may be renegotiated. The protocol key exchange may call for inclusion of Attestation state in session key generation or may log session handshake messages showing the attestation state, or session state (such as a session MACing key) may include attestation state. Session reestablishment refreshes the session state to reflect the trust impact from having applied the updates.
When updates 1720 are applied at a bottom-half resource to update an IO device, a new IO device driver context (virtual IO device) is created that has an update 1718 applied, such as an updated driver. The previous driver or IO device remains operational during this process started with a migration 1716. The new (updated, virtual) device 1710B establishes a new connection to the security-services module 1712 which may include attestation of the new device 1710B. The security-services module 1712 advertises availability of the new device to the guests. The guests that depend on the IO device may, at their convenience, open a connection to the new device 1710B and close the connection to the previous device. The data on the device remains available and consistent across both virtual devices as a property of virtualization abstraction.
Updates 1702 that are applied to the top-half guest or secure enclave 1708A to 1708N may impact trust dependency on lower layer IO devices. A proactive migration 1704 may apply to top-half nodes as well. A new migration target 1710A (guest or secure enclave) is created with a clone of the guest/enclave to be migrated, and then the update 1706 is applied. The migration target 1710A creates a new connection to the security-services module 1712 which multiplexes with the IO devices. The old enclave/guest migrates workload and data to the migration target 1710A and begins using the target's attested connection to the security-services module 1712. Migration of the workload and data may be further facilitated by virtualizing the workload and data into memory that is shared across old and new target. The updated and attested part of the workload are held separately.
When the node completes migration, the attested state of the new node reflects the update. When the node interacts with a peer node that requires knowledge of the node's attestation state, the new node may re-attest to an orchestrator, attestation service or the peer node directly to obtain an attestation token representing the updated state. Previously minted tokens are discarded in favor of the most recently issued token. Tokens may expire requiring periodic reissuance.
This implementation uses intent-based declarative operations that describe the various ways an elastic workload can be partitioned and distributed. Thus, the precise resource requirements for a WL partition are readily available to orchestrators or cluster resource managers for dynamic distribution or coalescence operations.
In further examples, the elastic workload infrastructure may be diverse in terms of hardware and software BOM that are used. The CVEs may be prioritized based on rank (e.g., ranking of an impact if compromised for a given vulnerability) and this rank may assist in the upgrade strategy that is applied across the orchestration and deployment infrastructure. This may leverage residual compute resources to minimize operational runtime impact on the upgrade process by providing recommendation to the backend services. A small amount of partitioned capacity may be reserved in respective nodes so that the workload can be migrated to its optimized partitioned capacity first (without having to drain the workload or migrate it long distance), and then restored to full capacity after a CVE fix is applied and rebooted.
Manifest standards contain BOM lifecycle intents where updates and patches are tracked by a BoM. A challenge is the older images do not know about newer images that superseded them. However, newer images may contain references to older images (in the BoM) such that a cluster resource manager can trace the lineage and determine if a CVE applies to an older revision of a workload fragment. An update service may be needed that maintains a database of update dependencies such that older images that are not aware of newer images can be searched efficiently. A publish/subscribe system could be used to multi-cast lifecycle notifications so that workload fragments are aware of the most current images and which lifecycle states apply for current configurations. Other secure environments (such as Intel® TDX, AMD® SEV-SNP (Secure Encrypted Virtualization, Secure Nested Paging), ARM® Confidential Compute Architecture (CCA), or ARM® TrustZone) may be used as a more secure hosting environment for elastic workloads that uses an IO resource. The IO resource driver or firmware may be updated by its device vendor or an administrative service provider that is not controlled by the workload orchestrator or cluster manager. The update may change the attested and assessed trust context resulting in a stale attestation result. Secure execution applications may want to prevent updates so they can optimize on a particular KPI vector while retaining a known attestation result. However, elastic cloud-to-edge may not have a central orchestrator that maintains attestation and update consistency. Elastic workload intents may describe the artifacts of the workload that should be negotiated in terms of how to effectively trade-off attestation integrity with operational availability.
Operational intents, attestation intents, software bill of material intents, security intents, or other policies (e.g., SLA or SLO) may be used by orchestration or resource manager nodes to assign workloads to resources, bind top and bottom halves, migrate workloads, monitor nodes, or perform other workload lifecycle operations. Intents, such as security intents, dimensions, and cores, which are described below, may be used in combination with intents that specify a minimum, maximum, target, or range of a cost, billing, bandwidth availability, compute availability, latency, or the like.
A CVE may differentiate between an exterior and interior impact. By applying a patch to a component outside the workload (BoM of Platform) may be effective way to mitigate the threat (e.g., by applying a software firewall) or patching may be applied within the workload (BoM of WL) to correct a vulnerability (e.g., by closing a buffer overflow).
If there is an increasing CVE backlog, a backlog threshold (e.g., a minimum rate of burning backlog is greater than rate of new CVEs including weighted severity) may define a grace period with policy-based rules where the update can be safely applied. Cyber Resiliency is affected in that a Cyber-Resilient Triad exists where the workload, data, and host node encounter independent CI/CD/CE processes, but the triad is reconciled by metadata intents that allow the CVE impact to be traced to a specific workload fragment and resource that contains the vulnerability. Through CE, the workload fragment or resource may be evaluated, updated/patched, re-attested, and re-deployed.
Distributed compute and networking have evolved into a hyper-connected compute continuum with the introduction of various C2E approaches. C2E approaches are expected to be adopted by many industry participants, including cloud service providers (CSPs), communications service providers (CoSPs), and enterprise providers. C2E approaches enable the transition from vertically integrated, industry-specific solutions to horizontal platforms where all compute nodes can execute workloads, produce, and consume data.
Industry participants may have the view that unreliable security hinders C2E operations. Security requirements cannot be addressed without establishing trust between the elements of the compute continuum. Protecting edge components with intrinsic security of applications, data and infrastructure is also a concern. This complex technical problem, driven by multi-party supply chains and customer interactions, also needs to be scalable.
Existing attempts for trust establishment have been based on siloed, vertical attestation for a compute node or workload (e.g., Intel® SGX attestation, Secure Boot using Trusted Platform Module (TPM), DICE, or SPIRE). Accordingly, there are not suitable Data Provenance attestation solutions available, and existing approaches are limited to per-app or per-microservice implementations. For example, consider the use of OPA (Open Policy Agent), which is an open source, general-purpose policy engine that unifies policy enforcement across a software stack. It provides a high-level declarative language to specify policy as code and APIs to offload policy decision-making. However, OPA lacks the aggregation of the assertions and ability to reassess the assertions (metadata) runtime.
There are other deficiencies with existing approaches for trust establishment. For instance, implementations follow needs, which may create lag in deployments. Further, there is fragmentation and redundancy among many approaches, resulting in failure-prone implementations. There are also implementation inefficiencies, such as hard-coded policies, which are difficult to maintain. Thus, existing implementations often have a lack of consistency, use an ad hoc format, are standard-adverse, are difficult to scale (being embedded in other code/product), and are difficult to secure (and if they are embedded, they may need additional technical solutions).
The following addresses aspects of multi-party trust coordination, to provide trustworthiness from the exchange of metadata, evaluation of metadata per policies, and decision making. This trust coordination is applicable to complex C2E compute environments that enable multiple parties to provide heterogeneous metadata and associated policies and entity capabilities. Further, the following also provides approaches to collect, analyze, make decision, and reassess the trustworthiness assertion based on policies and entity capabilities.
Systems and methods described herein provide for trust coordination across heterogenous elements in an C2E setting, with use of a trust coordination framework 1910. This framework 1910 provides a mechanism for processing of heterogeneous multi-party metadata that includes collection, analysis (reasoning) and dynamic reassessment of policies over metadata. The framework 1910 simplifies evaluation of relevant metadata and policy management, and makes or assists decisions that influence workload orchestration. The framework 1910 relieves reliant services (e.g., dependent services) from the complexity of handling metadata and policies (in terms of quantity, size, and heterogeneity), such as by SGX attestations, TPM attestations (like in Keylime), software supply chain assertions, data provenance assertions, etc. The framework 1910 also consolidates metadata and policy handling in a single instance for multiple relying services. The framework 1910 also minimizes code maintenance of relying services to update metadata formats and policies.
Compared to a singular attestation approach, the following trust coordination implementation will enable specific requirements of entities such as devices, workloads, or data instances to support assertion policies. For example, the trust coordination implementation can introduce new methods, protocols and interfaces for registration, and the exchange and query of assertions and for registration and evaluation of policies (e.g., APIs available to the edge and cloud customers to specify or select an attestation policy when they want to run a sensitive workload in the cloud that requires protection from software and hardware exploits).
The TCF 2102 simplifies metadata and policy management by relieving “relying” services (e.g., dependent services) from the complexity of handling metadata and policies—in terms of quantity, size, and heterogeneity. For instance, the relying services may include Intel® SGX, Intel® TDX, DICE or TPM attestations (or attestations from a similar secure environment), software supply chain assertions, data provenance assertions, etc. Use of the TCF 2102 also consolidates metadata and policy handling in a single instance for multiple relying services and avoids code maintenance of relying services to update metadata formats and policies.
The high-level operations are used to assess the trustworthiness of platforms, software, or workloads. First, the necessary Root-of-Trusts (RoTs) are defined at operation 2612. The RoTs will convey (or will certify other parties who will convey) software and/or hardware features and properties. Second, assertions are collected at operation 2614. Finally, a policy is evaluated at operation 2616 to determine a decision. As the context changes (e.g., new assertions are collected, or previous assertions are updated), policies are (or can be) re-evaluated at operation 2620 to confirm previous decisions, or to trigger contingency actions. In further examples, open-source solutions such as Open Policy Agent (OPA) or another policy engine can be used for policy specification and evaluation.
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Many attestation services are based on a centralized deployment model where workload nodes request attestation following a passport model. The passport is presented to WL orchestrators who vet the node and schedule the WL as appropriate. In elastic compute scenarios, however, WL nodes are not static. Portions of a WL may be dynamically partitioned where WL partitions are also dynamically provisioned to a microservices hosting node. For instance, consider a scenario where a respective microservice (also referred to as a “μService”) obtains a passport (token) from an attestation verification service, which results in O(n log n) to O(n{circumflex over ( )}2) latency overhead; here, n is the number of microservice partitions as a microservice cross-checks the other n microservices. This does not scale well for C2E infrastructures.
Other approaches rely on a cloud service abstraction that centralizes attestation processing as a service abstraction. The cloud services provider may scale the service by adding resources to the server backplane. This approach does not address edge network environments that typically have compute dispersed at different locations with differing compute, connectivity, resource, and latency properties. As noted above, centralized attestation services, e.g., hosted in a cloud, also introduce latency in an edge network.
The following system and methods integrate an attestation mesh into an edge mesh that optimizes edge WLs. In an example, this optimization is provided by co-locating FaaS functions on hardware such as IPUs that are also hosting a distributed WL. The attestation WL is then decomposed into FaaS functions for easy co-location with the other WL/FaaS mesh execution locations. The various stages of attestation processing may rely on attestation inputs, e.g., evidence, reference values, endorsed values, policies, which are supplied by support services but may be cached locally by the hardware (e.g., IPUs) of a mesh node.
This approach allows attestation appraisals to occur within the latency envelope that is appropriate for the edge workload node. More frequent or timely attestation appraisals can be performed because the mesh node with attestation support can cache the various appraisal inputs and manage the cache along with the other mesh-aware WL caching policy.
