Patent Application
20220109610
This disclosure relates generally to edge networks and, more particularly, to methods, systems, articles of manufacture and apparatus to orchestrate intermittent surplus power in edge networks.
In recent years, renewable energy sources have been used to provide power to network nodes. In some examples, network nodes are located in very remote areas that are not proximate to traditional power supplies, transmission lines and/or other power sources. Renewable energy sources include arrays of solar cells, wind turbines, and others.
In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. The figures are not to scale.
As used herein, the phrase “in communication,” including variations thereof, encompasses direct communication and/or indirect communication through one or more intermediary components, and does not require direct physical (e.g., wired) communication and/or constant communication, but rather additionally includes selective communication at periodic intervals, scheduled intervals, aperiodic intervals, and/or one-time events.
As used herein, “processor circuitry” is defined to include (i) one or more special purpose electrical circuits structured to perform specific operation(s) and including one or more semiconductor-based logic devices (e.g., electrical hardware implemented by one or more transistors), and/or (ii) one or more general purpose semiconductor-based electrical circuits programmed with instructions to perform specific operations and including one or more semiconductor-based logic devices (e.g., electrical hardware implemented by one or more transistors). Examples of processor circuitry include programmed microprocessors, Field Programmable Gate Arrays (FPGAs) that may instantiate instructions, Central Processor Units (CPUs), Graphics Processor Units (GPUs), Digital Signal Processors (DSPs), XPUs, or microcontrollers and integrated circuits such as Application Specific Integrated Circuits (ASICs). For example, an XPU may be implemented by a heterogeneous computing system including multiple types of processor circuitry (e.g., one or more FPGAs, one or more CPUs, one or more GPUs, one or more DSPs, etc., and/or a combination thereof) and application programming interface(s) (API(s)) that may assign computing task(s) to whichever one(s) of the multiple types of the processing circuitry is/are best suited to execute the computing task(s).
Compute, memory, and storage are scarce resources and generally decrease depending on the Edge location (e.g., fewer processing resources being available at consumer endpoint devices, than at a base station, than at a central office). However, the closer the Edge location is to the endpoint (e.g., user equipment (UE)), the more space and power becomes constrained. Thus, Edge computing attempts to reduce the amount of resources needed for network services, through the distribution of more resources that are located closer both geographically and in network access time. In this manner, Edge computing attempts to bring the compute resources to workload data where appropriate, or bring the workload data to the compute resources. In some examples, a workload includes, but is not limited to executable processes, such as algorithms, machine learning algorithms, image recognition algorithms, gain/loss algorithms, etc.
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 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 or ARM compute hardware architecture) implemented at base stations, gateways, network routers, or other devices that are much closer to endpoint 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. In another 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. In yet another example, central office network management hardware may be replaced with standardized 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 which the compute resource is “moved” to the data, as well as scenarios in which the data is “moved” to the compute resource. In another 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.
Examples of latency, resulting from network communication distance and processing time constraints, may range from less than a millisecond (ms) when among the endpoint layer A200, under 5 ms at the Edge devices layer A210, to between 10 to 40 ms when communicating with nodes at the network access layer A220. Beyond the Edge cloud A110 are core network A230 and cloud data center A240 layers, each with increasing latency (e.g., between 50-60 ms at the core network layer A230, to 100 ms or more at the cloud data center layer). As a result, operations at a core network data center A235 or a cloud data center A245, with latencies of at least 50 to 100 ms or more, will not be able to accomplish many time-critical functions of the use cases A205. Each of these latency values is provided for purposes of illustration and contrast; the use of other access network mediums and technologies may further reduce the latencies. In some examples, respective portions of the network may be categorized as “close Edge,” “local Edge,” “near Edge,” “middle Edge,” or “far Edge” layers, relative to a network source and destination. For instance, from the perspective of the core network data center A235 or a cloud data center A245, a central office or content data network may be considered as being located within a “near Edge” layer (“near” to the cloud, having high latency values when communicating with the devices and endpoints of the use cases A205), whereas an access point, base station, on-premise server, or network gateway may be considered as located within a “far Edge” layer (“far” from the cloud, having low latency values when communicating with the devices and endpoints of the use cases A205). Other categorizations of a particular network layer as constituting a “close,” “local,” “near,” “middle,” or “far” Edge may be based on latency, distance, number of network hops, or other measurable characteristics, as measured from a source in any of the network layers A200-A240.
The various use cases A205 may access resources under usage pressure from incoming streams due to multiple services utilizing the Edge cloud. To achieve results with low latency, the services executed within the Edge cloud A110 balance varying requirements in terms of: (a) Priority (throughput or latency) and Quality of Service (QoS) (e.g., traffic for an autonomous car may have higher priority than a temperature sensor in terms of response time requirement; or, a performance sensitivity/bottleneck may exist at a compute/accelerator, memory, storage, or network resource, depending on the application); (b) Reliability and Resiliency (e.g., some input streams need to be acted upon and the traffic routed with mission-critical reliability, where as some other input streams may tolerate an occasional failure, depending on the application); and (c) Physical constraints (e.g., power, cooling and form-factor).
The end-to-end service view for these use cases involves the concept of a service-flow and is associated with a transaction. The transaction details the overall service requirement for the entity consuming the service, as well as the associated services for the resources, workloads, workflows, and business functional and business level requirements. The services executed with the terms described may be managed at each layer in a way to assure real-time, and runtime contractual compliance for the transaction during the lifecycle of the service. When a component in the transaction is missing its agreed to service level agreement (SLA), the system as a whole (components in the transaction) may provide the ability to (1) understand the impact of the SLA violation, and (2) augment other components in the system to resume overall transaction SLA, and (3) implement steps to remediate. In some examples, an SLA is an agreement, commitment and/or contract between entities. The SLA may include parameters (e.g., latency) and corresponding values (e.g., time in milliseconds) that must be satisfied before the SLA is deemed compliant.
