Patent Application
20240264874
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-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 again independent of a workload orchestrator.
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 create 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 fille 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 clastic 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 clastic 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 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. 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 normal 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 re-issuance.
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.
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 min. 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 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.
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.
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.
Microservices may be executed on a variety of platforms including but not limited to an IPU, edge processing unit (EPU), accelerator device, or offload device. An offload device is one that takes a workload from a main processor or other primary processing device, and completed the workload on behalf of the processor.
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 u Service 3318 to facilitate binding the uService 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.
Elastic WLs may be dynamically partitioned, distributed and deployed to be highly responsive and to draw on federated infrastructure for execution, storage, acceleration, and power. Workloads (WLs) and distributed workloads (D-WL) may require interactions with other workloads to perform basic exchanges of information or assets without having to form a federated distributed workload. WLs may rely on container apps where distributed containers interact via an API (e.g., OpenAPI, using REST semantics) that are not aware of security barriers when RESTful semantics are applied to Web, Cloud, and Edge services.
Workloads in which multiple distributed operations need to rendezvous (e.g., synchronize) at least once are common. They arise when transactional updates are required, for example in trade, banking, investment, electronic commerce, and many other areas. Further, the need for time-coordinated or periodically aligned computations (which prompts the need to rendezvous) arises naturally in transportation planning, event management, supply chain optimization, inventory streamlining, shipping and delivery, and other federated operations. Ensuring that systems coordinate in trust-critical operations like software or firmware upgrades, auditing, signing, and other secure operations also drives sequencing and the need for rendezvous operations.
A barrier mechanism may be used to synchronize workloads. For instance, a barrier may pause, suspend, sleep, or put a workload in a wait state to allow it to synchronize with one or more other workloads that are going to rendezvous at the barrier.
In a data center (DC) cloud and across DCs, there is high predictability that distributed tasks rendezvous as needed within some tolerable threshold for outlier latencies—since the high resource elasticity, monitoring oversight, and rich network redundancy in DCs provide effective checks on stragglers. In contrast, when these tasks need to execute in a federated setting across edge (and cloud) installations, barrier synchronization creates significant impediments to predictability. When nodes fail or tasks take a long time to reach their rendezvous points (e.g., barriers), timeouts and other problems arise, and this adds to uncertainty whether a complex chain of actions will actually reach their desired conclusion. Migrating task chains or task webs out of data centers and onto wide-scale edge-based execution becomes overly complicated and riddled with fragilities.
Further, decentralized web apps (DAPS) may use distributed ledger technology (DLT) to synchronize operations of a smart contract. These approaches work when all parties are in the same domain/blockchain. In a multi-stakeholder Edge, WLs must synchronize across domain/DLT via a gateway that preserves asset integrity including prevention of ‘double spend’ or atomic delete. Existing approaches to distributing and decentralizing workloads are not aware of security boundaries, geo-political barriers and geo-location policies that intend to restrict where a WL may reside, run, or access data.
A Secure Asset Transfer (SAT) protocol specifies a gateway interoperability architecture for transferring or exchanging digital assets in accordance with transactionality expectations (ACID (Atomicity, Consistency, Isolation, and Durability) guarantees) while providing for verifiability by third parties. It enables just-in-time trust between entities spanning autonomous networks without a requiring trusted go-between.
The present systems and methods implement barrier synchronization for various distributed workload topologies. In an example, barrier operations are provided as a service with a container sidecar in a pod acting as a proxy for the barrier service for the primary container in the pod. Thus, barrier-as-a-service (BaaS), also referred to as a rendezvous-as-a-service (RaaS), may be provided to compute nodes. The sidecar-based proxy manages peer-to-peer barrier/rendezvous coordination. Barriers may be localized to a group of compute nodes (e.g., local to an edge location, edge cloudlet, or clique of hosts, etc.).
In related examples, elastic workloads may be provisioned with metadata that describes security barriers. Elastic WL distribution, resource assignment, and hosting managers may use barrier synchronization metadata to control or instruct the WL distribution algorithms to account for barrier crossing requirements. This ensures barrier crossing security requirements are met before a barrier crossing is attempted. Subsequent use of the WL that may require crossing the barrier will synchronize to use the most current metadata description of the barrier crossing and ensure subsequent crossing rules are synchronized. In situations where resources and data represent a valued asset that may be transferred, barrier synchronization applies appropriate ACID properties to the transaction to ensure asset value is preserved. Further details are provided in the examples and figures described below.
There are three models illustrated in
In Model A 3600A, a number of waiting tasks z0, z1, . . . , zn 3606A become unblocked and start execute when all of the arrivers 3602A have checked in. A barrier synchronization manager (BSM) may be used to synchronize the arrivers 3602A and departers 3606A.
The barrier synchronization manager (BSM) is placed on universally available and reachable nodes so that the BSM can be a reliable anchor for receiving and acting on barrier check ins. The BMS may execute a boundary action upon receiving the last waited-for check in. After performing the boundary action, the barrier synchronization service notifies each of the departers 3606A, waits for a handshake (ack) from the departers 3606A, and then notifies the arrivers 3602A if the arrivers 3602A requested notification. In general, the executing entities such as processes, containers, threads, etc. which perform the arriver tasks may be different from the entities that perform the departer tasks, but they do not have to be different. In other cases, they may be the same processes, containers, etc. For example, in a worker pool architecture, the arriver and departer tasks may be flexibly scheduled on any worker (a thread, a process, a container, etc.) that is available for such assignment.