The ASM 3100 may be hosted on the same infrastructure nodes used to host (e.g., execute) decentralized elastic WLs. The elastic and ASM WLs are treated as different tenants utilizing hardware and software isolation techniques including OS process isolation, containers, micro-kernels, virtual memory, processor operational modes, virtualization, or physical and virtual segmentation, such as with use of technologies such as Intel® SGX, Intel® TDX, AMD® Secure Encrypted Virtualization (SEV), ARM® TrustZone and so forth. The ASM microservices (AS0, AS1, . . . , ASn, shown with instances 3110, 3111, 3112) are provisioned with cryptographic identities, credentials, and policies that establish them as a trusted group within the ASM 3100. The microservices maintain cross-connections that may be utilized to quickly offload attestation processing functions and to share (cache) various attestation related data including endorsement manifests, reference value manifests, device certificates, device sessions (e.g., SPDM, RA-TLS, Attested TLS, or HTTP-Attest), device decentralized identifiers (DID), tokens (e.g., OAuth2, OpenID-Connect, IETF EAT, IETF JWT, or IETF CWT), D-WL policies such as SLAs, SLOs, trust anchors, security policy, security settings, intermediate attestation results, final attestation results (AR), audit information, telemetry information and any other data that may be generated as a result of operating a robust ASM.
The WLM may interface with a microservice mesh 3214 (μSM) that implements and deploys popular FaaS functions commonly used across multiple WLs. The μSM may be implemented on the same infrastructure nodes as other mesh capabilities including WLM, ASM, and so forth. The μSM FaaS functions are isolated using tenant isolation technology as described elsewhere in this document.
The μSM may interface with an attestation service mesh 3216, which may perform a variety of functions for attestation services, as discussed above.
Infrastructure nodes may be hosted by an infrastructure processor mesh 3218 (IPUM) that maintains pools for compute resources configured to host mesh-based workloads (as described above) that are optimized for efficient edge operation. For example, a pool of IPUs may be integrated on a common backplane, rack, cluster, geo-location network, etc., having memory, storage, acceleration, and network connectivity resources readily available for pool IPU consumption. IPU pools may be connected for efficient resource sharing, load balancing, and resiliency, including for respective resource slices 3220.
An attestation result in the form of an attestation token (shown in operation 2) may utilize industry standard token structures such as CBOR Web Token, JSON Web Token, Concise Evidence, W3C DID/VC, X.509 certificates, Trusted Platform Module etc. or proprietary formats such as Intel® SGX attestation block, etc. The attestation token may be returned to the IPU 3320 upon successful verification and appraisal (e.g., as outlined in an IETF RATS Architecture (RFC9334), which provides an example approach of attestation appraisal). The token may be presented to another mesh layer node such as a WL FaaS μService 3318 in response to a request to obtain IPU resources or in response to other events that bind WL hosting resources. The token (e.g., token-1, shown in operation 3) may contain the AS issue attestation result, a token validity period, policies for proper use, and additional security related attributes including authentication credentials, authorization policies, an audit policy, access control rules and so forth. The token may be evaluated by the p Service 3318 to facilitate binding the μService layer resources to the IPU layer resources. It may also be supplied with an attestation payload to an AS microservice 3312 (
A WLM may elastically increase/decrease the number of nodes in the distributed workload resulting in inaccurate attestation tokens. However, fully (exhaustively) re-attesting every D-WL and all the layers to the IPU/root-of-trust may not be required. Only the configurations affected by D-WL manipulation need to be re-issued.
For instance, in
If the mesh deployments are static (do not contain changes that trigger reissuance of any sub-token) then the ‘Pool Token 1,2,3’ may be reused by any node in the pool/mesh to satisfy attestation checking requirements by checking a single signature and singleton token properties.
The pool-token for attestation (and other token forms) described herein may contain additional attributes for Authentication, Authorization, Audit, or Access control (in addition to Attestation). The token may be referred as an AAAA or “A4” token or “A5” token respectively. As such, the A4/A5 token may be supplied with a variety of network interfaces in Edge, Cloud, DApp deployments having minimal impact to API/interface design. The token may be passed directly over the interface or a reference to the token given where the receiver may obtain the token out-of-band. The token may be realized using existing standard token structures including W3C DID, CWT, JWT, XML-DSIG, CMS, and others.
Security is complex and complicated in a Cloud Native or in a Cloud-to-Edge Elastic Workload context, where containerized workloads are launched in clusters that reside in the cloud-to-edge continuum. Clusters may have varied hardware (e.g., multi-vendor, multi-generation components from a single or multiple vendors), different cluster orchestrators (e.g., different flavors of Kubernetes provided by Red Hat®, SUSE™, Ubuntu Canonical™, etc.), different operating systems, and different kernels further down the Cloud Native stack—all of which may be updated or patched at different intervals. In this context, security tasks, oversight, and securing workloads amidst the diversity of hardware and software become a significant concern.
Security measures are complicated by static aspects and dynamic aspects of workload execution. Even when a system is following best practices, there may be a number of dynamic aspects of security risks that only arise during time of the execution. For example, the workload itself might have dependencies, so the security risk might only become apparent when the dependencies are invoked during execution. There may also be a concern of a security risk in the workload itself or an action involved with the workload, including whether the resource or action involved in the workload has been previously exploited, or is subject to a vulnerability.
Even if only a single node is used for workload execution, a security concern might involve whether there are other workloads executing on the same node that are vulnerable or have a dependency that is exploitable. This type of evaluation becomes more difficult in cases where a single workload might have one set of vulnerabilities and other workloads might have a different set of vulnerabilities, and yet heterogeneous edge computing environments may want to handle these workloads concurrently.
The following discusses aspects of implementable security intents that are actuated in Cloud Native and elastic workload contexts. The following approaches seek to let developers develop workloads that operators can deploy anywhere, with an accompanying security intent. The use and adaptation to security intent definitions can help eliminate the need for developers or implementers to plan for all permutations of hardware, software, and infrastructure capabilities. A security intent as discussed herein is a definition of security features or requirements, which includes some set of multiple expressed primitives related to security aspects such as confidentiality, integrity, isolation, auditability, etc. Security intents can be customized to a given containerized workload based on the workload's application context needs, for instance, to enable the containerized workload to be rendered with more security or less security. Accordingly, the security primitives allow control of the defined aspects to ensure that properties such as confidentiality, integrity, auditability can be successfully met, and that appropriate remedial measures can enhance security functionality when needed.
The following thus introduces the definition and use of security intent primitives to ease the task of development and security oversight. These primitives can help quantify a security risk to other workloads running on shared infrastructure, which could prompt the migration of a high-value workload to run in a legacy virtual machine or possibly even in a confidential virtual machine. The following also extends an Intent-Driven-Orchestration framework to launch workloads to meet security needs and monitor them over a lifecycle per configured security policies (while also meeting Key Performance Indicators (KPIs) such as cost and performance). Consequently, this reduces security risk to individual workloads and other workloads running on the shared infrastructure. By quantifying security risk and the cost-benefit analysis of different approaches, security can be handled by a general intent-driven framework that can weigh different axes of data values such as cost, performance, and the like. Accordingly, this approach with security intents extends the concepts provided by intent-driven orchestration, with such concepts extended to handle security.
The execution of the best plan 3620 is used to implement various aspects of the workload processing based on the high-level intents 3604. Periodic monitoring is used to detect deviations in the desired state, which triggers re-planning and corrective actions. Thus, with the basic framework of intent-driven orchestration, some level of security risk monitoring 3630 may be used. However, the security risk monitoring 3630 may simply lead to the selection of another execution plan 3610.
The use of high-level intents 3604 in these and similar settings may be extended to introduce security details from security-specific intents and primitives. No longer will an end user need to specify all characteristics of security in the plan details, which requires in-depth knowledge like the vendor hardware platform or container runtime. Rather, the requirements of the security setting itself can be pre-defined and evaluated dynamically, depending on the type of deployment and setting.
In some examples, even customers and end-users can provide and update a workload specification that provide aspects of security intents. For the execution of cloud-native workloads, this may include modifying a Helm chart, Docker-compose file, or Docker file. Alternately in no-code/low-code environments, this may involve the selection of a user interface option to specify high-level application security intents. In addition to these specifications, components can be provided in the cloud infrastructure (e.g., running as services) to comprehend input, weigh alternatives to realize the requested capabilities, and transform the request specification based on the high-level intents. For instance, these components can be visible when listing the control-related containers/services running in the system.
The static phase operations 3680 and dynamic phase operations 3640 generally correspond to static aspects and dynamic aspects of assessing and implementing security measures. Static aspects of security risks may include evaluating the workload or the system for Common Vulnerabilities and Exposures (CVEs, e.g., publicly disclosed computer security flaws), evaluating the particular configuration of a container used with the workload (e.g., capabilities, signature validation), and the application and verification of security policies on a node or cluster-based level. As an example, dynamic aspects of security risks include those risks noted above, with the additional consideration of security conditions encountered at runtime of the specific workload. Such conditions at runtime may be caused by conditions such as: the workload itself may have dependencies; specific vulnerabilities may have different levels of risk or exploitation; different types of vulnerabilities may exist; workload dependencies may be vulnerable, or the workload dependency might be exploitable; other workloads might attack a particular workload; or a workload might have its own vulnerabilities and other workloads might have their own vulnerabilities.
In an example, the security intents 3606 are provided within incoming requests associated with the workload (discussed in more detail in
In the static phase operations 3680, security hygiene procedures 3650 may be performed before launching a cluster-aware deployment 3660 of a workload. Here, security hygiene procedures 3650 may be performed out of the execution loop as an initial first phase, or as part of a static phase (e.g., when any new workload arrives) for handling security intents. The security hygiene procedures 3650 may include performing CVE scanning/mitigation, system re-configuration, container validation and evaluation, and other security configuration and validation practices. The security hygiene procedures 3650 may be implemented with additional software security tools (not shown), including tools that scan against a software bill of materials, which check for execution with root privileges, or which lock down some process or workload. The security hygiene procedures 3650 may also interact with policies and utilize container orchestration tools such as Kyverno™ and the Open Policy Agent (OPA) Gatekeeper.
The static phase operations 3680 thus may implement one or more aspects of CVE scanning/mitigation or container configuration, directly based on the security intents 3606. Additionally, an orchestrator can determine and consider the high-level intents specified for the workload in relation to a particular orchestrated node or cluster. Based on the intent specifications, scheduling of the workload will fail if hardware or software capabilities are lacking in the cluster. For example, if the user has specified high confidentiality (e.g., an intent of “confidentiality: high” in the intent definition), this intent can translate into launching the workload in a hardware-enabled trusted execution environment or confidential computing platform. In further examples, if the workload is unable to be scheduled in a particular cluster, the workload may be launched in another cluster—or the current cluster may be enhanced with nodes with additional capability for the security requirements.
The use of security intents thus provides an additional specification of execution features to ensure appropriate security actions are performed. Within the node, the high-level security intent is translated into low-level security actions, to address the whole set of high-level requirements. Thus, in contrast to the use of individual security tools, the security intents 3606 can be used to invoke a series of tools and actions—on demand—that operationalize and apply an overall security policy for workload execution. This can help ensure that a specified security model will correctly apply to the specific workload execution.
The dynamic phase operations 3640 involves monitoring workloads for any new vulnerabilities and taking action (such as when triggered by a new CVE detection), with the security risk monitoring 3630. Potential actions that may be performed in the dynamic phase operations 3640 may include: cordoning off (e.g., isolating, removing) a node, migrating workloads, or patching a node. Actions taken on the workload may include stopping the workload, auto-updating the workload, or migrating the workload to another node or cluster for isolation or confidentiality. The dynamic phase may also include the exploration of plans 3610, and execution of a base plan (the best plan 3620), as discussed with reference to
The use of security intents may allow the specification of multiple aspects for any specific security property, goal, or objective. In the context of intent-driven orchestration for cloud-native workloads, there may be multiple definitions of intents for more than one aspect of latency (such as median and tail); similarly, there may be multiple dimensions of intents for more than one aspect of security or some security intent property (such as confidentiality, integrity, isolation, etc.). Other aspects of cost, performance, and power can also be weighed and evaluated in connection with the intents.