Thus, with these variations and service features in mind, Edge computing within the Edge cloud A110 may provide the ability to serve and respond to multiple applications of the use cases A205 (e.g., object tracking, video surveillance, connected cars, etc.) in real-time or near real-time, and meet ultra-low latency requirements for these multiple applications. These advantages enable a whole new class of applications (Virtual Network Functions (VNFs), Function as a Service (FaaS), Edge as a Service (EaaS), standard processes, etc.), which cannot leverage conventional cloud computing due to latency or other limitations.
However, the advantages of Edge computing come with caveats. The devices located at the Edge are often resource constrained and therefore there is pressure on usage of Edge resources. Typically, this is addressed through the pooling of memory and storage resources for use by multiple users (tenants) and devices. The Edge may be power and cooling constrained and therefore the power usage needs to be accounted for by the applications that are consuming the most power. There may be inherent power-performance tradeoffs in these pooled memory resources, as many of them are likely to use emerging memory technologies, where more power requires greater memory bandwidth. Likewise, improved security of hardware and root of trust functions are also required because Edge locations may be unmanned and may even need permissioned access (e.g., when housed in a third-party location). Such issues are magnified in the Edge cloud A110 in a multi-tenant, multi-owner, or multi-access setting, where services and applications are requested by many users, especially as network usage dynamically fluctuates and the composition of the multiple stakeholders, use cases, and services changes.
At a more generic level, an Edge computing system may be described to encompass any number of deployments at the previously discussed layers operating in the Edge cloud A110 (network layers A200-A240), which provide coordination from client and distributed computing devices. One or more Edge gateway nodes, one or more Edge aggregation nodes, and one or more core data centers may be distributed across layers of the network to provide an implementation of the Edge computing system by or on behalf of a telecommunication service provider (“telco,” or “TSP”), internet-of-things service provider, cloud service provider (CSP), enterprise entity, or any other number of entities. Various implementations and configurations of the Edge computing system may be provided dynamically, such as when orchestrated to meet service objectives.
Consistent with the examples provided herein, a client compute node may be embodied as any type of endpoint component, device, appliance, or other thing capable of communicating as a producer or consumer of data. Further, the label “node” or “device” as used in the Edge computing system does not necessarily mean that such node or device operates in a client or agent/minion/follower role; rather, any of the nodes or devices in the Edge computing system refer to individual entities, nodes, or subsystems that include discrete or connected hardware or software configurations to facilitate or use the Edge cloud A110.
As such, the Edge cloud A110 is formed from network components and functional features operated by and within Edge gateway nodes, Edge aggregation nodes or other Edge compute nodes among network layers A210-A230. The Edge cloud A110 may be embodied as any type of network that provides Edge computing and/or storage resources that are proximately located to radio access network (RAN) capable endpoint devices (e.g., mobile computing devices, IoT devices, smart devices, etc.), which are discussed herein. In other words, the Edge cloud A110 may be envisioned as an “Edge” that connects the endpoint devices and traditional network access points that serve as an ingress point into service provider core networks, including mobile carrier networks (e.g., Global System for Mobile Communications (GSM) networks, Long-Term Evolution (LTE) networks, 5G/6G networks, etc.), while also providing storage and/or compute capabilities. Other types and forms of network access (e.g., Wi-Fi, long-range wireless, wired networks including optical networks) may also be utilized in place of or in combination with such 3GPP carrier networks.
The network components of the Edge cloud A110 may be servers, multi-tenant servers, appliance computing devices, and/or any other type of computing devices. For example, the Edge cloud A110 may include an appliance computing device that is a self-contained electronic device including a housing, a chassis, a case or a shell. In some circumstances, the housing may be dimensioned for portability such that it can be carried by a human and/or shipped. Example housings may include materials that form one or more exterior surfaces that partially or fully protect contents of the appliance, which may include weather protection, hazardous environment protection (e.g., EMI, vibration, extreme temperatures), and/or enable submergibility. Example housings may include power circuitry to provide power for stationary and/or portable implementations, such as AC power inputs, DC power inputs, AC/DC or DC/AC converter(s), power regulators, transformers, charging circuitry, batteries, wired inputs and/or wireless power inputs. Example housings and/or surfaces thereof may include or connect to mounting hardware to enable attachment to structures such as buildings, telecommunication structures (e.g., poles, antenna structures, etc.) and/or racks (e.g., server racks, blade mounts, etc.). Example housings and/or surfaces thereof may support one or more sensors (e.g., temperature sensors, vibration sensors, light sensors, acoustic sensors, capacitive sensors, proximity sensors, etc.). One or more such sensors may be contained in, carried by, or otherwise embedded in the surface and/or mounted to the surface of the appliance. Example housings and/or surfaces thereof may support mechanical connectivity, such as propulsion hardware (e.g., wheels, propellers, etc.) and/or articulating hardware (e.g., robot arms, pivotable appendages, etc.). In some circumstances, the sensors may include any type of input devices such as user interface hardware (e.g., buttons, switches, dials, sliders, etc.). In some circumstances, example housings include output devices contained in, carried by, embedded therein and/or attached thereto. Output devices may include displays, touchscreens, lights, LEDs, speakers, I/O ports (e.g., USB), etc. In some circumstances, Edge devices are presented in the network for a specific purpose (e.g., a traffic light), but may have processing and/or other capacities that may be utilized for other purposes. Such Edge devices may be independent from other networked devices and may be provided with a housing having a form factor suitable for its primary purpose; yet be available for other compute tasks that do not interfere with its primary task. Edge devices include Internet of Things devices. The appliance computing device may include hardware and software components to manage local issues such as device temperature, vibration, resource utilization, updates, power issues, physical and network security, etc. Example hardware for implementing an appliance computing device is described in conjunction with
In
Edge nodes operate in many different types of environments. Edge node environments disclosed herein focus on circumstances where the Edge nodes obtain power from renewable resources that are not connected to typical power sources (e.g., a power grid infrastructure that supplies 120 VAC, 240 VAC, etc. from coal-fired, oil-fired, nuclear power, etc.). Edge nodes disclosed herein include at least one battery as well as connectivity to a renewable power source, such as wind (e.g., wind turbines) and/or solar. Generally speaking, renewable power sources are sometimes referred to herein as ambient power sources. The availability of power output by these sources can vary in a manner difficult to predict. For instance, while expected durations of daylight are reliably predicted when utilizing solar power, cloud cover and weather reduce solar power output capabilities in a much less predictable manner. Similarly, while wind power is not necessarily affected by sundown events, wind can be intermittently available.