As shown in Model B 3600B, it is possible that the departer tasks 3606B are the same as arriver tasks 3606B, as is the case with many BSP (block-sequential-parallel) algorithms. In that sense, Model B 3600B is a special case of Model A 3600A, where the barrier “Y” represents a stage in the execution of these algorithms at which one iteration completes and the next iteration needs to be ordered after all concurrent activities from a previous iteration are completed.
As shown in Model C 3600C, when the last of arrivers 3602C reaches a barrier, a computation and data finalization and redistribution stage needs to be completed before departers 3606C can proceed with their execution. In effect, the barrier “Y” is a double barrier between arrivers 3602C and departers 3606C, where at a first internal barrier 3608, the arrivers 3602C all wait for the last of the arrivers 3602C to reach the barrier, and then, at a second internal barrier 3610 complete a distributed commit action on data, before the departers 3606C can continue.
While Models A-C 3600A-C illustrate general cases of barrier coordination, other coordination operations may exist including a one-to-one rendezvous. For one-to-one rendezvous, a single lightweight intermediary (proxy) can take the delegation for arriver-side proxy actions, which permits the arriver to unblock, and to release resources when possible. It can do so on behalf of multiple one-to-one arriver-to-departer rendezvous points.
For an M-N rendezvous, M arrivers meet at a rendezvous barrier, and then perform a boundary action, a barrier synchronization service lambda is launched, and it forwards the arriver notifications to the BSM. The BSM is placed on universally available and reachable nodes so that the BSM can be a reliable anchor for receiving and acting on barrier check ins. The BSM launches the boundary action upon receiving the last waited-for check in. After performing the boundary action, the BSM notifies each of the N departer tasks, waits for a handshake (ack) from the departers, and then notifies the arrivers if the arrivers have requested an acknowledgement.
For Secure Assets Transfer (SAT), the barrier may act as an escrow agent. At the point of a barrier, whether one-to-one on M-N, it may often be the case that digital assets may need to be transferred in a secure (auditable, non-repudiable, and integral) manner. In such a case, the BSM implements the SAT protocol as a proxy for asynchronous transfer in two phases: in the first phase, it completes the transfer of digital assets so that the BSM becomes an escrow owner of the digital assets at the arrivers' side, and then once the arriver-side rendezvous is complete, it initiates the transfer of the escrowed digital assets to the departer(s); for each phase, it implements the provisions of the SAT protocol. The arrivers may disconnect from the BSM after the digital assets are transferred to the BSM or the arrivers can disconnect after the completion of the transfer to the departers.
Another barrier coordination operation is reordering departers. In such a case, departers are the same arrivers, but the reason for the barrier is to implement or enforce a specific order in which arrivers continue past the barrier (as departers). The BSM implements a signaling service. The BSM may require all arrivers to reach the barrier before releasing them from the barrier in order, or it may only require that each arriver be released in the required reorder and block other arrivers as needed to enforce the departer order.
Another barrier coordination operation is to ensure consistency of state. In this case, the BSM implements a provision for receiving all updates from each of the arrivers, and then commits all of the updates once the last arriver has reached the barrier. Until that point, all data modifications or other updates by the arrivers are considered provisional. The BMS may implement a multi-version concurrency protocol to ensure consistency, and in that case, the BSM does not have to implement an all-or-nothing (transactional) update capability.
Another barrier coordination operation is to ensure wavefront consistency. This is a special case of consistency where the BSM does not provide for consistency of states, but about aligned snapshots of computation among concurrent tasks which may or may not share a global state. This is a special case where the barrier is a barrier in virtual time that different tasks may reach at different times, but where the virtual time is in the form of epoch boundaries, where an epoch boundary is defined by some condition being signaled for each task. One simple usage is a distributed checkpoint to which a distributed computation can be rewound, and from where it can be continued forward. Another is to record and replay or simulate or audit a distributed computation by verifying consistency at each event boundary.
Another barrier coordination operation is to control max-lag and feedback. The BSM may implement a max-lag control so that when the barrier does not get reached within the max-lag amount of time, notifications can be provided back to all registered parties that have registered an interest in receiving the notification. This is to permit: (a) early release from a barrier sync when the barrier is unlikely to be achieved in the stipulated time, handling errors/exceptions, and reissuing the computation that was unable to achieve the barrier, but this time using alternative resources or using alternative strategies; and (b) retiring completed transactions so long as a quorum of acknowledgements has been received within the max-lag amount of time (as in Raft protocol).
Another barrier coordination operation is to control resources. In workloads that have a cap on the rate at which API calls can be performed to a metered service (e.g., tokenbucket scheduling), a BSM can be configured so that it is only a resource barrier and not an event barrier. Thus, it can collect tokens from arrivers and signal the departers according to the number of tokens received, subject to a max rate (tokens received over time). The tokens may represent resource allocation, resource usage, or the like. This achieves a distributed version of counting semaphores on the one hand, and as a token bucket scheme for regulating the rate of departures at the barrier.
Another barrier coordination operation is to handle data or operation handoff or baton passing. A BSM may also function as a pooling and routing service for data and operations, such that M arrivers are mapped to N departers, where the arrivers and departers may or may not be in disjoint sets. A routing table is configured at the barrier service so that arriver K (1<=K<=M) hands off its data or an operation code or both to the routing service, and the routing service signals one or more specific departers J (1<=J<=N) and handed off the operation and data payload to perform, according to the table that is configured with the BSM. Specifically, an operation may be launched at the right departer that has hardware or acceleration logic or data sharing resources set up with the arrivers that are expected to trigger the forward operation. The barrier service also implements publish-subscribe queues for receiving (at the arriver end) and for sending (at the departer end) so that the dispatching of operations need not require blocking for arrivers.