As depicted in
In an example, static customer-mandated security intents are to be satisfied at launch time. Modifiers of the security intents (e.g., defined values such as high, medium, low, numeric values in a range, etc.) guide the implementation that is desired, with the choice being one that meets or exceeds the stated requirements. For example, logging provides auditability but remote logging provides even greater security—because if the original workload is compromised, it is harder to tamper with remote logs and cripple forensic analysis. Thus, a security intent may specify not only that logging will occur, but what type of logging, and what amount of logging.
The security intents primitives can be extended as needed. The purpose in having primitives is to mix and match according to the needs of the workload application. For instance, particular use cases of workloads involving Health Care, Finance, Human Resources, and Machine Learning applications may specify multiple types of security intent primitives. In other situations, for example during development and/or testing, a lesser level of security may suffice, and thus fewer security intent primitives may be specified.
Thus, a significant technical advantage of high-level security intents is that the intents serve as a shorthand “template” for security recipes, while being agnostic about the platform details, software services, and the software stack in place. The set of primitives supported in the security intents can be expressed or made available to users via an API, documentation. In some no-code/low-code development environments, the set of primitives in the security intents can be made available in drop-down menus or as annotations and decorators in a user interface. In some examples, the output of such tools may be a Helm Chart or a Docker compose file with markings (e.g., as illustrated in connection with the Docker compose file format 3710 in
When security intents are provided with an incoming request, such requests can provide a specific designation associated with security attributes such as “confidentiality” and “auditability”. These designations can include high level tags for each attribute, and specific attribute values or value ranges. Some examples of attributes provided in the security intents are shown as follows with reference to
Confidentiality 3711 (e.g., a required level or type of confidentiality needed for the workload and the workload data); Integrity 3712 (e.g., a required level or type of integrity checks or verification to be performed on the workload or on software (including dependencies) used to execute the workload); and Isolation 3713 (e.g., a required level or type of isolation needed for execution of the workload). In an example, security isolation may be implemented with a container runtime such as Confidential Containers to create a confidential VM on-the-fly using confidential computing technologies. The use a confidential VM also can provide integrity and isolation. However, if confidentiality is not requested but just isolation, a workload could be launched in a classic VM/or lightweight VM (e.g., kata-container) to provide address space isolation.
Auditability 3714 (e.g., a required type or result of auditable data in connection with the workload and the workload data). This may include use of a Service Mesh that provides a sidecar or other component (for example, an Ambient Service Mesh) that provides logging capabilities. With a “high” value specified for auditability, the logging can be remotely provided, such as on a backing datastore with replication. With a “low” value specified for auditability, the logging can be provided to a local disk.
Infrastructure Risk 3715 (e.g., a sensitivity of infrastructure use or interaction with the workload and the workload data). This may include assessing and reducing a node-level risk from respective OS/hypervisor CVEs. This may also include placement of a node into “maintenance mode” to drain it of its current workloads and migrating them to a non-vulnerable node (most likely patched earlier), patching the current node before pressing it back into service. A CVE such as a Linux kernel vulnerability might invoke this sequence of actions.
Other types of security intents may be related to other aspects of: Auditability; Confidentiality; Integrity; Isolation; Immutability; Administrative Access; Networking Capabilities (firewall); and the like.
TABLE 1, below, defines an example of multiple security intents and a pod specification with the security intents, for an example deployment in Kubernetes.
The handling of security intents may involve the use of static and dynamic aspects, as discussed with reference to
In addition to dynamic changes or adjustment of a platform, other aspects of handling security intents may include disabling access or capabilities. For example, a lock down of system access may be implemented using a runtime that prevents any execution access via admin credentials into a container. In other examples, alerting may be used to handle security intents. This may include alerting an end-user about a workload's vulnerabilities and potentially launching it in a lightweight VM such as a Kata-container (a classic VM wrapped container to obtain address space isolation and sandboxing). Other types of alerts might be used to notify a workload owner if a reported CVE has a patch to recommend updating and re-launching; whereas a workload with a high severity CVE might notify a workload owner before stopping execution. In some examples, alerting might be accompanied by no action taken (such as when a CVE infested workload does not affect other workloads running on the same node, if all of the other workloads are running in TEEs).
A further example of adaptation in response to security intents may involve supporting key creation and protection approaches in a TEE to secure communications. Running a workload in a Trusted Execution Environment (TEE) may protect the workload from other workloads sharing the infrastructure, but the workload still needs network security to be protected from denial of service attacks, and attestation to ensure that the correct workload state is running as expected. However, even a TEE-protected workload can be malicious and launch attacks on other workloads, so even a TEE-protected workload needs to plug vulnerabilities and have appropriate network security measures in place.
In further examples, the use of security intents may be coordinated with Security Risk Scoring. Scoring may be initiated by ensuring basic levels of security hygiene, starting at an initial security risk score of 0. In this context, security hygiene refers to the security best practices (e.g., recommendations defined by NIST, CIS Bench and others) that address vulnerabilities in the workloads based on their software bill of materials, launching containers with the least privileges necessary, not running a workload as root among other examples. Occasionally a workload needs certain elevated privileges in which case a high base risk score can be assigned to the same. The system may adjust the security risk score if all high-level security intents for a workload cannot be honored. Per configured security policy a workload risk score may render it unfit to launch or will be launched with warnings. The system may also assign a security risk score stemming from an individual workload to other workloads sharing the infrastructure. The goal is to capture the blast radius of a vulnerability. Some vulnerabilities may affect workloads running on the same node while others may hurt the whole cluster and beyond if the resources can be used to launch a denial of service attack across the internet.
The attack potential of an individual vulnerability in a workload or one of its dependencies may be considered in some examples, although the examples above are focused on a node level security risk. If a workload is the sole occupant of a node, the risk might be tolerable but as more workloads share the node, the risk rises O(N) where N are the number of workloads with regulated industry workloads weighting the risk contribution higher. Accordingly, a node level security risk score may be provided from a sum of the contributions of each of the individual workload vulnerabilities.
In further examples, monitoring is performed with the use of a monitoring component. The monitoring component periodically checks to determine if any new vulnerabilities have been reported that affects the running workloads. In the event of a new CVE, the security risk score is recomputed for the host node (sum-CVE-severity-core * number of co-resident workloads) and for the individual affected workloads.
One or more actions as mentioned above are evaluated for best strategy per configured security policies. The general rule of thumb is when more workloads are sharing the infrastructure it is more important to address the vulnerabilities via patching when fixes are available or by using other mitigation mechanisms (such as firewall rules, eBPF (extended Berkeley Packet Filter) security tools, or migrating a high value workload to another node or into a confidential virtual machine) to reduce security risk. A node level software CVE can be addressed with a maintenance mode to the node as discussed above.
The use of security intents may be combined with the use of a variety of other security tools and frameworks. As will be understood, a variety of security tools can be used to scan for Software bill of materials, to inspect for the execution of software root privileges, or to implement lock-down measures for security. These tools, by themselves, often only address one limited aspect of security, and do not make it easy to address other concerns (such as to guarantee confidentiality). The use of security intents with these tools, in contrast, provides an improved way to operationalize security and apply a policy to make the correct tool address the correct security concern.
As an example, security intents may be used with a built-in container Pod Security admission controller, such as that provided by Kubernetes. This controller can validate created Pods against one of three security policies and admits, warns, or rejects the creation.
As an example, security intents may be used with Grype™, Trivy™ and other vulnerability scanning tools that scan a Software Bill of Materials to determine if there exist any known vulnerabilities against them and how severe they are, and whether there exist patches for the same.
As another example, security intents may be used with Kubescape™ This tool scans Kubernetes resources for risks, assigns a risk score (0 to 100) to each examined risk, and suggests remediations (configuration changes) to address risk.
As another example, security intents may be used with Open Policy Agent (OPA) Gatekeeper. Gatekeeper uses an OPA constraint framework to describe and enforce policies. Gatekeeper also supports mutation with mutation Custom Resource Descriptors.
As another example, security intents may be used with Kyverno™ Like OPA Gatekeeper, Kyverno can validate and mutate configurations using admission controls and background scans. It can also generate configuration data.
As another example, security intents may be used with intent-based network security implemented in hardware components. One example is the use of intent-based network security in Cisco® networking hardware, although other networking implementations are also possible.
The use of security intents provides enhancements over the basic use of these tools. While some Kubernetes tools (Kubescape™, OPA Gatekeeper™, Kyverno™ and others) listed above check for CVEs, configuration errors, and access policies, they do not quantify the risk to other workloads running on shared infrastructure, nor do they address heterogeneity in hardware across clusters cloud to edge. Likewise, while intent-based network security approaches can improve the use of network security through networking hardware improvements, such processing is complementary to protect the actual workloads and infrastructure, the ingress/egress point of network packets.
The use of security intents as discussed above may be used in combination with aspects of confidential computing platforms (e.g., including but not limited to Intel® Trust Domain Extensions (TDX), Intel® Software Guard Extensions (SGX), ARM® TrustZone, AMD® Secure Encrypted Virtualization (SEV)). This enables running even regulated industry workloads on shared infrastructure to realize cost savings, lower latency, and higher availability.
Accordingly, these approaches with security intents provide a way for assessing and handling a methodical security risk-based addressing of high-level security intents. The use of security intents can free the end user of needing deep hardware and software knowledge to protect workloads, while enabling flexible deployments and improved system operations. Other technical advantages and benefits will also be apparent, including in combination with the following trust coordination operations.
Security intents (and intents in general), allow operators to provide requirements for a workload or deployment environment. Operational or policy intents may be used to define operational elements in a deployment environment. The intents are declarative and may be used in a multi-layer hierarchical organization with independent intents having dependent intents or dimensions. For instance, an independent parent policy intent may have a dependent security intent child.
Intents may have one or more properties. For example, a security intent may have properties, such as confidentiality, integrity, isolation, or auditability. Each property may reference one or more dimensions. A dimension is a value that is calculated, observed, measured, or otherwise derived. A dimension is a variable that can be used in an intent either statically or dynamically. The value of the dimension is returned from a core component, which is specific to the dimension. The core component may exist in as an extension or agent of an orchestrator. The core component may interface with monitoring agents or other components to obtain telemetry data, operational data, status data, or the like from workload nodes. The core component may provide an output based on different measurements, user inputs, policies, or calculations, where the output is used as the value of the dimension or used to calculate the value of the dimension. In other examples, the dimension is specified by a policy intent or by other means. Dimensions may be used in any intent declaration, including a security intent, an attestation intent, a policy intent, a trust intent, or the like.
The present systems and methods implement tiered intent declarations for workloads. In an example, a policy intent may be used to define dimensions for one or more security intents, where the security intents import and use the dimensions in the policy intent. In this way, the policy intent may be used to control higher-level policies (e.g., for a customer) and security intents may be used to handle workload or workload fragment security. Further details are provided in the examples and figures described below.
The security intent 3806 may be used to manage one or more workload 3804 and resource 3802 bindings or executions. The policy intent 3808 may be used to manage one or more security intents 3806 and their corresponding workloads 3804 and resources 3802.
TABLE 2, below, illustrates a multi-tiered intent schema. TABLE 2 includes an example of a policy intent labeled “premium-subscriber-agreement”, a policy intent labeled “prioritize-bandwidth”, a security intent labeled “secure-workload”, and a pod specification with the security intent, for an example deployment in Kubernetes.