Edge nodes that utilize renewable power sources charge on-board power reserves (e.g., one or more batteries) when power output from those renewable power sources is available, thereby allowing the Edge nodes to operate during circumstances where there is no sunlight (e.g., evening/night), where sunlight is occluded (e.g., storms, clouds), and/or where there is no wind. Examples disclosed herein virtualize and/or otherwise normalize power in terms of “units” as a currency for prioritizing selection of particular Edge nodes capable of accepting workload tasks from other Edge nodes. Because different Edge nodes can be heterogeneous (e.g., different on-board resources, different battery capacities, etc.), examples herein normalize node battery capacities in “power units” that are relevant to relative capabilities of co-participating nodes. Tasks/workloads performed by the various nodes in an Edge network are rated on a relative basis using “power units” that consider, in part, (a) task transactions and (b) corresponding computing cycles per transaction (specific to each node). For instance, a first example task on a first example node (e.g., a computing platform) may require a different number of computing cycles per transaction depending on a number of processors the node has, a number of cores per processor the node has, and/or the types of processing resources the node can invoke to execute the transactions (e.g., a CPU, a GPU, a DSP, etc.).
Examples disclosed herein evaluate candidate proactive task execution in view of circumstances of excess ambient power. For instance, Edge node tasks related to garbage collection or persistent memory defragmentation may have established thresholds to trigger task execution. To illustrate, a normal trigger threshold is 70% to trigger defragmentation (as a low priority process) and 90% to trigger defragmentation (as a high/urgent priority process). However, examples disclosed herein consider improved utilization of excess ambient power that cannot be stored when an on-board battery is already at 100% charge capacity. If the excess ambient power is not consumed, it is wasted. As such, examples disclosed herein apply an acceleration factor (offset) to established threshold values in response to surplus ambient power to accelerate triggering of tasks that would not otherwise occur, thereby utilizing the surplus ambient power in a productive manner. Continuing with the aforementioned example, if a current defragmentation value is only 35% (which is substantially below a threshold at which fragmentation is triggered for a low priority process), examples disclosed herein cause the defragmentation process to begin to bring the defragmentation value down to 0%, which buffers additional time for future tasks to be executed and reduces reliance upon consuming battery power in the future for that particular task. Additionally, this example scenario permits more battery power to be reserved for future planned tasks and future emergency tasks.
While the aforementioned example related to defragmentation acceleration assists proactive power management for one node, examples disclosed herein also broadcast their power surplus to other nodes to enable proactive task offloading from one node to another. For instance, a surplus node may broadcast and/or otherwise advertise a power surplus to any number of communicatively connected Edge nodes, some of which have tasks suitable for offloading to another node. Such offloaded tasks are analyzed to determine a number of power units required to satisfy those tasks, and such surplus nodes (e.g., donor nodes) are analyzed to determine a number of surplus power units available for consumption. Stated differently, examples disclosed herein enable computation offloading as a service to other Edge appliances that may have a deficit of power units and/or heavy task requirement, as disclosed in further detail below.
The example platform 102 includes resources 108, which may include, but are not limited to high-bandwidth memory 110, double data rate (DDR) memory 112 (e.g., DDR synchronous dynamic random-access memory (DDR SDRAM), central processing unit(s) (CPUs) 114 having any number of cores, and accelerators 116 (e.g., neural network accelerators, and signal processors, convolutional neural network processors, etc.). The example platform 102 also includes example adaptive power managing circuitry 118, which includes example power unit analysis circuitry 120, service level agreement (SLA) analysis circuitry 122, analysis circuitry 124, and advertising circuitry 126.
In operation, the example adaptive power managing circuitry 118 performs an inventory of its own resources 108. Generally speaking, the example platform 102 represents one Edge node in a network of any number of communicatively connected Edge nodes. The various Edge nodes may each have different configurations, structures and/or capabilities, such as CPUs 114 having different numbers of cores, more or less memory, etc. As such, the example adaptive power managing circuitry 118 performs the inventory of the example resources 108 so that future advertising of its capabilities can facilitate workload offloading of one or more other networked Edge nodes that might need help, as described in further detail below.
The example power unit analysis circuitry 120 calculates a power unit status of the platform 102 to determine its current power availability. Several conditions and/or factors, if present, may affect the current power availability of the platform 102 including, but not limited to a capacity of the example battery subsystem 106, a current workload demand of the platform 102, circumstances where the example renewable energy infrastructure 104 has experienced disruptions (e.g., cloud cover that prevents solar power harvesting, lack of wind, etc.), thereby causing an excess drain on the example battery subsystem 106, etc. The example SLA analysis circuitry 122 analyzes a current SLA status of the example platform 102 to determine a metric corresponding to the platform's ability to satisfy an agreed-upon SLA for any given workload. Metrics corresponding to the SLA status include, but are not limited to a binary satisfied/not-satisfied metric, or a percentage value (e.g., a threshold value) indicative of a relative progress of the platform 102 to satisfy the SLA obligations (e.g., 90% complete). In some circumstances, unforeseen conditions may strain an ability of the platform 102 to satisfy the SLA, thereby causing an excess drain on battery reserves. If such conditions persist for too long, the platform 102 may need to adjust one or more parameters (“knobs”) in an effort to extend capabilities of the example resources 108, even if such capabilities are diminished (e.g., lowering a processor clock cycle, lowering NIC bandwidth).