Another barrier coordination operation is to manage privacy and integrity compliance. The BSM provides for secure anonymization, encryption, mapping, etc. but where different arrivers entities enter the barrier with their own different security and privacy sensitive attributes, and exchange needed information via an enclave that a) integrates the information and b) transforms the integrated information according to the security and privacy goals, with each party remaining opaque (to protect privacy) to other parties.
Another barrier coordination operation is to provide multi-stage provisional commit barriers. Barriers may implement multiple stages, which are dynamically composed of single-phase barriers. This is to achieve a compound barrier in which the internal barriers implement commit points, and the compound barrier either issue a successful completion or a timeout or an error or an abort as a total operation. Thus, the entry-exit protocol for the compound barrier provides for implementing nested distributed transactions.
Another barrier coordination operation is to resolve circular dependencies. Often different stages in an operation are ordered so that updates to a persistent state can be kept traceable and auditable. In data graphs (data objects with persistent structure linkages between one another—such as in file systems, object stores, etc.) it is also necessary to recognize which updates were partial and therefore need to be retracted so that the overall structure remains correct. Databases usually achieve this with logs (either undo-logs, redo-logs, and often both), however in a highly decentralized, distributed set of actors, it is a challenge to collect and later re-render (apply undo-logs and then apply redo logs) logs of operations, and so is solved by a combination of redundancy techniques (such as keeping three copies of data) and periodic barrier points for checkpointing. All these however severely constrain the ability to proceed with concurrency when the number of entities that operate on distributed data is large, varying over time, and separated from each other by non-trivial latencies. Again, databases solve this by using multi-version concurrency protocols; but such protocols do not work well for other data organizations (for example, file systems, distributed object caches, etc.).
An approach is to organize object data and object linkages hierarchically so that lower levels in a structure are reflected into persistence before making updates to higher levels and reflecting them into persistence as well, which has two benefits: (a) on a crash, updates in lower hierarchical levels can be backed out and reconciled if possible at recovery time, without requiring logs; and (b) because updates in higher levels tend to be more rare (for example, it is less frequent to add directories to top levels in a file system than to add files and directories at lower levels), it reduces the frequency (or, allows a coarser granularity) with which more sensitive data structures need to be updated among parallel actors.
A naïve approach for reflecting such ordered updates into persistence is to perform such persistent updates with synchronous disk writes (including to networked storage). The more agile approach is to identify the partial orderings among all disk writes (e.g., strings) and then writing out the strings in any order (while enforcing sequential updates within a string). This can be done by detecting sequentially circular dependencies and breaking them by writing out the string before adding to it a new update to an object that is already in the string as a predecessor.
In a localized case of a single machine or a local area file system or object system, it is not difficult to identify such circular dependencies, but it is difficult to do so in a wide area storage system. This is where the barrier services can help. The BSM can receive such string updates and only when they intersect at a particular point, apply the first received string first and then apply the second received string.
Another barrier coordination operation is to handle singleton-to-batch transformations. The use of computation pooling and function handoffs to best suitable and best available resources (including handoffs to specialized accelerators) is described above. In a similar way, configurable pooling of operations can be used to schedule operations that are estimated to take a small amount of time collectively as a batch, for departers to be aggregated dynamically for latency constrained batching (singletons to batch transformation).
Another barrier coordination operation is to handle security boundaries. Barrier synchronization metadata (BSMD) may be used to describe security properties and intents. The metadata is bound to the WL as part of the authoring and deployment of the WL. When a source WL passes operations or data across a security boundary to a destination WL, the BSM may be used to ensure that security properties and intents used on the source WL are enforced on the data or operations in the destination WL. Resources of underlying workload nodes are also orchestrated to ensure that the security intents and properties are maintained in the destination node, in the case where the destination node is different than the source node.
The sidecar 3706A-N is used as a proxy to the barrier synchronization service provided by a BSM 3708. The sidecar 3706A-N shares resources with the main container 3704A-N within the respective pod 3702A-N, as a result the pod 3702A-N is able to access the synchronization service 3708 from any given application container (e.g., main container 3704A-N). So, even though the barrier synchronization service 3708 is typically on a remote node, the service appears to the main container 3704A-N as a local service. It also means that any waiting that a local application needs to do, whether as arriver or as a departer, can be done in a simple manner. The application just waits by blocking for an event or an event-with-a-timeout locally (and this can be translated into efficient waiting primitives such as UMWAIT). The sidecars 3706A-N act as proxies and may implement various optimizations such as aggregating the barrier synchronizations on behalf of all tasks local to a machine, and invoking any local collective action (e.g., the two-stage commit depicted in Model C 3600C) on behalf of all local tasks. Effectively sidecars 3706A-N both abstract the actions of a barrier while also serving to pool the network interactions with a barrier synchronization service.
The barrier service 3902 can now receive and respond to barrier protocol requests in reference to this barrier handle 3906. Tasks, both static (e.g., launched at start time) or arise dynamically in the course of an application, or a usage of the application, need to know about the barrier events handles 3906. Further, a task may need to employ different barrier handles 3906 at different points of its operation (for example, a task that requests some status from a group of other tasks when they reach a particular barrier may use one barrier handle, while the same task may later need a second group of tasks to return signed confirmations that a contractual action was completed and logged, and for that it may need to use a second barrier handle).
In operation 4100, an application controller creates a barrier descriptor record of a barrier. In operation 4102, the application controller registers the barrier descriptor record with a barrier service. In operation 4104, the barrier service responds by returning a barrier handle. The barrier handle is used to reference the barrier.
