Patent Application
20240403118
The present disclosure generally relates to cloud computing, and more specifically, to deploying workloads in a cloud computing system based on energy efficiency.
In a cloud computing system, pod controllers are responsible for managing and distributing workloads across multiple pods. Pods are the basic units of deployment in container-based systems, such as Kubernetes. Pod controllers, like the Kubernetes Deployment or ReplicaSet controllers, ensure that the desired number of pod replicas are running and distribute the workload across them.
A pod controller receives a desired state definition, typically in the form of a declarative configuration file or through an API, which specifies the desired number of replicas and other parameters for the pods in the cloud computing system. Based on the desired state definition, the controller creates the necessary number of pod replicas, and each pod is scheduled on a suitable node in the cloud computing system. Currently, suitable nodes are identified based on the resources available on each compute node and the resources required by the workload. Once the pods are created, the controller can employ a load-balancing mechanism to distribute incoming requests or workloads across the available pod replicas. Load balancing ensures that the workload is evenly distributed to maximize resource utilization and minimize response times.
In general, the pod controller continuously monitors the health and performance of the pod replicas. If a pod becomes unresponsive or fails, the controller takes action to maintain the desired number of replicas. For example, the controller can terminate the failing pod and create another pod on another, or the same, compute node to replace the failing pod. In addition, depending on the workload demands, the pod controller can automatically scale the number of replicas up or down. This process is often based on predefined scaling policies or metrics such as CPU or memory utilization. Scaling up involves creating additional pod replicas to handle the increased load while scaling down involves terminating excess replicas during periods of lower demand.
By managing the lifecycle of pod replicas and distributing workloads across them, pod controllers help optimize resource utilization, ensure fault tolerance, and provide scalability in cloud computing systems.
Embodiments of the present disclosure are directed to computer-implemented methods for deploying workloads in a cloud computing system based on energy efficiency. The computer-implemented method includes obtaining an energy efficiency metric for each of a plurality of compute nodes in a cloud computing environment and classifying each of the plurality of compute nodes into one of a plurality of energy efficiency groups based on the energy efficiency metric of each of the plurality of compute nodes. The method also includes creating a partition of nodes from the plurality of compute nodes, wherein the partition includes one compute node selected from each of the plurality of energy efficiency groups, deploying a replica of a workload to each of the plurality of compute nodes in the partition, and monitoring an energy consumption and a computing performance of each of the plurality of compute nodes in the partition during a probing period. The method further includes identifying, based on the energy consumption and performance, a selected energy efficiency group from the plurality of energy efficiency groups and deploying the workload to one or more of the plurality of compute nodes in the selected energy efficiency group.
According to another non-limiting embodiment of the disclosure, another computer-implemented method for deploying workloads in a cloud computing system based on energy efficiency. The computer-implemented method includes obtaining an energy efficiency metric and a resource availability for each of a plurality of compute nodes in the cloud computing environment and classifying each of the plurality of compute nodes into one of a plurality of energy efficiency groups based on the energy efficiency metric of each of the plurality of compute nodes. The method also includes creating a partition of nodes from the plurality of compute nodes, wherein the partition includes one compute node selected from each of the plurality of energy efficiency groups, deploying a replica of a workload to each of the plurality of compute nodes in the partition, and monitoring an energy consumption and a computing performance of each of the plurality of compute nodes in the partition during a probing period. The method further includes identifying, based on the energy consumption and performance, a selected energy efficiency group from the plurality of energy efficiency groups and deploying the workload to one or more of the plurality of compute nodes in the selected energy efficiency group, the one or more of the plurality of compute nodes in the selected energy efficiency group selected based on the resource availability of each of the plurality of compute nodes in the selected energy efficiency group.