The dimensions include “bandwidth_constraint”, “billed_cost”, and “base_cost”. These are used as variables in different intent property declarations. Note that in this example, the “billed_cost” dimension is a dependent dimension calculated based on the dimensions “base_cost” and “bandwidth_constraint”. The “base_cost” dimension is defined in the policy intent as the literal value of “1.00”. The “billed_cost” dimension is defined as a function of the “base_cost” dimension and the “bandwidth_constraint” dimension. Here, the “bandwidth_constraint” dimension is not specified. As such, the value of the “bandwidth_constraint” dimension is obtained from a core component. As discussed above, the core component may provide dimension values, which can be used by one or more intent declarations.
Other examples of dimensions include a “confidential” property of a security intent that has the dimensions of “true” and “false”, which are used to specify whether the core component succeeded in its part to guarantee that a workload is confidential. As another example, an “IntegrityCost” property of a security intent may have a float dimension to specify what is the cost calculated to maintain the integrity of the workload as a value from 0.00 to 1.00. A “BandwidthConstraint” property may have a float dimension to specify a limit imposed to the maximum bandwidth due to the respective core component being applied successfully.
Using the multi-tiered schema, security intents (or other intents) are able incorporate a previously created policy intent, which is expected to have already been created by an administrator or operator, an automated tool based on what the user has signed up for, or a user of a workload, for example, so that the cores components have enough information to calculate solutions and generate dimensions, which can further inform the other core components or be used by policy intents for calculations. As the outputs from core components are processed, the dependency graph is resolved and the variables converge to their final values.
A policy intent may include available dimensions, with static or dynamic parameters associated with each (e.g., see example for billing above), and a list of properties and what kinds of intents or intent parameters they are allowed to process. Core components may provide entirely new dimensions that can be measured, constrained, and compared; thus, modifying the scope of a policy. A policy intent may also provide a policy to resolve ties.
Using this schema, implementations of security intent definitions, policy intent definitions, and other intent definitions may be used to define the data to make it possible to calculate a final solution of dimensions without ambiguity.
For example, a core component focused on computational orchestration (e.g., Intel QuickAssist Technology (QAT)) can provide a dimension to specify different parameters regarding the rate of crypto operations. Then, a policy intent may be created with that dimension. For example, by enabling a QAT Core Component in the cores: section, a type: crypto_rate in the dimensions: section becomes a valid entry. Furthermore, that dimension may then be readable by every core component defined for the policy intent, which can then be used to help decide the final solution.
At 3904, the method 3900 includes identifying intents that define requirements for the execution of the workload on the plurality of compute nodes.
At 3906, the method 3900 includes monitoring the execution of the workload to produce monitoring data.
At 3908, the method 3900 includes controlling the execution of the workload based on the intents and the monitoring data, to dynamically adapt to changed conditions during the execution of the workload.
In an embodiment, the method 3900 includes identifying common vulnerabilities and exposures (CVEs) related to the workload or the plurality of compute nodes. In such an embodiment, controlling the execution of the workload includes performing a remedial action in response to the identified CVEs.
In a further embodiment, controlling the execution of the workload includes determining, based on the intents, whether to initiate an update to a component of one of the plurality of compute nodes or the workload. Then, initiating a workload migration process to migrate the workload from a first execution environment to a second execution environment, and apply the update to the component.
In an embodiment, the first execution environment is a first compute node and the second execution environment is a second compute node. In another embodiment, the first execution environment is a first virtual execution environment on a compute node and the second execution environment is a second virtual execution environment on the compute node.
In an embodiment, the method 3900 includes initiating an attestation of the component after the update is complete and initiating a trust binding between the plurality of compute nodes and the workload after the attestation of the component is complete. In a further embodiment, the update includes at least one of: updated workload code for the workload, configuration parameters for the workload, quality-of-service policies for the workload, key performance indicators for the workload, or data source or data sink parameters for the workload. In another embodiment, the update includes at least one of: updated firmware for at least one of the plurality of compute nodes, configuration parameters for at least one of the plurality of compute nodes, or calibration parameters for at least one of the plurality of compute nodes.
In an embodiment, the first execution environment is a first compute node and the second execution environment is a second compute node. In another embodiment, the first execution environment is a first virtual execution environment on a compute node and the second execution environment is a second virtual execution environment on the compute node.
In an embodiment, the intents define policy intents through one or more properties and dimensions for use in a security intent. In a further embodiment, a dimension includes a name and a value. In a further embodiment, the value is defined in the intents. In another embodiment, the value is provided by a core component.
In an embodiment, the security intent includes at least one property of: confidentiality, integrity, isolation, audibility, or infrastructure. In another embodiment, the security intent includes a reference to the intents that define policy intents. In a further embodiment, the intents and the security intent are stored in separate files.
Additional examples of the presently described method, system, and device embodiments include the following, non-limiting implementations. Each of the following non-limiting examples may stand on its own or may be combined in any permutation or combination with any one or more of the other examples provided below or throughout the present disclosure.
Example 1 is a data center system, comprising: a plurality of compute nodes; and an orchestration node configured by instructions stored on non-transitory machine-readable, to: identify a workload for execution on the plurality of compute nodes; identify intents that define requirements for the execution of the workload on the plurality of compute nodes; monitor the execution of the workload to produce monitoring data; and control the execution of the workload based on the intents and the monitoring data, to dynamically adapt to changed conditions during the execution of the workload.
In Example 2, the subject matter of Example 1 includes, wherein the orchestration node is configured to identify common vulnerabilities and exposures (CVEs) related to the workload or the plurality of compute nodes, and wherein to control the execution of the workload, the orchestration node is to perform a remedial action in response to the identified CVEs.
In Example 3, the subject matter of Example 2 includes, wherein to control the execution of the workload, the orchestration node is to: determine, based on the intents, whether to initiate an update to a component of one of the plurality of compute nodes or the workload; and initiate a workload migration process to migrate the workload from a first execution environment to a second execution environment, and apply the update to the component.
In Example 4, the subject matter of Example 3 includes, wherein the orchestration node is to: initiate an attestation of the component after the update is complete; and initiate a trust binding between the plurality of compute nodes and the workload after the attestation of the component is complete.
In Example 5, the subject matter of Example 4 includes, wherein the update includes at least one of: updated workload code for the workload, configuration parameters for the workload, quality-of-service policies for the workload, key performance indicators for the workload, or data source or data sink parameters for the workload.
In Example 6, the subject matter of Examples 4-5 includes, wherein the update includes at least one of: updated firmware for at least one of the plurality of compute nodes, configuration parameters for at least one of the plurality of compute nodes, or calibration parameters for at least one of the plurality of compute nodes.
In Example 7, the subject matter of Examples 3-6 includes, wherein the first execution environment is a first compute node and the second execution environment is a second compute node.
In Example 8, the subject matter of Examples 3-7 includes, wherein the first execution environment is a first virtual execution environment on a compute node and the second execution environment is a second virtual execution environment on the compute node.
In Example 9, the subject matter of Examples 1-8 includes, wherein the intents define policy intents through one or more properties and dimensions for use in a security intent.
In Example 10, the subject matter of Example 9 includes, wherein a dimension includes a name and a value.
In Example 11, the subject matter of Example 10 includes, wherein the value is defined in the intents.
In Example 12, the subject matter of Examples 10-11 includes, wherein the value is provided by a core component.
In Example 13, the subject matter of Examples 9-12 includes, wherein the security intent includes at least one property of: confidentiality, integrity, isolation, audibility, or infrastructure.
In Example 14, the subject matter of Examples 9-13 includes, wherein the security intent includes a reference to the intents that define policy intents.
In Example 15, the subject matter of Example 14 includes, wherein the intents and the security intent are stored in separate files.
Example 16 is a method performed by an orchestration node to orchestrate workloads on a plurality of compute nodes, comprising: identifying a workload for execution on the plurality of compute nodes; identifying intents that define requirements for the execution of the workload on the plurality of compute nodes; monitoring the execution of the workload to produce monitoring data; and controlling the execution of the workload based on the intents and the monitoring data, to dynamically adapt to changed conditions during the execution of the workload.
In Example 17, the subject matter of Example 16 includes, identifying common vulnerabilities and exposures (CVEs) related to the workload or the plurality of compute nodes, and wherein controlling the execution of the workload comprises performing a remedial action in response to the identified CVEs.
In Example 18, the subject matter of Example 17 includes, wherein controlling the execution of the workload comprises: determining, based on the intents, whether to initiate an update to a component of one of the plurality of compute nodes or the workload; and initiating a workload migration process to migrate the workload from a first execution environment to a second execution environment, and apply the update to the component.
In Example 19, the subject matter of Example 18 includes, initiating an attestation of the component after the update is complete; and initiating a trust binding between the plurality of compute nodes and the workload after the attestation of the component is complete.
In Example 20, the subject matter of Example 19 includes, wherein the update includes at least one of: updated workload code for the workload, configuration parameters for the workload, quality-of-service policies for the workload, key performance indicators for the workload, or data source or data sink parameters for the workload.
In Example 21, the subject matter of Examples 19-20 includes, wherein the update includes at least one of: updated firmware for at least one of the plurality of compute nodes, configuration parameters for at least one of the plurality of compute nodes, or calibration parameters for at least one of the plurality of compute nodes.
In Example 22, the subject matter of Examples 18-21 includes, wherein the first execution environment is a first compute node and the second execution environment is a second compute node.
In Example 23, the subject matter of Examples 18-22 includes, wherein the first execution environment is a first virtual execution environment on a compute node and the second execution environment is a second virtual execution environment on the compute node.
In Example 24, the subject matter of Examples 16-23 includes, wherein the intents define policy intents through one or more properties and dimensions for use in a security intent.
In Example 25, the subject matter of Example 24 includes, wherein a dimension includes a name and a value.
In Example 26, the subject matter of Example 25 includes, wherein the value is defined in the intents.
In Example 27, the subject matter of Examples 25-26 includes, wherein the value is provided by a core component.
In Example 28, the subject matter of Examples 24-27 includes, wherein the security intent includes at least one property of: confidentiality, integrity, isolation, audibility, or infrastructure.
In Example 29, the subject matter of Examples 24-28 includes, wherein the security intent includes a reference to the intents that define policy intents.
In Example 30, the subject matter of Example 29 includes, wherein the intents and the security intent are stored in separate files.
Example 31 is a machine-readable medium including instructions, which when executed by an orchestration node in a network, cause the orchestration node to: identify a workload for execution on a plurality of compute nodes in the network; identify intents that define requirements for the execution of the workload on the plurality of compute nodes; monitor the execution of the workload to produce monitoring data; and control the execution of the workload based on the intents and the monitoring data, to dynamically adapt to changed conditions during the execution of the workload.
In Example 32, the subject matter of Example 31 includes, wherein instructions cause the orchestration node to identify common vulnerabilities and exposures (CVEs) related to the workload or the plurality of compute nodes, and wherein to control the execution of the workload, the orchestration node is to perform a remedial action in response to the identified CVEs.
In Example 33, the subject matter of Example 32 includes, wherein to control the execution of the workload, the instructions cause the orchestration node to: determine, based on the intents, whether to initiate an update to a component of one of the plurality of compute nodes or the workload; and initiate a workload migration process to migrate the workload from a first execution environment to a second execution environment, and apply the update to the component.
In Example 34, the subject matter of Example 33 includes, wherein the instructions cause the orchestration node to: initiate an attestation of the component after the update is complete; and initiate a trust binding between the plurality of compute nodes and the workload after the attestation of the component is complete.
In Example 35, the subject matter of Example 34 includes, wherein the update includes at least one of: updated workload code for the workload, configuration parameters for the workload, quality-of-service policies for the workload, key performance indicators for the workload, or data source or data sink parameters for the workload.