In operation 4110, a barrier call from a task is transformed into a barrier primitive. For instance, a language compiler, runtime, or barrier library may be used to transform a barrier call in a first programming language to a barrier primitive. In operation 4112, when a task invokes it programmed barrier primitive for the first time, the primitive initiates a request to the application controller for a barrier handle. In operation 4114, the application controller returns the barrier handle that corresponds to the event ID of the requesting task (its sidecar). The barrier handle represents the session or connection to the barrier provider. The event ID is a transaction identifier that represents a specific context of attributes that are navigating the barrier.
At operation 4120, after obtaining the barrier handle, the sidecar for the task is able to transform the invocations of the barrier primitive from the task to a call (synchronous or asynchronous) into the barrier service, and pass it both the barrier handle and barrier primitive arguments.
Typically, rendezvous points in a task's sequence of operations, instructions, etc. occur in the form of synchronization primitives like Event-Wait( . . . parameters . . . ), Semaphore-Wait ( . . . parameters . . . ), Condition-Wait ( . . . parameters . . . ), and so on. So, operation 4110 involves a local transformation for a task (usually by overloading or preempting such invocations with a stub) to forward the invocation of a barrier primitive over to a sidecar process or a sidecar linked library.
When a sidecar receives a barrier primitive, it checks to see if it already has previously requested and cached the barrier handle that corresponds to that barrier primitive. If not, then it is the first time that a particular barrier primitive is encountered in the task and the sidecar generates a request to the application controller and receives a barrier handle (operation 4112). At this point, if the application controller/cohort has no barrier handle established for this event, it performs operations 4100, 4102, and 4104 to instantiate a barrier handle. This dynamic setup may be needed in various lazy-computing usages for barriers that are very infrequently encountered.
Having a barrier handle to use, the sidecar is able to complete the rendezvous barrier operation on behalf of its task by contacting the barrier service with the barrier handle and the various invocation specific parameters (like timeout, any data to be supplied or retrieved at the rendezvous operation, etc.). The parameters are stored, checked, or used in a process to perform one or more barrier operations. The barrier operations are used to update state, store or commit data changes, create new records, disqualify old records, perform data transformations, or the like. After the barrier operations for the barrier are complete, the departers are released.
Turning to security, barriers may also be used to enforce security intents and other properties. Locking or barrier synchronization semantics may be expressed in metadata. The metadata may be used for computational flow graphs. A SAT gateway enables multiple WLs to interact reliably. However, there are many possible security and reliability options available to a SAT gateway. The barrier synchronization metadata (BSMD) is used to capture the barrier traversal semantics in a security ‘intent’ script.
BSMD is used to inform distributed topology design when distributing a WL across multiple nodes that includes crossing a security boundary. A barrier synchronization modeling tool may be augmented with security boundary instrumentation and metadata. The metadata describes the security properties and intents that are needed to safely breech the barrier. These metadata are bound to the WL as part of the authoring process. The metadata is used to construct the barrier interface and protocol that is appropriate to achieve the requirements for traversing the security boundary.
Once nodes across different security boundaries can connect and interact through barriers, they need to synchronize data (assets) based on the context for their interaction. For example, the context may include a transaction that needs to transfer an information asset held by one network to a second network. A secure asset transfer protocol is needed to achieve asset transfer with ACID properties. Further, barrier synchronization facilitates the decomposition of WL for elastic compute where a WL may be decomposed into multiple distributed parts, but there are constraints due to barrier policies that limit the choices for possible hosting. The authoring tool may use security intents as inputs into BSMD to create a workload topology, and then output the topology as a Directed Acyclic Graph (DAG).
The authoring interface 4200 includes a Security Boundary A 4202A and a Security Boundary B 4202B, each of which include one or more workload modules 4204A-D. Workload modules 4204A-D may be implemented using pods, containers, or the like.
A ‘security boundary’ abstraction allows a set of design objects to be grouped together in a common security boundary 4202A-B. The security boundary 4202A-B can be instrumented such that the security properties can be associated with the security boundary context and all workload modules 4204A-D inside the security group area receive the same security properties.
When the WL design is rendered as a WL, the security properties are applied to workload nodes. Particularly, the security boundary may involve inclusion of security sub-routines, functions, workflows, or sub-workloads that enforce the security boundary. For example, if a workload node is dragged-and-dropped from outside the security boundary to inside the security boundary, an onboarding sub-workflow is automatically applied such that the ingressing node becomes subject to attestation, authentication, audit, and/or authorization challenges. Ingress may also involve provisioning or assignment to a temporary isolation context where further security assessment and configuration can be performed prior to admission into the intended WL security context. The design tool may automatically assign interior WL nodes to perform onboarding, provisioning and other enforcement actions or the design tool may link to a predefined onboarding workflow, sub-WL, or functions.
Egress from the inside of a security boundary 4202A-B to outside the security boundary 4202A-B may have similar security constraints applied that remove secrets, keys, configuration, privileges and so forth that were made conditional for membership inside the security boundary 4202A-B.
Interaction between a workflow node within a security boundary A 4202A may be represented in the authoring interface 4200 as a line (e.g., edge) drawn from workload module node B 4204B in security boundary A 4202A to a workload module node D 4204D in security boundary B 4204B. Nodes B and D may not transit to/from security boundaries but may exchange data between security boundaries. The exchange of information may fall under restrictions established by security policies of security boundaries A and B 4202A-B. The security specific properties of the B-to-D interaction may be supplied into the tool by associating said security properties with the edge connecting nodes B and D.
Information design principles may use “property graphs” as a term of art meaning a graph consisting of nodes and edges may have properties associated with either nodes or edges. The authoring interface 4200 applies property graph design principles to represent security constructs in a “drag-and-drop” property graph abstraction. The authoring interface 4200 translates user input into workload functions, data, and orchestration operations such that the A-to-B interaction is instantiated in a workload by applying a secure challenge/response protocol (e.g., TLS, HTTPS, SPDM, IPSEC, CoAPS) that requires either workload module A or workload module B to present authentication, authorization, attestation, or other credentials as a pre-requisite to WL information exchange. This may be automatically applied to the WL definition or may be applied using a previously designed secure challenge/response workflow subroutine.