According to another non-limiting embodiment of the disclosure, another computer-implemented method for deploying workloads in a cloud computing system based on energy efficiency. The computer-implemented method includes obtaining resource requirements of the workload and identifying a plurality of compute nodes in the cloud computing system that have a resource availability sufficient for the resource requirements of the workload. The method also includes obtaining an energy efficiency metric for each of the plurality of compute nodes, classifying each of the plurality of compute nodes into one of a plurality of energy efficiency groups based on the energy efficiency metric of each of the plurality of compute nodes, and creating a partition of nodes from the plurality of compute nodes, wherein the partition includes one compute node selected from each of the plurality of energy efficiency groups. The method further includes deploying a replica of the workload to each of the plurality of compute nodes in the partition and monitoring an energy consumption and a computing performance of each of the plurality of compute nodes in the partition during a probing period. The method also includes identifying, based on the energy consumption and performance, a selected energy efficiency group from the plurality of energy efficiency groups and deploying the workload to one or more of the plurality of compute nodes in the selected energy efficiency group.
Embodiments also include computing systems and computer program products for deploying workloads in a cloud computing system based on energy efficiency.
Additional technical features and benefits are realized through the techniques of the present disclosure. Embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed subject matter. For a better understanding, refer to the detailed description and to the drawings.
The specifics of the exclusive rights described herein are particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of the embodiments of the present disclosure are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:
As discussed in more detail above, current methods for deploying workloads in a cloud computing system include identifying nodes for deployment based on the resources available on each node and the resources required by the workload. In addition, current load balancing methods are configured to maximize resource utilization and minimize response times in the cloud computing system. While these current methods are suitable for deploying workloads in a cloud computing system, these methods do not take into account the power consumption efficiency of executing the workloads on various compute nodes or pods.
In exemplary embodiments, a method for deploying workloads in a cloud computing system based on energy efficiency is provided. In one embodiment, the energy efficiency of each compute node in a cloud computing system is obtained and the nodes are each assigned to an energy efficiency group. When the pod controller assigns a new workload, the pod controller deploys a replica of the workload to one compute node assigned to each energy efficiency group for execution during a probing period. During the probing period, the energy consumption and computing performance of the compute nodes executing the replicas of the workload are monitored. Based on the energy consumption and the computing performance of the compute nodes executing the replicas of the workload during the probing period, a selected energy efficiency group is identified from the plurality of energy efficiency groups. Next, after the probing period, the pod controller deploys the workload to compute nodes in the selected energy efficiency group.
In exemplary embodiments, the pod controller is further configured to identify suitable compute nodes from the selected energy efficiency group for deployment of the workload based on the resources available on each node and the resources required by the workload.
Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems, and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.
A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.
Computing environment 100 contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as deploying workloads in a cloud computing system based on energy efficiency 150. In addition to block 150, computing environment 100 includes, for example, computer 101, wide area network (WAN) 102, end user device (EUD) 103, remote server 104, public cloud 105, and private cloud 106. In this embodiment, computer 101 includes processor set 110 (including processing circuitry 120 and cache 121), communication fabric 111, volatile memory 112, persistent storage 113 (including operating system 122 and block 150, as identified above), peripheral device set 114 (including user interface (UI), device set 123, storage 124, and Internet of Things (IoT) sensor set 125), and network module 115. Remote server 104 includes remote database 130. Public cloud 105 includes gateway 140, cloud orchestration module 141, host physical machine set 142, virtual machine set 143, and container set 144.
COMPUTER 101 may take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database 130. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment 100, detailed discussion is focused on a single computer, specifically computer 101, to keep the presentation as simple as possible. Computer 101 may be located in a cloud, even though it is not shown in a cloud in
PROCESSOR SET 110 includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry 120 may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry 120 may implement multiple processor threads and/or multiple processor cores. Cache 121 is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set 110. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor set 110 may be designed for working with qubits and performing quantum computing.
Computer readable program instructions are typically loaded onto computer 101 to cause a series of operational steps to be performed by processor set 110 of computer 101 and thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cache 121 and the other storage media discussed below. The program instructions, and associated data, are accessed by processor set 110 to control and direct performance of the inventive methods. In computing environment 100, at least some of the instructions for performing the inventive methods may be stored in block 150 in persistent storage 113.
COMMUNICATION FABRIC 111 is the signal conduction paths that allow the various components of computer 101 to communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up busses, bridges, physical input/output ports and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.