In view of the aforementioned resources 108, power unit status and SLA status, the example power unit analysis circuitry 120 determines whether the example platform 102 has a surplus amount of power. If not, the example adaptive power managing circuitry 118 calculates a number of power units that, if processed by a donor platform, would allow the example platform 102 to successfully maintain its SLA obligations, as described in further detail below. On the other hand, in the event the example adaptive power managing circuitry 118 determines that surplus power units are available, then the example adaptive power managing circuitry 118 advertises a quantity of power units that are available for one or more other Edge nodes (e.g., other network connected platforms) to use, as described in further detail below.
Returning to the scenario in which the example platform 102 has a surplus of power units (e.g., based on a combination of 100% battery capacity, full sunlight on a solar array, and an SLA metric indicative of 90% complete with SLA requirements), the example analysis circuitry 124 selects a threshold acceleration factor to apply to one or more tasks (e.g., one or more local tasks that are either currently executing (e.g., on local resources) or scheduled for future execution when one or more triggers occur). In some examples, a local task of interest is not yet executing but has a corresponding metric to identify when the task should trigger/execute. As discussed above, an example normal trigger threshold prior to invoking a defragmentation task could be a 70% defragmentation level of memory. However, when a surplus of power units is available to the example platform 102, the example threshold analysis circuitry 124 applies the acceleration factor to reduce the trigger threshold to accelerate instantiation of the (e.g., local) task before it would otherwise occur under normal circumstances. In other words, application of the acceleration factor to a first trigger threshold generates a second trigger threshold that, when satisfied by current conditions, designates the task for early execution. For example, if the threshold acceleration factor is set to 50%, then the example analysis circuitry 124 adjusts the normal trigger threshold from 70% (e.g., a first trigger threshold) to 35% (e.g., a second or otherwise accelerated trigger threshold). In some examples, speculatively executing one or more tasks in view of surplus power units occurs in a manner that consumes all such surplus power units without remaining power units to share with neighboring Edge nodes (e.g., neighboring platforms). For instance, the speculative execution may occur in the late afternoon while the sun is going down and, upon completion of the speculative tasks, the battery reserves are still full. In some examples, speculatively executing one or more tasks in view of surplus power units occurs in a manner that leaves additional surplus that is capable of being shared with participating Edge nodes.
In the event the example power unit analysis circuitry 120 determines that surplus power units remain and/or in the event the example analysis circuitry 124 determines that there are no candidate workloads/tasks (e.g., local tasks) that can be accelerated, the example advertising circuitry 126 advertises the surplus power units (e.g., to remote Edge nodes having one or more remote tasks capable of being transferred to the example platform 102). In some examples, the advertising circuitry 126 generates telemetry information corresponding to current Edge node capabilities of the local Edge node 102 that can be used by one or more remote Edge nodes communicatively connected to the Edge node 102. In some examples, the advertising circuitry 126 processes any type of telemetry information to/from the platform 102 in which the advertising information (telemetry) includes resources information (e.g., the type and quantity of processors, the type and quantity of accelerators, the type and quantity of memory, the amount of available power units, one or more process address space identifiers (PASIDs) corresponding to candidate tasks to be offloaded, etc.). Example telemetry information 130 is shown in
The example SLA analysis circuitry 122 evaluates a candidate workload (received workload, also referred to as a remote task) that is responsive to the telemetry advertisement to determine that a corresponding SLA can be satisfied by the resources of the Edge node donating the power units (e.g., the “donor Edge node”). In some examples, the SLA analysis circuitry 122 evaluates (a) the PASID corresponding to the workload, (b) information corresponding to criticality of the resources (e.g., core processor type/capability, necessary amount of memory, necessary amount of bandwidth, etc.), and/or (c) conditions to be met by the SLA (e.g., SLA parameters indicative of performance requirements, such as 100 minutes per day of task execution (e.g., garbage collection)). If the example platform 102 and/or its resources 108 are capable of satisfying the SLA parameters corresponding to the remote task, then the example SLA analysis circuitry 122 accepts the remote task for execution, thereby utilizing excess power units that would otherwise be wasted by the renewable energy infrastructure 104. The example adaptive power managing circuitry 118 allocates needed resources to process the received workload, and the SLA analysis circuitry 122 continues to monitor the platform 102 to verify that (a) SLA obligation of the platform workload(s) is satisfied and (b) SLA obligation of the received workload(s) is satisfied. In the event SLA metrics indicate that the SLA obligations cannot be met, the SLA analysis circuitry 122 invokes one or more knobs to adjust performance of the resources 108. For example, in response to decreasing and/or otherwise degradation of SLA metrics, the SLA analysis circuitry 122 increases processor frequency, activates one or more cores, increases I/O bandwidth, etc. when a requisite quantity of power units are available. However, in response to decreasing and/or otherwise degrading SLA metrics, the SLA analysis circuitry 122 may invoke the example advertising circuitry 126 to report back to the Edge node that requested assistance to inform it that the workload(s) cannot be completed in accordance with SLA requirements. When the workload(s) are complete or returned to sender, the example power unit analysis circuitry 120 re-evaluates current conditions to determine if the platform 102 is capable of again helping one or more other platforms with workload execution in view of surplus power unit conditions.