A barrier descriptor record may be configured based on security intents. A BSM is then able to enforce security, trust, resiliency, and other policies that arrivers are bound by on data or operations that are provided to or executed by departers.
At 4304, the method 4300 includes receiving a set of barrier messages from a first set of tasks executing on at least one of a plurality of compute nodes in the system, the respective set of tasks operating as a part of a distributed workload. In an embodiment, the first set of tasks and the second set of tasks include microservices.
At 4304, the method 4300 includes evaluating the set of barrier messages to determine whether a barrier synchronization condition is satisfied. In an embodiment, the barrier synchronization condition includes a condition that at least one of the first set of tasks has sent a barrier message indicating that they have reached a rendezvous point.
At 4306, the method 4300 includes initiating execution of a second set of tasks executing on at least one of the plurality of compute nodes in the system in response to determining that the barrier synchronization condition is satisfied.
In an embodiment, evaluating the set of barrier messages includes determining that all of the first set of tasks have sent a respective barrier message indicating that each of the first set of tasks have reached a rendezvous point. In a further embodiment, initiating the execution of the second set of tasks includes ordering the first set of tasks, assigning the ordered first set of tasks as the second set of tasks, and initiating the second set of tasks in order. In another embodiment, initiating the execution of the second set of tasks includes committing transactions for the first set of tasks before initiating the second set of tasks.
In an embodiment, the barrier message includes reference to a digital asset to be transferred to at least one of the second set of tasks. In such an embodiment, the method 4300 includes transferring the digital asset into an escrow data store managed by a barrier synchronization manager executing on the computing device and transferring the digital asset to the at least one of the second set of tasks from the escrow data store.
In an embodiment, evaluating the set of barrier messages includes determining a security intent from a barrier descriptor corresponding to the set of barrier messages and confirming that a configuration of the second set of tasks conforms to the security intent. In a related embodiment, the first set of tasks and the second set of tasks operate within different security boundaries.
In an embodiment, the method 4300 includes receiving, from a requester, a barrier registration message to create a barrier at a barrier synchronization manager. The barrier synchronization manager returns a barrier handle upon success registration of the barrier. The method 4300 includes transmitting the barrier handle to the requester, for reference to the barrier by the requester when interfacing with the barrier synchronization manager.
In an embodiment, the barrier registration message includes a reference to an arriver side handler, the arriver side handler configured to process barrier messages from the first set of tasks; a reference to a departer side handler, the departer side handler configured to initiate the second set of tasks; and a barrier type to configure the barrier synchronization condition. The handlers for the arriver side and departer side may include pointers or other structures to communicate with an arriver or departer task, which may be in a wait state. The barrier type may refer to the various barrier coordination operations described above, including a multi-stage provisional commit barrier, a data or operation handoff barrier, a resource transfer barrier, a max-lag control barrier, a wavefront control barrier, a state consistency barrier, a reordering barrier, a secure asset transfer barrier, an M-N barrier, or a 1-1 (one-to-one) barrier.
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 system, comprising: a plurality of compute nodes; and a computing device configured to: receive a set of barrier messages from a first set of tasks executing on at least one of the plurality of compute nodes in the system, the respective set of tasks operating as a part of a distributed workload; evaluate the set of barrier messages to determine whether a barrier synchronization condition is satisfied; and initiate execution of a second set of tasks executing on at least one of the plurality of compute nodes in the system in response to determining that the barrier synchronization condition is satisfied.
In Example 2, the subject matter of Example 1 includes, wherein to evaluate the set of barrier messages includes operations to determine that all of the first set of tasks have sent a respective barrier message indicating that each of the first set of tasks have reached a rendezvous point.
In Example 3, the subject matter of Example 2 includes, wherein to initiate the execution of the second set of tasks includes operations to: order the first set of tasks; assign the ordered first set of tasks as the second set of tasks; and initiate the second set of tasks in order.
In Example 4, the subject matter of Example 3 includes, wherein to initiate the execution of the second set of tasks includes operations to commit transactions for the first set of tasks before initiating the second set of tasks.
In Example 5, the subject matter of Examples 1-4 includes, wherein the barrier synchronization condition includes a condition that at least one of the first set of tasks has sent a barrier message indicating that they have reached a rendezvous point.
In Example 6, the subject matter of Examples 1-5 includes, wherein the barrier message includes reference to a digital asset to be transferred to at least one of the second set of tasks, and wherein the computing device is configured to: transfer the digital asset into an escrow data store managed by a barrier synchronization manager executing on the computing device; and transfer the digital asset to the at least one of the second set of tasks from the escrow data store.
In Example 7, the subject matter of Examples 1-6 includes, wherein to evaluate the set of barrier messages includes operations to: determine a security intent from a barrier descriptor corresponding to the set of barrier messages; and confirm that a configuration of the second set of tasks conforms to the security intent.
In Example 8, the subject matter of Example 7 includes, wherein the first set of tasks and the second set of tasks operate within different security boundaries.
In Example 9, the subject matter of Examples 1-8 includes, wherein the computing device is configured to: receive, from a requester, a barrier registration message to create a barrier at a barrier synchronization manager, wherein the barrier synchronization manager returns a barrier handle upon success registration of the barrier; and transmit the barrier handle to the requester, for reference to the barrier by the requester when interfacing with the barrier synchronization manager.