VOLATILE MEMORY 112 is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, the volatile memory is characterized by random access, but this is not required unless affirmatively indicated. In computer 101, the volatile memory 112 is located in a single package and is internal to computer 101, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer 101.
PERSISTENT STORAGE 113 is any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computer 101 and/or directly to persistent storage 113. Persistent storage 113 may be a read only memory (ROM), but typically at least a portion of the persistent storage allows writing of data, deletion of data and re-writing of data. Some familiar forms of persistent storage include magnetic disks and solid state storage devices. Operating system 122 may take several forms, such as various known proprietary operating systems or open source Portable Operating System Interface type operating systems that employ a kernel. The code included in block 150 typically includes at least some of the computer code involved in performing the inventive methods.
PERIPHERAL DEVICE SET 114 includes the set of peripheral devices of computer 101. Data communication connections between the peripheral devices and the other components of computer 101 may be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion type connections (for example, secure digital (SD) card), connections made though local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device set 123 may include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smart watches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storage 124 is external storage, such as an external hard drive, or insertable storage, such as an SD card. Storage 124 may be persistent and/or volatile. In some embodiments, storage 124 may take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computer 101 is required to have a large amount of storage (for example, where computer 101 locally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor set 125 is made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.
NETWORK MODULE 115 is the collection of computer software, hardware, and firmware that allows computer 101 to communicate with other computers through WAN 102. Network module 115 may include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network module 115 are performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network module 115 are performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer readable program instructions for performing the inventive methods can typically be downloaded to computer 101 from an external computer or external storage device through a network adapter card or network interface included in network module 115.
WAN 102 is any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data, now known or to be developed in the future. In some embodiments, the WAN may be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WAN and/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and edge servers.
END USER DEVICE (EUD) 103 is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer 101), and may take any of the forms discussed above in connection with computer 101. EUD 103 typically receives helpful and useful data from the operations of computer 101. For example, in a hypothetical case where computer 101 is designed to provide a recommendation to an end user, this recommendation would typically be communicated from network module 115 of computer 101 through WAN 102 to EUD 103. In this way, EUD 103 can display, or otherwise present, the recommendation to an end user. In some embodiments, EUD 103 may be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.
REMOTE SERVER 104 is any computer system that serves at least some data and/or functionality to computer 101. Remote server 104 may be controlled and used by the same entity that operates computer 101. Remote server 104 represents the machine(s) that collects and store helpful and useful data for use by other computers, such as computer 101. For example, in a hypothetical case where computer 101 is designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computer 101 from remote database 130 of remote server 104.
PUBLIC CLOUD 105 is any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloud 105 is performed by the computer hardware and/or software of cloud orchestration module 141. The computing resources provided by public cloud 105 are typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set 142, which is the universe of physical computers in and/or available to public cloud 105. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine set 143 and/or containers from container set 144. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration module 141 manages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gateway 140 is the collection of computer software, hardware, and firmware that allows public cloud 105 to communicate through WAN 102.
Some further explanation of virtualized computing environments (VCEs) will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.
PRIVATE CLOUD 106 is similar to public cloud 105, except that the computing resources are only available for use by a single enterprise. While private cloud 106 is depicted as being in communication with WAN 102, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloud 105 and private cloud 106 are both part of a larger hybrid cloud.
One or more embodiments described herein can utilize machine learning techniques to perform prediction and or classification tasks, for example. In one or more embodiments, machine learning functionality can be implemented using an artificial neural network (ANN) having the capability to be trained to perform a function. In machine learning and cognitive science, ANNs are a family of statistical learning models inspired by the biological neural networks of animals, and in particular the brain. ANNs can be used to estimate or approximate systems and functions that depend on a large number of inputs. Convolutional neural networks (CNN) are a class of deep, feed-forward ANNs that are particularly useful at tasks such as, but not limited to analyzing visual imagery and natural language processing (NLP). Recurrent neural networks (RNN) are another class of deep, feed-forward ANNs and are particularly useful at tasks such as, but not limited to, unsegmented connected handwriting recognition and speech recognition. Other types of neural networks are also known and can be used in accordance with one or more embodiments described herein.