In Example 36, the subject matter of Examples 34-35 includes, wherein the update includes at least one of: updated firmware for at least one of the plurality of compute nodes, configuration parameters for at least one of the plurality of compute nodes, or calibration parameters for at least one of the plurality of compute nodes.
In Example 37, the subject matter of Examples 33-36 includes, wherein the first execution environment is a first compute node and the second execution environment is a second compute node.
In Example 38, the subject matter of Examples 33-37 includes, wherein the first execution environment is a first virtual execution environment on a compute node and the second execution environment is a second virtual execution environment on the compute node.
In Example 39, the subject matter of Examples 31-38 includes, wherein the intents define policy intents through one or more properties and dimensions for use in a security intent.
In Example 40, the subject matter of Example 39 includes, wherein a dimension includes a name and a value.
In Example 41, the subject matter of Example 40 includes, wherein the value is defined in the intents.
In Example 42, the subject matter of Examples 40-41 includes, wherein the value is provided by a core component.
In Example 43, the subject matter of Examples 39-42 includes, wherein the security intent includes at least one property of: confidentiality, integrity, isolation, audibility, or infrastructure.
In Example 44, the subject matter of Examples 39-43 includes, wherein the security intent includes a reference to the intents that define policy intents.
In Example 45, the subject matter of Example 44 includes, wherein the intents and the security intent are stored in separate files.
Example 46 is at least one machine-readable medium including instructions that, when executed by processing circuitry, cause the processing circuitry to perform operations to implement of any of Examples 1-45.
Example 47 is an apparatus comprising means to implement of any of Examples 1-45.
Example 48 is a system to implement of any of Examples 1-45.
Example 49 is a method to implement of any of Examples 1-45.
Compute, memory, and storage are scarce resources, and generally, decrease depending on the edge location (e.g., fewer processing resources being available at consumer end point devices than at a base station or at a central office). However, the closer that the edge location is to the endpoint (e.g., UEs), the more that space and power are constrained. Thus, edge computing, as a general design principle, attempts to minimize the resources needed for network services, through the distribution of more resources which are located closer both geographically and in-network access time. In this manner, edge computing attempts to bring the compute resources to the workload data where appropriate, or bring the workload data to the compute resources.
The following describes aspects of an edge cloud architecture that covers multiple potential deployments and addresses restrictions that some network operators or service providers may have in their own infrastructures. These include, variation of configurations based on the edge location (because edges at a base station level, for instance, may have more constrained performance and capabilities in a multi-tenant scenario); configurations based on the type of compute, memory, storage, fabric, acceleration, or like resources available to edge locations, tiers of locations, or groups of locations; the service, security, and management and orchestration capabilities; and related objectives to achieve usability and performance of end services. These deployments may accomplish processing in network layers that may be considered as “near edge,” “close edge,” “local edge,” “middle edge,” or “far edge” layers, depending on latency, distance, and timing characteristics.
Edge computing is a developing paradigm where computing is performed at or closer to the “edge” of a network, typically through the use of a compute platform (e.g., x86, AMD or ARM hardware architectures) implemented at base stations, gateways, network routers, or other devices which are much closer to end point devices producing and consuming the data. For example, edge gateway servers may be equipped with pools of memory and storage resources to perform computation in real-time for low latency use-cases (e.g., autonomous driving or video surveillance) for connected client devices. Or as an example, base stations may be augmented with compute and acceleration resources to directly process service workloads for connected user equipment, without further communicating data via backhaul networks. Or as another example, central office network management hardware may be replaced with compute hardware that performs virtualized network functions and offers compute resources for the execution of services and consumer functions for connected devices. Within edge computing networks, there may be scenarios in services in which the compute resource will be “moved” to the data, as well as scenarios in which the data will be “moved” to the compute resource. Or as an example, base station compute, acceleration and network resources can provide services to scale to workload demands on an as-needed basis by activating dormant capacity (subscription, capacity-on-demand) to manage corner cases, emergencies or to provide longevity for deployed resources over a significantly longer implemented lifecycle.
In contrast to the network architecture of
Depending on the real-time requirements in a communications context, a hierarchical structure of data processing and storage nodes may be defined in an edge computing deployment. For example, such a deployment may include local ultra-low-latency processing, regional storage, and processing as well as remote cloud data center-based storage and processing. Key performance indicators (KPIs) may be used to identify where sensor data is appropriately transferred and where it is processed or stored. This typically depends on the ISO layer dependency of the data. For example, lower layer (PHY, MAC, routing, etc.) data typically changes quickly and is better handled locally to meet latency requirements. Higher layer data such as Application-Layer data is typically less time-critical and may be stored and processed in a remote cloud data center.
In the example of
It should be understood that some of the devices in 4110 are multi-tenant devices where Tenant1 may function within a Tenant1 ‘slice’ while a Tenant2 may function within a Tenant2 ‘slice’ (and, in further examples, additional or sub-tenants may exist; and a respective tenant may be specifically entitled and transactionally tied to a specific set of features all the way to specific hardware features). A trusted multi-tenant device may further contain a tenant-specific cryptographic key such that the combination of a key and a slice may be considered a “root of trust” (RoT) or tenant-specific RoT. A ROT may further be computed dynamically composed using a compute security architecture, such as a DICE (Device Identity Composition Engine) architecture where a DICE hardware building block is used to construct layered trusted computing base contexts for secured and authenticated layering of device capabilities (such as with use of a Field Programmable Gate Array (FPGA)). The ROT also may be used for a trusted computing context to support respective tenant operations, etc. Use of this ROT and the compute security architecture may be enhanced by the attestation operations further discussed herein.
Edge computing nodes may partition resources (memory, central processing unit (CPU), graphics processing unit (GPU), interrupt controller, input/output (I/O) controller, memory controller, bus controller, etc.) where respective partitionings may contain a RoT capability and where fan-out and layering according to a DICE model may further be applied to Edge Nodes. Cloud computing nodes consisting of containers, FaaS (function as a service) engines, servlets, servers, or other computation abstraction may be partitioned according to a DICE layering and fan-out structure to support a RoT context for each. Accordingly, the respective RoTs spanning devices in 4110, 4122, and 4140 may coordinate the establishment of a distributed trusted computing base (DTCB) such that a tenant-specific virtual trusted secure channel linking all elements end-to-end can be established.
Further, it will be understood that a container may have data or workload-specific keys protecting its content from a previous edge node. As part of the migration of a container, a pod controller at a source edge node may obtain a migration key from a target edge node pod controller where the migration key is used to wrap the container-specific keys. When the container/pod is migrated to the target edge node, the unwrapping key is exposed to the pod controller that then decrypts the wrapped keys. The keys may now be used to perform operations on container specific data. The migration functions may be gated by properly attested edge nodes and pod managers (as described above).
As an example, the edge computing system may be extended to provide orchestration of multiple applications through the use of containers (a contained, deployable unit of software that provides code and needed dependencies), in a multi-owner, multi-tenant environment. A multi-tenant orchestrator may be used to perform key management, trust anchor management, and other compute security functions related to the provisioning and lifecycle of the trusted ‘slice’ concept in
Accordingly, an edge-computing system may be configured to fulfill requests and responses for various client endpoints from multiple virtual edge instances (and, from a cloud or remote data center, not shown). The use of these virtual edge instances supports multiple tenants and multiple applications (e.g., augmented reality (AR)/virtual reality (VR), enterprise applications, content delivery, gaming, compute offload) simultaneously. Further, there may be multiple types of applications within the virtual edge instances (e.g., normal applications, latency-sensitive applications, latency-critical applications, user plane applications, networking applications, etc.). The virtual edge instances may also be spanned across systems of multiple owners at different geographic locations (or, respective computing systems and resources which are co-owned or co-managed by multiple owners).
For instance, a respective edge node 4122, 4124 may implement the use of containers, such as with the use of a container “pod” 4126, 4128 providing a group of one or more containers. In a setting that uses one or more container pods, a pod controller or orchestrator is responsible for local control and orchestration of the containers in the pod. Various edge node resources (e.g., storage, compute, services, depicted with hexagons) provided for the respective edge slices of virtual edges 4132, 4134 are partitioned according to the needs of a respective container.
With the use of container pods, a pod controller oversees the partitioning and allocation of containers and resources. The pod controller receives instructions from an orchestrator (e.g., performing orchestration functions 4160) that instructs the controller on how to appropriately partition physical resources and for what duration, such as by receiving key performance indicator (KPI) targets based on SLA contracts. The pod controller determines which container uses which resources and for how long to complete the workload and satisfy the SLA. The pod controller also manages container lifecycle operations such as: creating the container, provisioning it with resources and applications, coordinating intermediate results between multiple containers working on a distributed application together, dismantling containers when workload completes, and the like. Additionally, a pod controller may serve a compute security role that prevents the assignment of resources until the right tenant authenticates or prevents provisioning of data or a workload to a container until an attestation result is satisfied.
Also, with the use of container pods, tenant boundaries can still exist but in the context of a respective pod of containers. If a respective tenant-specific pod has a tenant-specific pod controller, there may be a shared pod controller that consolidates resource allocation requests to avoid typical resource starvation situations. Further controls may be provided to ensure the attestation and trustworthiness of the pod and pod controller. For instance, the orchestrator 4160 may provision an attestation verification policy to local pod controllers that perform attestation verification. If an attestation satisfies a policy for a first tenant pod controller but not a second tenant pod controller, then the second pod may be migrated to a different edge node that does satisfy it. Alternatively, the first pod may be allowed to execute and a different shared pod controller is installed and invoked before the second pod executing.
In further examples, edge computing systems may deploy containers in an edge computing system. As a simplified example, a container manager is adapted to launch containerized pods, functions, and functions-as-a-service instances through execution via compute nodes, or to separately execute containerized virtualized network functions through execution via compute nodes. This arrangement may be adapted for use by multiple tenants in system arrangement, where containerized pods, functions, and functions-as-a-service instances are launched within virtual machines specific to an individual tenant (aside from the execution of virtualized network functions).
Within the edge cloud, a first edge node 4122 (e.g., operated by a first owner) and a second edge node 4124 (e.g., operated by a second owner) may operate or respond to a container orchestrator to coordinate the execution of various applications within the virtual edge instances offered for respective tenants. For instance, the edge nodes 4122, 4124 may be coordinated based on edge provisioning functions 4150, while the operation of the various applications is coordinated with orchestration functions 4160.
Various system arrangements may provide an architecture that treats VMs, Containers, and Functions equally in terms of application composition (and resulting applications are combinations of these three ingredients). A respective ingredient may involve the use of one or more accelerator (e.g., FPGA, ASIC, cryptographic execution) components as a local backend. In this manner, applications can be split across multiple edge owners, coordinated by an orchestrator.
It should be appreciated that the edge computing systems and arrangements discussed herein may be applicable in various solutions, services, and/or use cases. As an example,
A respective node of the edge gateway nodes 4220 may communicate with one or more edge resource nodes 4240, which are illustratively embodied as compute servers, appliances or components located at or in a communication base station 4242 (e.g., a base station of a cellular network). As discussed above, a respective edge resource node 4240 includes some processing and storage capabilities, and, as such, some processing and/or storage of data for the client compute nodes 4210 may be performed on the edge resource node 4240. For example, the processing of data that is less urgent or important may be performed by the edge resource node 4240, while the processing of data that is of a higher urgency or importance may be performed by edge gateway devices or the client nodes themselves (depending on, for example, the capabilities of a respective component). Further, various wired or wireless communication links (e.g., fiber optic wired backhaul, 5G wireless links) may exist among the edge nodes 4220, edge resource node(s) 4240, core data center 4250, and network cloud 4260.