In Example 10, the subject matter of Example 9 includes, wherein the barrier registration message includes: a reference to an arriver side handler, the arriver side handler configured to process barrier messages from the first set of tasks; a reference to a departer side handler, the departer side handler configured to initiate the second set of tasks; and a barrier type to configure the barrier synchronization condition.
In Example 11, the subject matter of Examples 1-10 includes, wherein the first set of tasks and the second set of tasks include microservices.
Example 12 is a method performed by a computing device in a system, comprising: receiving a set of barrier messages from a first set of tasks executing on at least one of a plurality of compute nodes in the system, the respective set of tasks operating as a part of a distributed workload; evaluating the set of barrier messages to determine whether a barrier synchronization condition is satisfied; and initiating execution of a second set of tasks executing on at least one of the plurality of compute nodes in the system in response to determining that the barrier synchronization condition is satisfied.
In Example 13, the subject matter of Example 12 includes, wherein evaluating the set of barrier messages includes determining that all of the first set of tasks have sent a respective barrier message indicating that each of the first set of tasks have reached a rendezvous point.
In Example 14, the subject matter of Example 13 includes, wherein initiating the execution of the second set of tasks includes: ordering the first set of tasks; assigning the ordered first set of tasks as the second set of tasks; and initiating the second set of tasks in order.
In Example 15, the subject matter of Example 14 includes, wherein initiating the execution of the second set of tasks includes committing transactions for the first set of tasks before initiating the second set of tasks.
In Example 16, the subject matter of Examples 12-15 includes, wherein the barrier synchronization condition includes a condition that at least one of the first set of tasks has sent a barrier message indicating that they have reached a rendezvous point.
In Example 17, the subject matter of Examples 12-16 includes, wherein the barrier message includes reference to a digital asset to be transferred to at least one of the second set of tasks, and wherein the method comprises: transferring the digital asset into an escrow data store managed by a barrier synchronization manager executing on the computing device; and transferring the digital asset to the at least one of the second set of tasks from the escrow data store.
In Example 18, the subject matter of Examples 12-17 includes, wherein evaluating the set of barrier messages includes: determining a security intent from a barrier descriptor corresponding to the set of barrier messages; and confirming that a configuration of the second set of tasks conforms to the security intent.
In Example 19, the subject matter of Example 18 includes, wherein the first set of tasks and the second set of tasks operate within different security boundaries.
In Example 20, the subject matter of Examples 12-19 includes, wherein the method comprises: receiving, from a requester, a barrier registration message to create a barrier at a barrier synchronization manager, wherein the barrier synchronization manager returns a barrier handle upon success registration of the barrier; and transmitting the barrier handle to the requester, for reference to the barrier by the requester when interfacing with the barrier synchronization manager.
In Example 21, the subject matter of Example 20 includes, wherein the barrier registration message includes: a reference to an arriver side handler, the arriver side handler configured to process barrier messages from the first set of tasks; a reference to a departer side handler, the departer side handler configured to initiate the second set of tasks; and a barrier type to configure the barrier synchronization condition.
In Example 22, the subject matter of Examples 12-21 includes, wherein the first set of tasks and the second set of tasks include microservices.
Example 23 is at least one non-transitory machine-readable medium including instructions, which when executed by a computing device in a system, cause the computing device to perform operations comprising: receiving a set of barrier messages from a first set of tasks executing on at least one of a plurality of compute nodes in the system, the respective set of tasks operating as a part of a distributed workload; evaluating the set of barrier messages to determine whether a barrier synchronization condition is satisfied; and initiating execution of a second set of tasks executing on at least one of the plurality of compute nodes in the system in response to determining that the barrier synchronization condition is satisfied.
In Example 24, the subject matter of Example 23 includes, wherein evaluating the set of barrier messages includes determining that all of the first set of tasks have sent a respective barrier message indicating that each of the first set of tasks have reached a rendezvous point.
In Example 25, the subject matter of Example 24 includes, wherein initiating the execution of the second set of tasks includes: ordering the first set of tasks; assigning the ordered first set of tasks as the second set of tasks; and initiating the second set of tasks in order.
In Example 26, the subject matter of Example 25 includes, wherein initiating the execution of the second set of tasks includes committing transactions for the first set of tasks before initiating the second set of tasks.
In Example 27, the subject matter of Examples 23-26 includes, wherein the barrier synchronization condition includes a condition that at least one of the first set of tasks has sent a barrier message indicating that they have reached a rendezvous point.
In Example 28, the subject matter of Examples 23-27 includes, wherein the barrier message includes reference to a digital asset to be transferred to at least one of the second set of tasks, and wherein the instructions cause the computing device to perform operations comprising: transferring the digital asset into an escrow data store managed by a barrier synchronization manager executing on the computing device; and transferring the digital asset to the at least one of the second set of tasks from the escrow data store.
In Example 29, the subject matter of Examples 23-28 includes, wherein evaluating the set of barrier messages includes: determining a security intent from a barrier descriptor corresponding to the set of barrier messages; and confirming that a configuration of the second set of tasks conforms to the security intent.
In Example 30, the subject matter of Example 29 includes, wherein the first set of tasks and the second set of tasks operate within different security boundaries.
In Example 31, the subject matter of Examples 23-30 includes, wherein the instructions cause the computing device to perform operations comprising: receiving, from a requester, a barrier registration message to create a barrier at a barrier synchronization manager, wherein the barrier synchronization manager returns a barrier handle upon success registration of the barrier; and transmitting the barrier handle to the requester, for reference to the barrier by the requester when interfacing with the barrier synchronization manager.