ANNs can be embodied as so-called “neuromorphic” systems of interconnected processor elements that act as simulated “neurons” and exchange “messages” between each other in the form of electronic signals. Similar to the so-called “plasticity” of synaptic neurotransmitter connections that carry messages between biological neurons, the connections in ANNs that carry electronic messages between simulated neurons are provided with numeric weights that correspond to the strength or weakness of a given connection. The weights can be adjusted and tuned based on experience, making ANNs adaptive to inputs and capable of learning. For example, an ANN for handwriting recognition is defined by a set of input neurons that can be activated by the pixels of an input image. After being weighted and transformed by a function determined by the network's designer, the activation of these input neurons are then passed to other downstream neurons, which are often referred to as “hidden” neurons. This process is repeated until an output neuron is activated. The activated output neuron determines which character was input. It should be appreciated that these same techniques can be applied in the case of containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.
Referring now to
In one embodiment, each sample collected by the sensors 206 includes a workload identifier, a pod identifier, and a current energy usage level for the portion of the identified workload being executed on the identified pod. The current energy usage may be expressed in kW/h for each workload ID and POD ID. The sensors 206 transmit the collected activity level and energy usage level for each pod 204 to a pod controller 210. The sensors 206 are further configured to collect and transmit to the pod controller 210, a set of performance data for each pod. The performance data for each pod can include, but is not limited to, the number of instructions executed per second, the number of disk operations performed per second, and the number of network operations performed per second by the compute node for each pod.
In one embodiment, each compute node 202 is configured to maintain an energy efficiency metric that tracks the overall energy efficiency of the compute node 202. The overall energy efficiency metric of the compute node 202 is the combined energy efficiency of the pods 204 executing on the compute node 202. The energy efficiency of the compute node is a measure of the computing operations performed by the compute node per unit of energy consumed by the compute node.
In one embodiment, the pod controller 210 is configured to obtain the energy efficiency metric from each node 202 in the cloud computing system 200. In one embodiment, the nodes 202 may periodically transmit the energy efficiency metric to the pod controller 210. In another embodiment, the pod controller 210 is configured to obtain the energy efficiency metric from each node 202 each time the pod controller 210 requires the energy efficiency metrics.
In exemplary embodiments, the pod controller 210 is configured to receive a new workload for deployment in the cloud computing system 200. As described in more detail in
Referring now to
At block 304, the method 300 includes classifying each of the plurality of compute nodes into one of a plurality of energy efficiency groups based on the energy efficiency metric of each of the plurality of compute nodes. In one embodiment, the plurality of energy efficiency groups includes at least three groups, a high-efficiency group, a high-performance group, and a balanced group. The high-efficiency group is characterized by compute nodes with an energy efficiency metric that is above a first threshold level. The high-performance group is characterized by compute nodes with an energy efficiency metric that is below a second threshold level. The balanced group is characterized by compute nodes with an energy efficiency metric that is between the first and second threshold levels. In one embodiment, the first and second threshold levels may be set by an administrator of the cloud computing system. In another embodiment, the first and second threshold levels may be automatically calculated by a pod controller of the cloud computing system based on a statistical analysis of the energy efficiency metric of each of the plurality of compute nodes. For example, the pod controller may be configured to set the first and second threshold levels such that twenty-five percent of the nodes are assigned to the high-performance group, twenty-five percent of the nodes are assigned to the high-efficiency group, and the remaining nodes are assigned to the balanced group.
At block 306, the method 300 includes creating a partition of nodes from the plurality of compute nodes, wherein the partition includes one compute node selected from each of the plurality of energy efficiency groups. For example, in an embodiment having three energy efficiency groups, the partition would include one node from each group. In one embodiment, multiple partitions are created by the pod controller.
At block 308, the method 300 includes deploying a replica of a workload to each of the plurality of compute nodes in the partition. Next, as shown at block 310, the method 300 includes monitoring an energy consumption and the computing performance of each of the plurality of compute nodes in the partition during a probing period. In exemplary embodiments, the duration of the probing period is set by an administrator of the cloud computing system.