However, in the event the example power unit analysis circuitry 120 determines the platform 102 is experiencing a power deficit, it calculates a quantity of that deficit and invokes the advertising circuitry 126 to query other platforms (Edge nodes) for candidate donors. As described above, any type of telemetry information may be included in the solicitation for candidate donor platforms that can receive one or more workloads in an effort to take advantage of surplus power units that, if not used, would otherwise be wasted.
As described above,
In some examples, the example task management system 100 includes adaptive power managing circuitry 118, which includes means for adaptively managing power. The example power unit analysis circuitry 120 includes means for analyzing power units. The example SLA analysis circuitry 122 includes means for analyzing SLAs. The example analysis circuitry 124 includes means for analyzing thresholds. The example advertising circuitry 126 includes means for advertising. For example, the means for adaptively managing power may be implemented by the example adaptive power managing circuitry 118. The means for analyzing power units may be implemented by the example power unit analysis circuitry 120. The means for analyzing SLAs may be implemented by the example SLA analysis circuitry 122. The means for analyzing thresholds may be implemented by the example analysis circuitry 124. The means for advertising may be implemented by the example advertising circuitry 126. In some examples, the aforementioned circuitry may be instantiated by processor circuitry such as the example processor circuitry 512 of
While an example manner of implementing the example task management system 100 of
Flowcharts representative of example hardware logic circuitry, machine readable instructions, hardware implemented state machines, and/or any combination thereof for implementing the example adaptive power managing circuitry 118 of
The machine readable instructions described herein may be stored in one or more of a compressed format, an encrypted format, a fragmented format, a compiled format, an executable format, a packaged format, etc. Machine readable instructions as described herein may be stored as data or a data structure (e.g., as portions of instructions, code, representations of code, etc.) that may be utilized to create, manufacture, and/or produce machine executable instructions. For example, the machine readable instructions may be fragmented and stored on one or more storage devices and/or computing devices (e.g., servers) located at the same or different locations of a network or collection of networks (e.g., in the cloud, in edge devices, etc.). The machine readable instructions may require one or more of installation, modification, adaptation, updating, combining, supplementing, configuring, decryption, decompression, unpacking, distribution, reassignment, compilation, etc., in order to make them directly readable, interpretable, and/or executable by a computing device and/or other machine. For example, the machine readable instructions may be stored in multiple parts, which are individually compressed, encrypted, and/or stored on separate computing devices, wherein the parts when decrypted, decompressed, and/or combined form a set of machine executable instructions that implement one or more operations that may together form a program such as that described herein.
In another example, the machine readable instructions may be stored in a state in which they may be read by processor circuitry, but require addition of a library (e.g., a dynamic link library (DLL)), a software development kit (SDK), an application programming interface (API), etc., in order to execute the machine readable instructions on a particular computing device or other device. In another example, the machine readable instructions may need to be configured (e.g., settings stored, data input, network addresses recorded, etc.) before the machine readable instructions and/or the corresponding program(s) can be executed in whole or in part. Thus, machine readable media, as used herein, may include machine readable instructions and/or program(s) regardless of the particular format or state of the machine readable instructions and/or program(s) when stored or otherwise at rest or in transit.
The machine readable instructions described herein can be represented by any past, present, or future instruction language, scripting language, programming language, etc. For example, the machine readable instructions may be represented using any of the following languages: C, C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language (HTML), Structured Query Language (SQL), Swift, etc.
As mentioned above, the example operations of
“Including” and “comprising” (and all forms and tenses thereof) are used herein to be open ended terms. Thus, whenever a claim employs any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, having, etc.) as a preamble or within a claim recitation of any kind, it is to be understood that additional elements, terms, etc., may be present without falling outside the scope of the corresponding claim or recitation. As used herein, when the phrase “at least” is used as the transition term in, for example, a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. The term “and/or” when used, for example, in a form such as A, B, and/or C refers to any combination or subset of A, B, C such as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) B with C, or (7) A with B and with C. As used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. As used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B.
As used herein, singular references (e.g., “a”, “an”, “first”, “second”, etc.) do not exclude a plurality. The term “a” or “an” object, as used herein, refers to one or more of that object. The terms “a” (or “an”), “one or more”, and “at least one” are used interchangeably herein. Furthermore, although individually listed, a plurality of means, elements or method actions may be implemented by, e.g., the same entity or object. Additionally, although individual features may be included in different examples or claims, these may possibly be combined, and the inclusion in different examples or claims does not imply that a combination of features is not feasible and/or advantageous.
While the example battery subsystem 106, renewable energy infrastructure 104 and/or the energy draw by the platform 102 may be in any type of energy measurement unit, the example power unit analysis circuitry 120 converts such disparate energy measurement units into a normalized metric of “power unit” to allow relative comparisons between the platforms (e.g., Edge nodes) participating in any network. As such, in the event a first Edge node identifies a surplus of 100 power units, a second Edge node can calculate a corresponding power deficiency using the same metric of power units to determine which one or more donor nodes are capable of assisting the second Edge node.
The example SLA analysis circuitry 122 analyzes a current SLA status of the example platform 102 to determine a metric corresponding to the platform's ability to satisfy an agreed-upon SLA for any given workload (block 206). In particular, based on (a) the current power unit status and (b) whether SLA obligations are being satisfied, the example power unit analysis circuitry 120 determines whether a surplus of power units exists (block 208). In the event of a power surplus, the example adaptive power managing circuitry 118 processes the power surplus to determine how to best utilize the surplus power units that, if not used, would otherwise be wasted (block 210), as described above and in further detail below. On the other hand, in the event of a power unit deficit (e.g., the workload is using a greater quantity of power units than the renewable energy infrastructure 104 and the battery subsystem 106 is expected to handle to satisfy the SLA requirements), then the example adaptive power managing circuitry 118 processes the power deficiency to seek donor Edge nodes to handle workload tasks (block 212).