The edge resource node(s) 4240 also communicate with the core data center 4250, which may include compute servers, appliances, and/or other components located in a central location (e.g., a central office of a cellular communication network). The core data center 4250 may provide a gateway to the global network cloud 4260 (e.g., the Internet) for the edge cloud 4010 operations formed by the edge resource node(s) 4240 and the edge gateway nodes 4220. Additionally, in some examples, the core data center 4250 may include an amount of processing and storage capabilities and, as such, some processing and/or storage of data for the client compute devices may be performed on the core data center 4250 (e.g., processing of low urgency or importance, or high complexity). The edge gateway nodes 4220 or the edge resource nodes 4240 may offer the use of stateful applications 4232 and a geographically distributed data storage 4234 (e.g., database, data store, etc.).
In further examples,
In further configurations, the edge computing system may implement FaaS computing capabilities through the use of respective executable applications and functions. In an example, a developer writes function code (e.g., “computer code” herein) representing one or more computer functions, and the function code is uploaded to a FaaS platform provided by, for example, an edge node or data center. A trigger such as, for example, a service use case or an edge processing event, initiates the execution of the function code with the FaaS platform.
In an example of FaaS, a container is used to provide an environment in which function code is executed. The container may be any isolated-execution entity (a workload execution environment) such as a process, a Docker or Kubernetes container, a virtual machine, etc. Within the edge computing system, various data center, edge, and endpoint (including mobile) devices are used to “spin up” functions (e.g., activate and/or allocate function actions) that are scaled on demand. The function code gets executed on the physical infrastructure (e.g., edge computing node) device and underlying virtualized containers. Finally, the container is “spun down” (e.g., deactivated and/or deallocated) on the infrastructure in response to the execution being completed.
Further aspects of FaaS may enable deployment of edge functions in a service fashion, including support of respective functions that support edge computing as a service. Additional features of FaaS may include: a granular billing component that enables customers (e.g., computer code developers) to pay only when their code gets executed; common data storage to store data for reuse by one or more functions; orchestration and management among individual functions; function execution management, parallelism, and consolidation; management of container and function memory spaces; coordination of acceleration resources available for functions; and distribution of functions between containers (including “warm” containers, already deployed or operating, versus “cold” which require deployment or configuration).
As a more detailed illustration of an Internet of Things (IoT) network,
Often, IoT devices are limited in memory, size, or functionality, allowing larger numbers to be deployed for a similar (or lower) cost compared to the cost of smaller numbers of larger devices. However, an IoT device may be a smartphone, laptop, tablet, or PC, or other larger device. Further, an IoT device may be a virtual device, such as an application on a smartphone or other computing device. IoT devices may include IoT gateways, used to couple IoT devices to other IoT devices and to cloud applications, for data storage, process control, and the like.
Networks of IoT devices may include commercial and home automation devices, such as water distribution systems, electric power distribution systems, pipeline control systems, plant control systems, light switches, thermostats, locks, cameras, alarms, motion sensors, and the like. The IoT devices may be accessible through remote computers, servers, and other systems, for example, to control systems or access data.
Returning to
In an example embodiment, the network 4300 can further include or be communicatively coupled to a Trust-a-a-Service instance or deployment configured to perform trust attestation operations within the network 4300, such as that discussed above.
Other example groups of IoT devices may include remote weather stations 4314, local information terminals 4316, alarm systems 4318, automated teller machines 4320, alarm panels 4322, or moving vehicles, such as emergency vehicles 4324 or other vehicles 4326, among many others. These IoT devices may be in communication with other IoT devices, with servers 4304, with another IoT device or system, another edge computing or “fog” computing system, or a combination therein. The groups of IoT devices may be deployed in various residential, commercial, and industrial settings (including in both private or public environments).
As may be seen from
Clusters of IoT devices may be equipped to communicate with other IoT devices as well as with a cloud network. This may allow the IoT devices to form an ad-hoc network between the devices, allowing them to function as a single device, which may be termed a fog device or system. Clusters of IoT devices, such as may be provided by the remote weather stations 4314 or the traffic control group 4306, may be equipped to communicate with other IoT devices as well as with the network 4300. This may allow the IoT devices to form an ad-hoc network between the devices, allowing them to function as a single device, which also may be termed a fog device or system.
In further examples, a variety of topologies may be used for IoT networks comprising IoT devices, with the IoT networks coupled through backbone links to respective gateways. For example, a number of IoT devices may communicate with a gateway, and with each other through the gateway. The backbone links may include any number of wired or wireless technologies, including optical networks, and may be part of a local area network (LAN), a wide area network (WAN), or the Internet. Additionally, such communication links facilitate optical signal paths among both IoT devices and gateways, including the use of MUXing/deMUXing components that facilitate the interconnection of the various devices.
The network topology may include any number of types of IoT networks, such as a mesh network provided with the network using Bluetooth low energy (BLE) links. Other types of IoT networks that may be present include a wireless local area network (WLAN) network used to communicate with IoT devices through IEEE 802.11 (Wi-Fi®) links, a cellular network used to communicate with IoT devices through an LTE/LTE-A (4G) or 5G cellular network, and a low-power wide-area (LPWA) network, for example, a LPWA network compatible with the LoRaWan specification promulgated by the LoRa alliance, or an IPV6 over Low Power Wide-Area Networks (LPWAN) network compatible with a specification promulgated by the Internet Engineering Task Force (IETF).
Further, the respective IoT networks may communicate with an outside network provider (e.g., a tier 2 or tier 3 provider) using any number of communications links, such as an LTE cellular link, a LPWA link, or a link based on the IEEE 802.15.4 standard, such as Zigbee®. The respective IoT networks may also operate with the use of a variety of network and internet application protocols such as the Constrained Application Protocol (CoAP). The respective IoT networks may also be integrated with coordinator devices that provide a chain of links that forms a cluster tree of linked devices and networks.
IoT networks may be further enhanced by the integration of sensing technologies, such as sound, light, electronic traffic, facial and pattern recognition, smell, vibration, into the autonomous organizations among the IoT devices. The integration of sensory systems may allow systematic and autonomous communication and coordination of service delivery against contractual service objectives, orchestration, and quality of service (QOS) based swarming and coordination/combinations of resources.
An IoT network, arranged as a mesh network, for instance, may be enhanced by systems that perform inline data-to-information transforms. For example, self-forming chains of processing resources comprising a multi-link network may distribute the transformation of raw data to information in an efficient manner, and the ability to differentiate between assets and resources and the associated management of the assets and resources. Furthermore, the proper components of infrastructure and resource-based trust and service indices may be inserted to improve the data integrity, quality, assurance, and deliver a metric of data confidence.
At a more generic level, an edge computing system may be described to encompass any number of deployments operating in the edge cloud 4010, which provide coordination from client and distributed computing devices.
A respective node or device of the edge computing system is located at a particular layer corresponding to layers 4410, 4420, 4430, 4440, and 4450. For example, the client compute nodes 4402 are located at an endpoint layer 4410, while the edge gateway nodes 4412 are located at an edge devices layer 4420 (local level) of the edge computing system. Additionally, the edge aggregation nodes 4422 (and/or fog devices 4424, if arranged or operated with or among a fog networking configuration 4426) is located at a network access layer 4430 (an intermediate level). Fog computing (or “fogging”) generally refers to extensions of cloud computing to the edge of an enterprise's network, typically in a coordinated distributed or multi-node network. Some forms of fog computing provide the deployment of compute, storage, and networking services between end devices and cloud computing data centers, on behalf of the cloud computing locations. Such forms of fog computing provide operations that are consistent with edge computing as discussed herein; many of the edge computing aspects discussed herein apply to fog networks, fogging, and fog configurations. Further, aspects of the edge computing systems discussed herein may be configured as a fog, or aspects of a fog may be integrated into an edge computing architecture.
The core data center 4432 is located at a core network layer 4440 (e.g., a regional or geographically-central level), while the global network cloud 4442 is located at a cloud data center layer 4450 (e.g., a national or global layer). The use of “core” is provided as a term for a centralized network location—deeper in the network—which is accessible by multiple edge nodes or components; however, a “core” does not necessarily designate the “center” or the deepest location of the network. Accordingly, the core data center 4432 may be located within, at, or near the edge cloud 4411 (e.g., edge cloud 4010).
Although an illustrative number of client compute nodes 4402, edge gateway nodes 4412, edge aggregation nodes 4422, core data centers 4432, and global network clouds 4442 are shown in
Consistent with the examples provided herein, a client compute node 4402 may be embodied as any type of end point component, device, appliance, or “thing” capable of communicating as a producer or consumer of data. Further, the label “node” or “device” as used in the edge computing system 4400 does not necessarily mean that such node or device operates in a client or minion/follower/agent role; rather, any of the nodes or devices in the edge computing system 4400 refer to individual entities, nodes, or subsystems which include discrete or connected hardware or software configurations to facilitate or use the edge cloud 4411.
As such, the edge cloud 4411 is formed from network components and functional features operated by and within the edge gateway nodes 4412 and the edge aggregation nodes 4422 of layers 4420, 4430, respectively. The edge cloud 4411 may be embodied as any type of network that provides edge computing and/or storage resources which are proximately located to radio access network (RAN) capable endpoint devices (e.g., mobile computing devices, IoT devices, smart devices, etc.), which are shown in
In some examples, the edge cloud 4411 may form a portion of or otherwise provide an ingress point into or across a fog networking configuration 4426 (e.g., a network of fog devices 4424, not shown in detail), which may be embodied as a system-level horizontal and distributed architecture that distributes resources and services to perform a specific function. For instance, a coordinated and distributed network of fog devices 4424 may perform computing, storage, control, or networking aspects in the context of an IoT system arrangement. Other networked, aggregated, and distributed functions may exist in the edge cloud 4411 between the cloud data center layer 4450 and the client endpoints (e.g., client compute nodes 4402). Some of these are discussed in the following sections in the context of network functions or service virtualization, including the use of virtual edges and virtual services which are orchestrated for multiple stakeholders.
The edge gateway nodes 4412 and the edge aggregation nodes 4422 cooperate to provide various edge services and compute security features to the client compute nodes 4402. Furthermore, because an individual client compute node 4402 may be stationary or mobile, a respective edge gateway node 4412 may cooperate with other edge gateway devices to propagate presently provided edge services and compute security features as the corresponding client compute node 4402 moves about a region. To do so, respective nodes of the edge gateway nodes 4412 and/or edge aggregation nodes 4422 may support multiple tenancies and multiple stakeholder configurations, in which services from (or hosted for) multiple service providers and multiple consumers may be supported and coordinated across a single or multiple compute devices.
In further examples, any of the compute nodes or devices discussed with reference to the present edge computing systems and environment may be fulfilled based on the components depicted in
In the simplified example depicted in
The compute node 4500 may be embodied as any type of engine, device, or collection of devices capable of performing various compute functions. In some examples, the compute node 4500 may be embodied as a single device such as an integrated circuit, an embedded system, a field-programmable gate array (FPGA), a system-on-a-chip (SOC), or other integrated system or device. In the illustrative example, the compute node 4500 includes or is embodied as a processor 4504 and a memory 4506. The processor 4504 may be embodied as any type of processor capable of performing the functions described herein (e.g., executing an application). For example, the processor 4504 may be embodied as a multi-core processor(s), a microcontroller, a processing unit, a specialized or special purpose processing unit, or other processor or processing/controlling circuit. In some examples, the processor 4504 may be embodied as, include, or be coupled to an FPGA, an application-specific integrated circuit (ASIC), reconfigurable hardware or hardware circuitry, or other specialized hardware to facilitate performance of the functions described herein. Also in some examples, the processor 4504 may be embodied as a specialized x-processing unit (xPU) also known as a data processing unit (DPU), infrastructure processing unit (IPU), or network processing unit (NPU). Such an xPU may be embodied as a standalone circuit or circuit package, integrated within an SOC, or integrated with networking circuitry (e.g., in a SmartNIC, or enhanced SmartNIC), dedicated compute circuitry, storage devices, or AI or specialized hardware (e.g., GPUs, programmed FPGAs, Network Processing Units (NPUs), Infrastructure Processing Units (IPUs), Storage Processing Units (SPUs), AI Processors (APUs), Data Processing Units (DPUs), Edge Processing Units (EPUs), or other specialized compute units such as a cryptographic processing unit/accelerator). Such an xPU may be designed to receive programming to process one or more data streams and perform specific tasks and actions for the data streams (such as hosting microservices, performing service management or orchestration, organizing or managing server or data center hardware, managing service meshes, or collecting and distributing telemetry), outside of the CPU or general purpose processing hardware. Thus, any of the C2E techniques described herein and their accompanying attestation, trust, security, provisioning, testing, simulation, or orchestration functions may be coordinated by an xPU. However, it will be understood that an xPU, a SOC, a CPU, and other variations of the processor 4504 may work in coordination with each other to execute many types of operations and instructions within and on behalf of the compute node 4500.