At block 312, the method 300 includes identifying, based on the energy consumption and performance, a selected energy efficiency group from the plurality of energy efficiency groups. In one embodiment, the selected energy efficiency group is identified as the group having the highest energy efficiency metric for the replica workload. That is which node had the highest computing performance per unit of energy consumed while executing the replica workload. In one embodiment, the computing performance is measured using one or more of the number of instructions executed per second, the number of disk operations performed per second, and the number of network operations performed per second by the compute node. In one embodiment, the energy efficiency metric is calculated as a weighted combination of the number of instructions executed per second, the number of disk operations performed per second, and the number of network operations performed per second by the compute node divided by the amount of energy consumed by the compute node during the probing period.
At block 314, the method 300 includes deploying the workload to one or more of the plurality of compute nodes in the selected energy efficiency group. In one embodiment, deploying the workload further includes identifying a resource availability of each of the plurality of compute nodes in the selected energy efficiency group and selecting one or more of the plurality of compute nodes in the selected energy efficiency group based on the resource availability of each of the plurality of compute nodes in the selected energy efficiency group.
Referring now to
At block 404, the method 400 includes classifying each of the plurality of compute nodes into one of a plurality of energy efficiency groups based on the energy efficiency metric of each of the plurality of compute nodes. In one embodiment, the plurality of energy efficiency groups includes at least three groups, a high-efficiency group, a high-performance group, and a balanced group. The high-efficiency group is characterized by compute nodes with an energy efficiency metric that is above a first threshold level. The high-performance group is characterized by compute nodes with an energy efficiency metric that is below a second threshold level. The balanced group is characterized by compute nodes with an energy efficiency metric that is between the first and second threshold levels. In one embodiment, the first and second threshold levels may be set by an administrator of the cloud computing system. In another embodiment, the first and second threshold levels may be automatically calculated by a pod controller of the cloud computing system based on a statistical analysis of the energy efficiency metric of each of the plurality of compute nodes. For example, the pod controller may be configured to set the first and second threshold levels such that twenty-five percent of the nodes are assigned to the high-performance group, twenty-five percent of the nodes are assigned to the high-efficiency group, and the remaining nodes are assigned to the balanced group.
At block 406, the method 400 includes creating a partition of nodes from the plurality of compute nodes, wherein the partition includes one compute node selected from each of the plurality of energy efficiency groups. For example, in an embodiment having three energy efficiency groups, the partition would include one node from each group. In one embodiment, multiple partitions are created by the pod controller.
At block 408, the method 400 includes deploying a replica of a workload to each of the plurality of compute nodes in the partition. Next, as shown at block 410 the method 400 includes monitoring energy consumption and the computing performance of each of the plurality of compute nodes in the partition during a probing period. In exemplary embodiments, the duration of the probing period is set by an administrator of the cloud computing system.
At block 412, the method 400 includes identifying, based on the energy consumption and performance, a selected energy efficiency group from the plurality of energy efficiency groups. In one embodiment, the selected energy efficiency group is identified as the group having the highest energy efficiency metric for the replica workload. That is which node had the highest computing performance per unit of energy consumed while executing the replica workload. In one embodiment, the computing performance is measured using one or more of the number of instructions executed per second, the number of disk operations performed per second, and the number of network operations performed per second by the compute node. In one embodiment, the energy efficiency metric is calculated as a weighted combination of the number of instructions executed per second, the number of disk operations performed per second, and the number of network operations performed per second by the compute node divided by the amount of energy consumed by the compute node during the probing period.
The method 400 concludes at block 414 by deploying the workload to one or more of the plurality of compute nodes in the selected energy efficiency group, the one or more of the plurality of compute nodes in the selected energy efficiency group selected based on the resource availability of each of the plurality of compute nodes in the selected energy efficiency group.