In response to the example analysis circuitry 124 applying the acceleration factor to all tasks that have a corresponding trigger threshold value (block 302), the analysis circuitry 124 determines whether there are one or more candidate tasks to accelerate (e.g., one or more tasks to cause to be executed despite their normal threshold trigger value has not yet been satisfied) (block 304). For example, if a first task has a trigger threshold of 70% (e.g., 70% defragmentation must be detected prior to the trigger occurring), and if the acceleration factor is 0.20, and the current defragmentation value (e.g., of memory) is at 30%, then the applied acceleration factor will have no effect on causing the defragmentation task to trigger (i.e., applying the factor of 0.2 to 70% brings the new trigger threshold down to 56%, which is still not “tripped” by the current defragmentation value of 30%). In such a circumstance, control of the example program 210 advances to block 312 to advertise the surplus, as described above and in further detail below.
In response to identifying tasks that become triggered in response to application of the acceleration factor (block 304), the example adaptive power managing circuitry 118 executes the identified tasks/workload(s) (block 306). To ensure that the accelerated task execution has not resulted in a circumstance where the platform 102 no longer has a surplus of power units (e.g., they were all consumed in response to accelerating tasks corresponding to the platform 102), the example power unit analysis circuitry 120 determines whether any surplus power units remain (block 308). If so, the example advertising circuitry 126 advertises the surplus to any number of other Edge nodes that are communicatively connected thereto (block 312). If the example advertising circuitry 126 identifies at least one consumer that responds to the advertised telemetry data corresponding to surplus power units (block 314) (e.g., a candidate workload is received by one or more other Edge nodes), the example SLA analysis circuitry 122 matches the received workload/task to appropriate resources 108 (block 316). As described above, matching the workload to resources may be aided by example telemetry data, such as the example telemetry information 130 shown in the illustrated example of
The example adaptive power managing circuitry 118 processes the received workloads/tasks (block 318) provided by the one or more other Edge nodes that responded to the advertisement of surplus power units, and the example SLA analysis circuitry 122 verifies that SLA requirements of the example platform 102 that is donating power units and the SLA requirements corresponding to the received workloads are satisfied (block 320). As described above, while the example platform 102 may have surplus power units at a first time and accept any requests to assist processing workload(s) from other Edge nodes, dynamic conditions may occur that reduce the ability of the platform 102 to satisfy SLA requirements. In the event SLA degradation is detected by the example SLA analysis circuitry 122 (block 320), it applies one or more adjustments to preserve at least one of (a) SLA requirement satisfaction (e.g., increasing a quantity and/or type of resources 108 to be applied to the tasks, increasing a frequency of processors executing the tasks), or (b) an ability for the platform 102 to continue operation without sacrificing its own battery reserves (block 322). If the adaptive power managing circuitry 118 determines that the workload is not yet complete (block 324), then control returns to block 320 to verify SLA conditions, otherwise control returns to block 204 of
Returning to the illustrated example of
The processor platform 500 of the illustrated example includes processor circuitry 512. The processor circuitry 512 of the illustrated example is hardware. For example, the processor circuitry 512 can be implemented by one or more integrated circuits, logic circuits, FPGAs, microprocessors, CPUs, GPUs, DSPs, and/or microcontrollers from any desired family or manufacturer. The processor circuitry 512 may be implemented by one or more semiconductor based (e.g., silicon based) devices. In this example, the processor circuitry 512 implements the example power unit analysis circuitry 120, the example SLA analysis circuitry 122, the example analysis circuitry 124, the example advertising circuitry 126 and/or the example adaptive power managing circuitry 118.
The processor circuitry 512 of the illustrated example includes a local memory 513 (e.g., a cache, registers, etc.). The processor circuitry 512 of the illustrated example is in communication with a main memory including a volatile memory 514 and a non-volatile memory 516 by a bus 518. The volatile memory 514 may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory (RDRAM®), and/or any other type of RAM device. The non-volatile memory 516 may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory 514, 516 of the illustrated example is controlled by a memory controller 517.
The processor platform 500 of the illustrated example also includes interface circuitry 520. The interface circuitry 520 may be implemented by hardware in accordance with any type of interface standard, such as an Ethernet interface, a universal serial bus (USB) interface, a Bluetooth® interface, a near field communication (NFC) interface, a Peripheral Component Interconnect (PCI) interface, and/or a Peripheral Component Interconnect Express (PCIe) interface.
In the illustrated example, one or more input devices 522 are connected to the interface circuitry 520. The input device(s) 522 permit(s) a user to enter data and/or commands into the processor circuitry 512. The input device(s) 522 can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, an isopoint device, and/or a voice recognition system.
One or more output devices 524 are also connected to the interface circuitry 520 of the illustrated example. The output device(s) 524 can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube (CRT) display, an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer, and/or speaker. The interface circuitry 520 of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip, and/or graphics processor circuitry such as a GPU.
The interface circuitry 520 of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) by a network 526. The communication can be by, for example, an Ethernet connection, a digital subscriber line (DSL) connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, an optical connection, etc.
The processor platform 500 of the illustrated example also includes one or more mass storage devices 528 to store software and/or data. Examples of such mass storage devices 528 include magnetic storage devices, optical storage devices, floppy disk drives, HDDs, CDs, Blu-ray disk drives, redundant array of independent disks (RAID) systems, solid state storage devices such as flash memory devices and/or SSDs, and DVD drives.