The main memory 4506 may be embodied as any type of volatile (e.g., dynamic random access memory (DRAM), etc.) or non-volatile memory or data storage capable of performing the functions described herein. Volatile memory may be a storage medium that requires power to maintain the state of data stored by the medium. Non-limiting examples of volatile memory may include various types of random access memory (RAM), such as DRAM or static random access memory (SRAM). One particular type of DRAM that may be used in a memory module is synchronous dynamic random access memory (SDRAM).
In one example, the memory device is a block addressable memory device, such as those based on NAND or NOR technologies. A memory device may also include a three-dimensional crosspoint memory device (e.g., Intel® 3D XPoint™ memory), or other byte-addressable write-in-place nonvolatile memory devices. The memory device may refer to the die itself and/or to a packaged memory product. In some examples, 3D crosspoint memory (e.g., Intel® 3D XPoint™ memory) may comprise a transistor-less stackable crosspoint architecture in which memory cells sit at the intersection of word lines and bit lines and are individually addressable and in which bit storage is based on a change in bulk resistance. In some examples, all or a portion of the main memory 4506 may be integrated into the processor 4504. The main memory 4506 may store various software and data used during operation such as one or more applications, data operated on by the application(s), libraries, and drivers.
The compute circuitry 4502 is communicatively coupled to other components of the compute node 4500 via the I/O subsystem 4508, which may be embodied as circuitry and/or components to facilitate input/output operations with the compute circuitry 4502 (e.g., with the processor 4504 and/or the main memory 4506) and other components of the compute circuitry 4502. For example, the I/O subsystem 4508 may be embodied as, or otherwise include memory controller hubs, input/output control hubs, integrated sensor hubs, firmware devices, communication links (e.g., point-to-point links, bus links, wires, cables, light guides, printed circuit board traces, etc.), and/or other components and subsystems to facilitate the input/output operations. In some examples, the I/O subsystem 4508 may form a portion of a system-on-a-chip (SoC) and be incorporated, along with one or more of the processor 4504, the main memory 4506, and other components of the compute circuitry 4502, into the compute circuitry 4502.
The one or more illustrative data storage devices 4510 may be embodied as any type of device configured for short-term or long-term storage of data such as, for example, memory devices and circuits, memory cards, hard disk drives, solid-state drives, or other data storage devices. A respective data storage device 4510 may include a system partition that stores data and firmware code for the data storage device 4510. A respective data storage device 4510 may also include one or more operating system partitions that store data files and executables for operating systems depending on, for example, the type of compute node 4500.
The communication circuitry 4512 may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications over a network between the compute circuitry 4502 and another compute device (e.g., an edge gateway node 4412 of the edge computing system 4400). The communication circuitry 4512 may be configured to use any one or more communication technology (e.g., wired or wireless communications) and associated protocols (e.g., a cellular networking protocol such a 3GPP 4G or 5G standard, a wireless local area network protocol such as IEEE 802.11/Wi-Fi®, a wireless wide area network protocol, Ethernet, Bluetooth®, Bluetooth Low Energy, an IoT protocol such as IEEE 802.15.4 or ZigBee®, low-power wide-area network (LPWAN) or low-power wide-area (LPWA) protocols, etc.) to effect such communication.
The illustrative communication circuitry 4512 includes a network interface controller (NIC) 4520, which may also be referred to as a host fabric interface (HFI). The NIC 4520 may be embodied as one or more add-in-boards, daughter cards, network interface cards, controller chips, chipsets, or other devices that may be used by the compute node 4500 to connect with another compute device (e.g., an edge gateway node 4412). In some examples, the NIC 4520 may be embodied as part of a system-on-a-chip (SoC) that includes one or more processors or included on a multichip package that also contains one or more processors. In some examples, the NIC 4520 may include a local processor (not shown) and/or a local memory and storage (not shown) that are local to the NIC 4520. In such examples, the local processor of the NIC 4520 (which can include general-purpose accelerators or specific accelerators) may be capable of performing one or more of the functions of the compute circuitry 4502 described herein. Additionally, or alternatively, the local memory of the NIC 4520 may be integrated into one or more components of the client compute node at the board level, socket level, chip level, and/or other levels.
Additionally, in some examples, a respective compute node 4500 may include one or more peripheral devices 4514. Such peripheral devices 4514 may include any type of peripheral device found in a compute device or server such as audio input devices, a display, other input/output devices, interface devices, and/or other peripheral devices, depending on the particular type of the compute node 4500. In further examples, the compute node 4500 may be embodied by a respective edge compute node in an edge computing system (e.g., client compute node 4402, edge gateway node 4412, edge aggregation node 4422) or like forms of appliances, computers, subsystems, circuitry, or other components.
In a more detailed example,
The edge computing node 4650 may include processing circuitry in the form of a processor 4652, which may be a microprocessor, a multi-core processor, a multithreaded processor, an ultra-low voltage processor, an embedded processor, an xPU/DPU/IPU/NPU, special purpose processing unit, specialized processing unit, or other known processing elements. The processor 4652 may be a part of a system on a chip (SoC) in which the processor 4652 and other components are formed into a single integrated circuit, or a single package, such as the Edison™ or Galileo™ SoC boards from Intel Corporation, Santa Clara, California. As an example, the processor 4652 may include an Intel® Architecture Core™ based processor, such as a Quark™, an Atom™, an i3, an i5, an i7, an i9, or an MCU-class processor, or another such processor available from Intel®. However, any number other processors may be used, such as available from Advanced Micro Devices, Inc. (AMD) of Sunnyvale, California, a MIPS-based design from MIPS Technologies, Inc. of Sunnyvale, California, an ARM-based design licensed from ARM Holdings, Ltd. or a customer thereof, or their licensees or adopters. The processors may include units such as an A5-A14 processor from Apple® Inc., a Snapdragon™ processor from Qualcomm® Technologies, Inc., or an OMAP™ processor from Texas Instruments, Inc. The processor 4652 and accompanying circuitry may be provided in a single socket form factor, multiple socket form factor, or a variety of other formats, including in limited hardware configurations or configurations that include fewer than all elements shown in
The processor 4652 may communicate with a system memory 4654 over an interconnect 4656 (e.g., a bus). Any number of memory devices may be used to provide for a given amount of system memory. As examples, the memory may be random access memory (RAM) in accordance with a Joint Electron Devices Engineering Council (JEDEC) design such as the DDR or mobile DDR standards (e.g., LPDDR, LPDDR2, LPDDR3, or LPDDR4). In particular examples, a memory component may comply with a DRAM standard promulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2 SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 for Low Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, and JESD209-4 for LPDDR4. Such standards (and similar standards) may be referred to as DDR-based standards and communication interfaces of the storage devices that implement such standards may be referred to as DDR-based interfaces. In various implementations, the individual memory devices may be of any number of different package types such as single die package (SDP), dual die package (DDP), or quad die package (Q17P). These devices, in some examples, may be directly soldered onto a motherboard to provide a lower profile solution, while in other examples the devices are configured as one or more memory modules that in turn couple to the motherboard by a given connector. Any number of other memory implementations may be used, such as other types of memory modules, e.g., dual inline memory modules (DIMMs) of different varieties including but not limited to microDIMMs or MiniDIMMs.
To provide for persistent storage of information such as data, applications, operating systems, and so forth, a storage 4658 may also couple to the processor 4652 via the interconnect 4656. In an example, the storage 4658 may be implemented via a solid-state disk drive (SSDD). Other devices that may be used for the storage 4658 include flash memory cards, such as SD cards, microSD cards, XD picture cards, and the like, and USB flash drives. In an example, the memory device may be or may include memory devices that use chalcogenide glass, multi-threshold level NAND flash memory, NOR flash memory, single or multi-level Phase Change Memory (PCM), a resistive memory, nanowire memory, ferroelectric transistor random access memory (FeTRAM), anti-ferroelectric memory, magnetoresistive random access memory (MRAM) memory that incorporates memristor technology, resistive memory including the metal oxide base, the oxygen vacancy base and the conductive bridge Random Access Memory (CB-RAM), or spin-transfer torque (STT)-MRAM, a spintronic magnetic junction memory-based device, a magnetic tunneling junction (MTJ) based device, a DW (Domain Wall) and SOT (Spin-Orbit Transfer) based device, a thyristor-based memory device, or a combination of any of the above, or other memory.
In low power implementations, the storage 4658 may be on-die memory or registers associated with the processor 4652. However, in some examples, the storage 4658 may be implemented using a micro hard disk drive (HDD) or solid-state drive (SSD). Further, any number of new technologies may be used for the storage 4658 in addition to, or instead of, the technologies described, such resistance change memories, phase change memories, holographic memories, or chemical memories, among others.
The components may communicate over the interconnect 4656. The interconnect 4656 may include any number of technologies, including industry-standard architecture (ISA), extended ISA (EISA), peripheral component interconnect (PCI), peripheral component interconnect extended (PCI-X), PCI express (PCIe), or any number of other technologies. The interconnect 4656 may be a proprietary bus, for example, used in an SoC based system. Other bus systems may be included, such as an I2C interface, an SPI interface, point to point interfaces, and a power bus, among others.
The interconnect 4656 may couple the processor 4652 to a transceiver 4666, for communications with the connected edge devices 4662. The transceiver 4666 may use any number of frequencies and protocols, such as 2.4 Gigahertz (GHz) transmissions under the IEEE 802.15.4 standard, using the Bluetooth® low energy (BLE) standard, as defined by the Bluetooth® Special Interest Group, or the ZigBee® standard, among others. Any number of radios, configured for a particular wireless communication protocol, may be used for the connections to the connected edge devices 4662. For example, a wireless local area network (WLAN) unit may be used to implement Wi-Fi® communications in accordance with the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard. In addition, wireless wide area communications, e.g., according to a cellular or other wireless wide area protocol, may occur via a wireless wide area network (WWAN) unit.
The wireless network transceiver 4666 (or multiple transceivers) may communicate using multiple standards or radios for communications at a different range. For example, the edge computing node 4650 may communicate with close devices, e.g., within about 10 meters, using a local transceiver based on BLE, or another low power radio, to save power. More distant connected edge devices 4662, e.g., within about 50 meters, may be reached over ZigBee or other intermediate power radios. Both communications techniques may take place over a single radio at different power levels or may take place over separate transceivers, for example, a local transceiver using BLE and a separate mesh transceiver using ZigBee®.
A wireless network transceiver 4666 (e.g., a radio transceiver) may be included to communicate with devices or services in the edge cloud 4690 via local or wide area network protocols. The wireless network transceiver 4666 may be an LPWA transceiver that follows the IEEE 802.15.4, or IEEE 802.15.4g standards, among others. The edge computing node 4650 may communicate over a wide area using LoRaWAN™ (Long Range Wide Area Network) developed by Semtech and the LoRa Alliance. The techniques described herein are not limited to these technologies but may be used with any number of other cloud transceivers that implement long-range, low bandwidth communications, such as Sigfox, and other technologies. Further, other communications techniques, such as time-slotted channel hopping, described in the IEEE 802.15.4e specification may be used.