In Example 32, the subject matter of Example 31 includes, wherein the barrier registration message includes: a reference to an arriver side handler, the arriver side handler configured to process barrier messages from the first set of tasks; a reference to a departer side handler, the departer side handler configured to initiate the second set of tasks; and a barrier type to configure the barrier synchronization condition.
In Example 33, the subject matter of Examples 23-32 includes, wherein the first set of tasks and the second set of tasks include microservices.
Example 34 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 1-33.
Example 35 is an apparatus comprising means to implement of any of Examples 1-33.
Example 36 is a system to implement of any of Examples 1-33.
Example 37 is a method to implement of any of Examples 1-33.
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 4510 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 4510, 4522, and 4540 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 4522, 4524 may implement the use of containers, such as with the use of a container “pod” 4526, 4528 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 4532, 4534 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 4560) 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 4560 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 4522 (e.g., operated by a first owner) and a second edge node 4524 (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 4522, 4524 may be coordinated based on edge provisioning functions 4550, while the operation of the various applications is coordinated with orchestration functions 4560.
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 4620 may communicate with one or more edge resource nodes 4640, which are illustratively embodied as compute servers, appliances or components located at or in a communication base station 4642 (e.g., a base station of a cellular network). As discussed above, a respective edge resource node 4640 includes some processing and storage capabilities, and, as such, some processing and/or storage of data for the client compute nodes 4610 may be performed on the edge resource node 4640. For example, the processing of data that is less urgent or important may be performed by the edge resource node 4640, 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 4620, edge resource node(s) 4640, core data center 4650, and network cloud 4660.
The edge resource node(s) 4640 also communicate with the core data center 4650, 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 4650 may provide a gateway to the global network cloud 4660 (e.g., the Internet) for the edge cloud 4410 operations formed by the edge resource node(s) 4640 and the edge gateway nodes 4620. Additionally, in some examples, the core data center 4650 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 4650 (e.g., processing of low urgency or importance, or high complexity). The edge gateway nodes 4620 or the edge resource nodes 4640 may offer the use of stateful applications 4632 and a geographically distributed data storage 4634 (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 4700 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 4700, such as that discussed above.
Other example groups of IoT devices may include remote weather stations 4714, local information terminals 4716, alarm systems 4718, automated teller machines 4720, alarm panels 4722, or moving vehicles, such as emergency vehicles 4724 or other vehicles 4726, among many others. These IoT devices may be in communication with other IoT devices, with servers 4704, with another IoT device or system, another edge computing or “fog” computing system, or a combination therein. The groups of IT 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 4714 or the traffic control group 4706, may be equipped to communicate with other IoT devices as well as with the network 4700. 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 4410, 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 4810, 4820, 4830, 4840, and 4850. For example, the client compute nodes 4802 are located at an endpoint layer 4810, while the edge gateway nodes 4812 are located at an edge devices layer 4820 (local level) of the edge computing system. Additionally, the edge aggregation nodes 4822 (and/or fog devices 4824, if arranged or operated with or among a fog networking configuration 4826) is located at a network access layer 4830 (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 4832 is located at a core network layer 4840 (e.g., a regional or geographically-central level), while the global network cloud 4842 is located at a cloud data center layer 4850 (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 4832 may be located within, at, or near the edge cloud 4410.
Although an illustrative number of client compute nodes 4802, edge gateway nodes 4812, edge aggregation nodes 4822, core data centers 4832, and global network clouds 4842 are shown in
Consistent with the examples provided herein, a client compute node 4802 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 4800 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 4800 refer to individual entities, nodes, or subsystems which include discrete or connected hardware or software configurations to facilitate or use the edge cloud 4410.
As such, the edge cloud 4410 is formed from network components and functional features operated by and within the edge gateway nodes 4812 and the edge aggregation nodes 4822 of layers 4820, 4830, respectively. The edge cloud 4410 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 4410 may form a portion of or otherwise provide an ingress point into or across a fog networking configuration 4826 (e.g., a network of fog devices 4824, 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 4824 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 4410 between the cloud data center layer 4850 and the client endpoints (e.g., client compute nodes 4802). 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 4812 and the edge aggregation nodes 4822 cooperate to provide various edge services and compute security features to the client compute nodes 4802. Furthermore, because an individual client compute node 4802 may be stationary or mobile, a respective edge gateway node 4812 may cooperate with other edge gateway devices to propagate presently provided edge services and compute security features as the corresponding client compute node 4802 moves about a region. To do so, respective nodes of the edge gateway nodes 4812 and/or edge aggregation nodes 4822 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 4900 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 4900 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 4900 includes or is embodied as a processor 4904 and a memory 4906. The processor 4904 may be embodied as any type of processor capable of performing the functions described herein (e.g., executing an application). For example, the processor 4904 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 4904 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 4904 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 4904 may work in coordination with each other to execute many types of operations and instructions within and on behalf of the compute node 4900.
The main memory 4906 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 cross-point 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 4906 may be integrated into the processor 4904. The main memory 4906 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 4902 is communicatively coupled to other components of the compute node 4900 via the I/O subsystem 4908, which may be embodied as circuitry and/or components to facilitate input/output operations with the compute circuitry 4902 (e.g., with the processor 4904 and/or the main memory 4906) and other components of the compute circuitry 4902. For example, the I/O subsystem 4908 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 4908 may form a portion of a system-on-a-chip (SoC) and be incorporated, along with one or more of the processor 4904, the main memory 4906, and other components of the compute circuitry 4902, into the compute circuitry 4902.
The one or more illustrative data storage devices 4910 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 4910 may include a system partition that stores data and firmware code for the data storage device 4910. A respective data storage device 4910 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 4900.