The machine executable instructions 532, which may be implemented by the machine readable instructions of
The cores 602 may communicate by a first example bus 604. In some examples, the first bus 604 may implement a communication bus to effectuate communication associated with one(s) of the cores 602. For example, the first bus 604 may implement at least one of an Inter-Integrated Circuit (I2C) bus, a Serial Peripheral Interface (SPI) bus, a PCI bus, or a PCIe bus. Additionally or alternatively, the first bus 604 may implement any other type of computing or electrical bus. The cores 602 may obtain data, instructions, and/or signals from one or more external devices by example interface circuitry 606. The cores 602 may output data, instructions, and/or signals to the one or more external devices by the interface circuitry 606. Although the cores 602 of this example include example local memory 620 (e.g., Level 1 (L1) cache that may be split into an L1 data cache and an L1 instruction cache), the microprocessor 600 also includes example shared memory 610 that may be shared by the cores (e.g., Level 2 (L2_cache)) for high-speed access to data and/or instructions. Data and/or instructions may be transferred (e.g., shared) by writing to and/or reading from the shared memory 610. The local memory 620 of each of the cores 602 and the shared memory 610 may be part of a hierarchy of storage devices including multiple levels of cache memory and the main memory (e.g., the main memory 514, 516 of
Each core 602 may be referred to as a CPU, DSP, GPU, etc., or any other type of hardware circuitry. Each core 602 includes control unit circuitry 614, arithmetic and logic (AL) circuitry (sometimes referred to as an ALU) 616, a plurality of registers 618, the L1 cache 620, and a second example bus 622. Other structures may be present. For example, each core 602 may include vector unit circuitry, single instruction multiple data (SIMD) unit circuitry, load/store unit (LSU) circuitry, branch/jump unit circuitry, floating-point unit (FPU) circuitry, etc. The control unit circuitry 614 includes semiconductor-based circuits structured to control (e.g., coordinate) data movement within the corresponding core 602. The AL circuitry 616 includes semiconductor-based circuits structured to perform one or more mathematic and/or logic operations on the data within the corresponding core 602. The AL circuitry 616 of some examples performs integer based operations. In other examples, the AL circuitry 616 also performs floating point operations. In yet other examples, the AL circuitry 616 may include first AL circuitry that performs integer based operations and second AL circuitry that performs floating point operations. In some examples, the AL circuitry 616 may be referred to as an Arithmetic Logic Unit (ALU). The registers 618 are semiconductor-based structures to store data and/or instructions such as results of one or more of the operations performed by the AL circuitry 616 of the corresponding core 602. For example, the registers 618 may include vector register(s), SIMD register(s), general purpose register(s), flag register(s), segment register(s), machine specific register(s), instruction pointer register(s), control register(s), debug register(s), memory management register(s), machine check register(s), etc. The registers 618 may be arranged in a bank as shown in
Each core 602 and/or, more generally, the microprocessor 600 may include additional and/or alternate structures to those shown and described above. For example, one or more clock circuits, one or more power supplies, one or more power gates, one or more cache home agents (CHAs), one or more converged/common mesh stops (CMSs), one or more shifters (e.g., barrel shifter(s)) and/or other circuitry may be present. The microprocessor 600 is a semiconductor device fabricated to include many transistors interconnected to implement the structures described above in one or more integrated circuits (ICs) contained in one or more packages. The processor circuitry may include and/or cooperate with one or more accelerators. In some examples, accelerators are implemented by logic circuitry to perform certain tasks more quickly and/or efficiently than can be done by a general purpose processor. Examples of accelerators include ASICs and FPGAs such as those discussed herein. A GPU or other programmable device can also be an accelerator. Accelerators may be on-board the processor circuitry, in the same chip package as the processor circuitry and/or in one or more separate packages from the processor circuitry.
More specifically, in contrast to the microprocessor 600 of
In the example of
The interconnections 710 of the illustrated example are conductive pathways, traces, vias, or the like that may include electrically controllable switches (e.g., transistors) whose state can be changed by programming (e.g., using an HDL instruction language) to activate or deactivate one or more connections between one or more of the logic gate circuitry 708 to program desired logic circuits.
The storage circuitry 712 of the illustrated example is structured to store result(s) of the one or more of the operations performed by corresponding logic gates. The storage circuitry 712 may be implemented by registers or the like. In the illustrated example, the storage circuitry 712 is distributed amongst the logic gate circuitry 708 to facilitate access and increase execution speed.
The example FPGA circuitry 700 of
Although
In some examples, the processor circuitry 512 of
A block diagram illustrating an example software distribution platform 805 to distribute software such as the example machine readable instructions 532 of
From the foregoing, it will be appreciated that example systems, methods, apparatus, and articles of manufacture have been disclosed that orchestrate and/or otherwise manage circumstances where Edge network nodes (e.g., platforms) have intermittent surplus power. Disclosed systems, methods, apparatus, and articles of manufacture improve the efficiency of Edge networks by optimizing circumstances where excess power is utilized rather than wasted, particularly in Edge networks that rely on renewable energy sources like solar and wind. Edge nodes normally include any number of tasks that require particular trigger thresholds prior to execution, but examples disclosed herein accelerate such triggers when surplus power is available. In effect, utilization of surplus power to execute tasks earlier than usual results in bolstering an ability for Edge network nodes to be ready for unforeseen emergency task execution where limited on-board battery storage can be maintained in a relatively higher state.
Example methods, apparatus, systems, and articles of manufacture to orchestrate intermittent surplus power in Edge networks are disclosed herein. Further examples and combinations thereof include the following:
Example 1 includes an apparatus to manage surplus power comprising power unit analysis circuitry to identify a power surplus, threshold analysis circuitry to apply an acceleration factor to a first trigger threshold of a local task, the acceleration factor to set a second trigger threshold, and designate the local task for early execution when a current metric corresponding to the local task satisfies the second trigger threshold, and adaptive power managing circuitry to execute the local task in response to detecting the designation for early execution.
Example 2 includes the apparatus as defined in example 1, further including advertising circuitry to advertise the power surplus to remote nodes.