Any number of other radio communications and protocols may be used in addition to the systems mentioned for the wireless network transceiver 4666, as described herein. For example, the transceiver 4666 may include a cellular transceiver that uses spread spectrum (SPA/SAS) communications for implementing high-speed communications. Further, any number of other protocols may be used, such as Wi-Fi® networks for medium speed communications and provision of network communications. The transceiver 4666 may include radios that are compatible with any number of 3GPP (Third Generation Partnership Project) specifications, such as Long Term Evolution (LTE) and 5th Generation (5G) communication systems, discussed in further detail at the end of the present disclosure. A network interface controller (NIC) 4668 may be included to provide a wired communication to nodes of the edge cloud 4690 or other devices, such as the connected edge devices 4662 (e.g., operating in a mesh). The wired communication may provide an Ethernet connection or may be based on other types of networks, such as Controller Area Network (CAN), Local Interconnect Network (LIN), DeviceNet, ControlNet, Data Highway+, PROFIBUS, or PROFINET, Time Sensitive Networks (TSN), among many others. An additional NIC 4668 may be included to enable connecting to a second network, for example, a first NIC 4668 providing communications to the cloud over Ethernet, and a second NIC 4668 providing communications to other devices over another type of network.
Given the variety of types of applicable communications from the device to another component or network, applicable communications circuitry used by the device may include or be embodied by any one or more of components 4664, 4666, 4668, or 4670. Accordingly, in various examples, applicable means for communicating (e.g., receiving, transmitting, etc.) may be embodied by such communications circuitry.
The edge computing node 4650 may include or be coupled to acceleration circuitry 4664, which may be embodied by one or more AI accelerators, a neural compute stick, neuromorphic hardware, an FPGA, an arrangement of GPUs, an arrangement of xPUs/DPUs/IPU/NPUs, one or more SoCs, one or more CPUs, one or more digital signal processors, dedicated ASICs, or other forms of specialized processors or circuitry designed to accomplish one or more specialized tasks. These tasks may include AI processing (including machine learning, training, inferencing, and classification operations), visual data processing, network data processing, object detection, rule analysis, or the like. Accordingly, in various examples, applicable means for acceleration may be embodied by such acceleration circuitry.
The interconnect 4656 may couple the processor 4652 to a sensor hub or external interface 4670 that is used to connect additional devices or subsystems. The devices may include sensors 4672, such as accelerometers, level sensors, flow sensors, optical light sensors, camera sensors, temperature sensors, a global navigation system (e.g., GPS) sensors, pressure sensors, barometric pressure sensors, and the like. The hub or interface 4670 further may be used to connect the edge computing node 4650 to actuators 4674, such as power switches, valve actuators, an audible sound generator, a visual warning device, and the like.
In some optional examples, various input/output (I/O) devices may be present within or connected to, the edge computing node 4650. For example, a display or other output device 4684 may be included to show information, such as sensor readings or actuator position. An input device 4686, such as a touch screen or keypad may be included to accept input. An output device 4684 may include any number of forms of audio or visual display, including simple visual outputs such as binary status indicators (e.g., LEDs) and multi-character visual outputs, or more complex outputs such as display screens (e.g., LCD screens), with the output of characters, graphics, multimedia objects, and the like being generated or produced from the operation of the edge computing node 4650. A display or console hardware, in the context of the present system, may be used to provide output and receive input of an edge computing system; to manage components or services of an edge computing system; identify a state of an edge computing component or service; or to conduct any other number of management or administration functions or service use cases.
A battery 4676 may power the edge computing node 4650, although, in examples in which the edge computing node 4650 is mounted in a fixed location, it may have a power supply coupled to an electrical grid, or the battery may be used as a backup or for temporary capabilities. The battery 4676 may be a lithium-ion battery, or a metal-air battery, such as a zinc-air battery, an aluminum-air battery, a lithium-air battery, and the like.
A battery monitor/charger 4678 may be included in the edge computing node 4650 to track the state of charge (SoCh) of the battery 4676. The battery monitor/charger 4678 may be used to monitor other parameters of the battery 4676 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 4676. The battery monitor/charger 4678 may include a battery monitoring integrated circuit, such as an LTC4020 or an LTC2990 from Linear Technologies, an ADT7488A from ON Semiconductor of Phoenix Arizona, or an IC from the UCD90xxx family from Texas Instruments of Dallas, TX. The battery monitor/charger 4678 may communicate the information on the battery 4676 to the processor 4652 over the interconnect 4656. The battery monitor/charger 4678 may also include an analog-to-digital (ADC) converter that enables the processor 4652 to directly monitor the voltage of the battery 4676 or the current flow from the battery 4676. The battery parameters may be used to determine actions that the edge computing node 4650 may perform, such as transmission frequency, mesh network operation, sensing frequency, and the like.
A power block 4680, or other power supply coupled to a grid, may be coupled with the battery monitor/charger 4678 to charge the battery 4676. In some examples, the power block 4680 may be replaced with a wireless power receiver to obtain the power wirelessly, for example, through a loop antenna in the edge computing node 4650. A wireless battery charging circuit, such as an LTC4020 chip from Linear Technologies of Milpitas, California, among others, may be included in the battery monitor/charger 4678. The specific charging circuits may be selected based on the size of the battery 4676, and thus, the current required. The charging may be performed using the Airfuel standard promulgated by the Airfuel Alliance, the Qi wireless charging standard promulgated by the Wireless Power Consortium, or the Rezence charging standard, promulgated by the Alliance for Wireless Power, among others.
The storage 4658 may include instructions 4682 in the form of software, firmware, or hardware commands to implement the techniques described herein. Although such instructions 4682 are shown as code blocks included in the memory 4654 and the storage 4658, it may be understood that any of the code blocks may be replaced with hardwired circuits, for example, built into an application-specific integrated circuit (ASIC).
Also in a specific example, the instructions 4682 on the processor 4652 (separately, or in combination with the instructions 4682 of the machine readable medium 4660) may configure execution or operation of a trusted execution environment (TEE) 4695. In an example, the TEE 4695 operates as a protected area accessible to the processor 4652 for secure execution of instructions and secure access to data. Various implementations of the TEE 4695, and an accompanying secure area in the processor 4652 or the memory 4654 may be provided, for instance, through use of Intel® Software Guard Extensions (SGX), AMD® Secure Encrypted Virtualization (SEV), or ARM® TrustZone® hardware security extensions, Intel® Management Engine (ME), or Intel® Converged Security Manageability Engine (CSME). Other aspects of security hardening, hardware roots-of-trust, and trusted or protected operations may be implemented in the edge computing node 4650 through the TEE 4695 and the processor 4652.
In an example, the instructions 4682 provided via memory 4654, the storage 4658, or the processor 4652 may be embodied as a non-transitory, machine-readable medium 4660 including code to direct the processor 4652 to perform electronic operations in the edge computing node 4650. The processor 4652 may access the non-transitory, machine-readable medium 4660 over the interconnect 4656. For instance, the non-transitory, machine-readable medium 4660 may be embodied by devices described for the storage 4658 or may include specific storage units such as optical disks, flash drives, or any number of other hardware devices. The non-transitory, machine-readable medium 4660 may include instructions to direct the processor 4652 to perform a specific sequence or flow of actions, for example, as described with respect to the flowchart(s) and block diagram(s) of operations and functionality depicted above. As used herein, the terms “machine-readable medium,” “computer-readable medium,” “machine-readable storage,” and “computer-readable storage” are interchangeable.
In an example embodiment, the edge computing node 4650 can be implemented using components/modules/blocks 4652-4686 which are configured as IP Blocks. An individual IP Block may contain a hardware RoT (e.g., device identifier composition engine, or DICE), where a DICE key may be used to identify and attest the IP Block firmware to a peer IP Block or remotely to one or more of components/modules/blocks 4662-4680. Thus, it will be understood that the node 4650 itself may be implemented as a SoC or standalone hardware package.
In further examples, a machine-readable medium also includes any tangible medium that is capable of storing, encoding or carrying instructions for execution by a machine and that cause the machine to perform any one or more of the methodologies of the present disclosure or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. A “machine-readable medium” thus may include but is not limited to, solid-state memories, and optical and magnetic media. Specific examples of machine-readable media include non-volatile memory, including but not limited to, by way of example, semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The instructions embodied by a machine-readable medium may further be transmitted or received over a communications network using a transmission medium via a network interface device utilizing any one of a number of transfer protocols (e.g., HTTP).
A machine-readable medium may be provided by a storage device or other apparatus which is capable of hosting data in a non-transitory format. In an example, information stored or otherwise provided on a machine-readable medium may be representative of instructions, such as instructions themselves or a format from which the instructions may be derived. This format from which the instructions may be derived may include source code, encoded instructions (e.g., in compressed or encrypted form), packaged instructions (e.g., split into multiple packages), or the like. The information representative of the instructions in the machine-readable medium may be processed by processing circuitry into the instructions to implement any of the operations discussed herein. For example, deriving the instructions from the information (e.g., processing by the processing circuitry) may include: compiling (e.g., from source code, object code, etc.), interpreting, loading, organizing (e.g., dynamically or statically linking), encoding, decoding, encrypting, unencrypting, packaging, unpackaging, or otherwise manipulating the information into the instructions.
In an example, the derivation of the instructions may include assembly, compilation, or interpretation of the information (e.g., by the processing circuitry) to create the instructions from some intermediate or preprocessed format provided by the machine-readable medium. The information, when provided in multiple parts, may be combined, unpacked, and modified to create the instructions. For example, the information may be in multiple compressed source code packages (or object code, or binary executable code, etc.) on one or several remote servers. The source code packages may be encrypted when in transit over a network and decrypted, uncompressed, assembled (e.g., linked) if necessary, and compiled or interpreted (e.g., into a library, standalone executable, etc.) at a local machine, and executed by the local machine.
The block diagrams of
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Implementation of the preceding techniques may be accomplished through any number of specifications, configurations, or example deployments of hardware and software. It should be understood that the functional units or capabilities described in this specification may have been referred to or labeled as components or modules, to more particularly emphasize their implementation independence. Such components may be embodied by any number of software or hardware forms. For example, a component or module may be implemented as a hardware circuit comprising custom very-large-scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A component or module may also be implemented in programmable hardware devices such as field-programmable gate arrays, programmable array logic, programmable logic devices, or the like. Components or modules may also be implemented in software for execution by various types of processors. An identified component or module of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified component or module need not be physically located together but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the component or module and achieve the stated purpose for the component or module.
Indeed, a component or module of executable code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices or processing systems. In particular, some aspects of the described process (such as code rewriting and code analysis) may take place on a different processing system (e.g., in a computer in a data center), than that in which the code is deployed (e.g., in a computer embedded in a sensor or robot). Similarly, operational data may be identified and illustrated herein within components or modules and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network. The components or modules may be passive or active, including agents operable to perform desired functions.
In the above Detailed Description, various features may be grouped to streamline the disclosure. However, claims may not set forth every feature disclosed herein as embodiments may feature a subset of said features. Further, embodiments may include fewer features than those disclosed in a particular example. Thus, the following claims are hereby incorporated into the Detailed Description, with a claim standing on its own as a separate embodiment.
This application claims the benefit of priority to U.S. Provisional Patent Application No. 63/456,310, filed Mar. 31, 2023, and titled “CLOUD TO EDGE SECURITY”, which is incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63456310 | Mar 2023 | US |