The communication circuitry 4912 may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications over a network between the compute circuitry 4902 and another compute device (e.g., an edge gateway node 4812 of the edge computing system 4800). The communication circuitry 4912 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 4912 includes a network interface controller (NIC) 4920, which may also be referred to as a host fabric interface (HFI). The NIC 4920 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 4900 to connect with another compute device (e.g., an edge gateway node 4812). In some examples, the NIC 4920 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 4920 may include a local processor (not shown) and/or a local memory and storage (not shown) that are local to the NIC 4920. In such examples, the local processor of the NIC 4920 (which can include general-purpose accelerators or specific accelerators) may be capable of performing one or more of the functions of the compute circuitry 4902 described herein. Additionally, or alternatively, the local memory of the NIC 4920 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 4900 may include one or more peripheral devices 4914. Such peripheral devices 4914 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 4900. In further examples, the compute node 4900 may be embodied by a respective edge compute node in an edge computing system (e.g., client compute node 4802, edge gateway node 4812, edge aggregation node 4822) or like forms of appliances, computers, subsystems, circuitry, or other components.
In a more detailed example,
The edge computing node 5050 may include processing circuitry in the form of a processor 5052, 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 5052 may be a part of a system on a chip (SoC) in which the processor 5052 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 5052 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 5052 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 5052 may communicate with a system memory 5054 over an interconnect 5056 (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 5058 may also couple to the processor 5052 via the interconnect 5056. In an example, the storage 5058 may be implemented via a solid-state disk drive (SSDD). Other devices that may be used for the storage 5058 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 5058 may be on-die memory or registers associated with the processor 5052. However, in some examples, the storage 5058 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 5058 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 5056. The interconnect 5056 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 (PCle), or any number of other technologies. The interconnect 5056 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 5056 may couple the processor 5052 to a transceiver 5066, for communications with the connected edge devices 5062. The transceiver 5066 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 5062. 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 5066 (or multiple transceivers) may communicate using multiple standards or radios for communications at a different range. For example, the edge computing node 5050 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 5062, 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 5066 (e.g., a radio transceiver) may be included to communicate with devices or services in the edge cloud 5090 via local or wide area network protocols. The wireless network transceiver 5066 may be an LPWA transceiver that follows the IEEE 802.15.4, or IEEE 802.15.4g standards, among others. The edge computing node 5050 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 5066, as described herein. For example, the transceiver 5066 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 5066 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) 5068 may be included to provide a wired communication to nodes of the edge cloud 5090 or other devices, such as the connected edge devices 5062 (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 5068 may be included to enable connecting to a second network, for example, a first NIC 5068 providing communications to the cloud over Ethernet, and a second NIC 5068 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 5064, 5066, 5068, or 5070. Accordingly, in various examples, applicable means for communicating (e.g., receiving, transmitting, etc.) may be embodied by such communications circuitry.
The edge computing node 5050 may include or be coupled to acceleration circuitry 5064, 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 5056 may couple the processor 5052 to a sensor hub or external interface 5070 that is used to connect additional devices or subsystems. The devices may include sensors 5072, 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 5070 further may be used to connect the edge computing node 5050 to actuators 5074, 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 5050. For example, a display or other output device 5084 may be included to show information, such as sensor readings or actuator position. An input device 5086, such as a touch screen or keypad may be included to accept input. An output device 5084 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 5050. 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 5076 may power the edge computing node 5050, although, in examples in which the edge computing node 5050 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 5076 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 5078 may be included in the edge computing node 5050 to track the state of charge (SoCh) of the battery 5076. The battery monitor/charger 5078 may be used to monitor other parameters of the battery 5076 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 5076. The battery monitor/charger 5078 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 5078 may communicate the information on the battery 5076 to the processor 5052 over the interconnect 5056. The battery monitor/charger 5078 may also include an analog-to-digital (ADC) converter that enables the processor 5052 to directly monitor the voltage of the battery 5076 or the current flow from the battery 5076. The battery parameters may be used to determine actions that the edge computing node 5050 may perform, such as transmission frequency, mesh network operation, sensing frequency, and the like.
A power block 5080, or other power supply coupled to a grid, may be coupled with the battery monitor/charger 5078 to charge the battery 5076. In some examples, the power block 5080 may be replaced with a wireless power receiver to obtain the power wirelessly, for example, through a loop antenna in the edge computing node 5050. 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 5078. The specific charging circuits may be selected based on the size of the battery 5076, 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 5058 may include instructions 5082 in the form of software, firmware, or hardware commands to implement the techniques described herein. Although such instructions 5082 are shown as code blocks included in the memory 5054 and the storage 5058, 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 5082 on the processor 5052 (separately, or in combination with the instructions 5082 of the machine readable medium 5060) may configure execution or operation of a trusted execution environment (TEE) 5095. In an example, the TEE 5095 operates as a protected area accessible to the processor 5052 for secure execution of instructions and secure access to data. Various implementations of the TEE 5095, and an accompanying secure area in the processor 5052 or the memory 5054 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 5050 through the TEE 5095 and the processor 5052.
In an example, the instructions 5082 provided via memory 5054, the storage 5058, or the processor 5052 may be embodied as a non-transitory, machine-readable medium 5060 including code to direct the processor 5052 to perform electronic operations in the edge computing node 5050. The processor 5052 may access the non-transitory, machine-readable medium 5060 over the interconnect 5056. For instance, the non-transitory, machine-readable medium 5060 may be embodied by devices described for the storage 5058 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 5060 may include instructions to direct the processor 5052 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 5050 can be implemented using components/modules/blocks 5052-5086 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 5062-5080. Thus, it will be understood that the node 5050 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, stand-alone executable, etc.) at a local machine, and executed by the local machine.
The block diagrams of
In the illustrated example of
In the illustrated example of
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 |