Example 3 includes the apparatus as defined in example 2, wherein the advertising circuitry is to generate telemetry information corresponding to available resources.
Example 4 includes the apparatus as defined in example 3, wherein the telemetry information includes at least one of a type of available processor, a quantity of processor cores, a type of available memory, a quantity of memory, or a type of accelerator.
Example 5 includes the apparatus as defined in example 2, wherein the advertising circuitry is to generate telemetry information corresponding to a quantity associated with the power surplus.
Example 6 includes the apparatus as defined in example 1, further including service level agreement (SLA) analysis circuitry to determine SLA parameters associated with a remote task.
Example 7 includes the apparatus as defined in example 6, wherein the SLA analysis circuitry is to accept the remote task for local execution if local resources satisfy the SLA parameters.
Example 8 includes an apparatus to control surplus power allocation comprising interface circuitry to facilitate network communication, and processor circuitry including one or more of at least one of a central processing unit, a graphic processing unit, or a digital signal processor, the at least one of the central processing unit, the graphic processing unit, or the digital signal processor having control circuitry to control data movement within the processor circuitry, arithmetic and logic circuitry to perform one or more first operations corresponding to instructions, and one or more registers to store a result of the one or more first operations, the instructions in the apparatus, a Field Programmable Gate Array (FPGA), the FPGA including logic gate circuitry, a plurality of configurable interconnections, and storage circuitry, the logic gate circuitry and interconnections to perform one or more second operations, the storage circuitry to store a result of the one or more second operations, or Application Specific Integrate Circuitry (ASIC) including logic gate circuitry to perform one or more third operations, the processor circuitry to perform at least one of the first operations, the second operations, or the third operations to instantiate power unit analysis circuitry to identify a power surplus, analysis circuitry to apply an acceleration factor to a first trigger threshold of a local task, the acceleration factor to set a second trigger threshold, and designate the local task for early execution when a current metric corresponding to the local task satisfies the second trigger threshold, and adaptive power managing circuitry to execute the local task in response to detecting the designation for early execution.
Example 9 includes the apparatus as defined in example 8, further including advertising circuitry to advertise the power surplus to remote nodes.
Example 10 includes the apparatus as defined in example 9, wherein the advertising circuitry is to generate telemetry information corresponding to available resources.
Example 11 includes the apparatus as defined in example 10, wherein the telemetry information includes at least one of a type of available processor, a quantity of processor cores, a type of available memory, a quantity of memory, or a type of accelerator.
Example 12 includes the apparatus as defined in example 9, wherein the advertising circuitry is to generate telemetry information corresponding to a quantity associated with the power surplus.
Example 13 includes the apparatus as defined in example 8, further including service level agreement (SLA) analysis circuitry to determine SLA parameters associated with a remote task.
Example 14 includes the apparatus as defined in example 13, wherein the SLA analysis circuitry accepts the remote task for local execution if local resources satisfy the SLA parameters.
Example 15 includes a system to direct surplus power in an Edge network comprising means for analyzing power units to identify a power surplus, means for analyzing thresholds to apply an acceleration factor to a first trigger threshold of a local task, the acceleration factor to set a second trigger threshold, and designate the local task for early execution when a current metric corresponding to the local task satisfies the second trigger threshold, and means for adaptively managing power to execute the local task in response to detecting the designation for early execution.
Example 16 includes the system as defined in example 15, further including means for advertising to advertise the power surplus to remote nodes.
Example 17 includes the system as defined in example 16, wherein the means for advertising is to generate telemetry information corresponding to available resources.
Example 18 includes the system as defined in example 17, wherein the telemetry information includes at least one of a type of available processor, a quantity of processor cores, a type of available memory, a quantity of memory, or a type of accelerator.
Example 19 includes the system as defined in example 16, wherein the means for advertising is to generate telemetry information corresponding to a quantity associated with the power surplus.
Example 20 includes the system as defined in example 15, further including means for service level agreement (SLA) analyzing to determine SLA parameters associated with a remote task.
Example 21 includes the system as defined in example 20, wherein the means for SLA analyzing is to accept the remote task for local execution if local resources satisfy the SLA parameters.
Example 22 includes At least one non-transitory computer readable storage medium comprising instructions that, when executed, cause at least one processor to at least identify a power surplus, apply an acceleration factor to a first trigger threshold of a local task, the acceleration factor to set a second trigger threshold, designate the local task for early execution when a current metric corresponding to the local task satisfies the second trigger threshold, and execute the local task in response to detecting the designation for early execution.
Example 23 includes the at least one computer readable storage medium as defined in example 22, wherein the instructions, when executed, cause the at least one processor to advertise the power surplus to remote nodes.
Example 24 includes the at least one computer readable storage medium as defined in example 23, wherein the instructions, when executed, cause the at least one processor to generate telemetry information corresponding to available resources.
Example 25 includes the at least one computer readable storage medium as defined in example 24, wherein the instructions, when executed, cause the at least one processor to identify the telemetry information as at least one of a type of available processor, a quantity of processor cores, a type of available memory, a quantity of memory, or a type of accelerator.
Example 26 includes the at least one computer readable storage medium as defined in example 23, wherein the instructions, when executed, cause the at least one processor to generate telemetry information corresponding to a quantity associated with the power surplus.
Example 27 includes the at least one computer readable storage medium as defined in example 22, wherein the instructions, when executed, cause the at least one processor to determine service level agreement (SLA) parameters associated with a remote task.
Example 28 includes the at least one computer readable storage medium as defined in example 27, wherein the instructions, when executed, cause the at least one processor to accept the remote task for local execution if local resources satisfy the SLA parameters.
The following claims are hereby incorporated into this Detailed Description by this reference. Although certain example systems, methods, apparatus, and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all systems, methods, apparatus, and articles of manufacture fairly falling within the scope of the claims of this patent.