Referring now to
At block 506, the method 500 includes obtaining an energy efficiency metric for each of the plurality of compute nodes. In one embodiment, the energy efficiency metric of a compute node includes the computing performance per unit of consumed energy by the compute node. In one embodiment, the energy efficiency metric for each of the plurality of compute nodes is obtained by a pod controller from each of the plurality of compute nodes by querying each of the plurality of compute nodes.
At block 508, the method 500 includes classifying each of the plurality of compute nodes into one of a plurality of energy efficiency groups based on the energy efficiency metric of each of the plurality of compute nodes. In one embodiment, the plurality of energy efficiency groups includes at least three groups, a high-efficiency group, a high-performance group, and a balanced group. The high-efficiency group is characterized by compute nodes with an energy efficiency metric that is above a first threshold level. The high-performance group is characterized by compute nodes with an energy efficiency metric that is below a second threshold level. The balanced group is characterized by compute nodes with an energy efficiency metric that is between the first and second threshold levels. In one embodiment, the first and second threshold levels may be set by an administrator of the cloud computing system. In another embodiment, the first and second threshold levels may be automatically calculated by a pod controller of the cloud computing system based on a statistical analysis of the energy efficiency metric of each of the plurality of compute nodes. For example, the pod controller may be configured to set the first and second threshold levels such that twenty-five percent of the nodes are assigned to the high-performance group, twenty-five percent of the nodes are assigned to the high-efficiency group, and the remaining nodes are assigned to the balanced group.
At block 510, the method 500 includes creating a partition of nodes from the plurality of compute nodes, wherein the partition includes one compute node selected from each of the plurality of energy efficiency groups. For example, in an embodiment having three energy efficiency groups, the partition would include one node from each group. In one embodiment, multiple partitions are created by the pod controller.
At block 512, the method 500 includes deploying a replica of the workload to each of the plurality of compute nodes in the partition. Next, as shown at block 514 the method 500 includes monitoring energy consumption and the computing performance of each of the plurality of compute nodes in the partition during a probing period. In exemplary embodiments, the duration of the probing period is set by an administrator of the cloud computing system.
At block 516, the method 500 includes identifying, based on the energy consumption and performance, a selected energy efficiency group from the plurality of energy efficiency groups. In one embodiment, the selected energy efficiency group is identified as the group having the highest energy efficiency metric for the replica workload. That is which node had the highest computing performance per unit of energy consumed while executing the replica workload. In one embodiment, the computing performance is measured using one or more of the number of instructions executed per second, the number of disk operations performed per second, and the number of network operations performed per second by the compute node. In one embodiment, the energy efficiency metric is calculated as a weighted combination of the number of instructions executed per second, the number of disk operations performed per second, and the number of network operations performed per second by the compute node divided by the amount of energy consumed by the compute node during the probing period. The method 500 concludes at block 518 by deploying the workload to one or more of the plurality of compute nodes in the selected energy efficiency group.
Various embodiments are described herein with reference to the related drawings. Alternative embodiments can be devised without departing from the scope of the present disclosure. Various connections and positional relationships (e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and/or positional relationships, unless specified otherwise, can be direct or indirect, and the present disclosure is not intended to be limiting in this respect. Accordingly, a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirect positional relationship. Moreover, the various tasks and process steps described herein can be incorporated into a more comprehensive procedure or process having additional steps or functionality not described in detail herein.
One or more of the methods described herein can be implemented with any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.
For the sake of brevity, conventional techniques related to making and using aspects of the present disclosure may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly herein or are omitted entirely without providing the well-known system and/or process details.
In some embodiments, various functions or acts can take place at a given location and/or in connection with the operation of one or more apparatuses or systems. In some embodiments, a portion of a given function or act can be performed at a first device or location, and the remainder of the function or act can be performed at one or more additional devices or locations.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and/or groups thereof.
The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the disclosure. The embodiments were chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.
The diagrams depicted herein are illustrative. There can be many variations to the diagram or the steps (or operations) described therein without departing from the spirit of the disclosure. For instance, the actions can be performed in a differing order or actions can be added, deleted or modified. Also, the term “coupled” describes having a signal path between two elements and does not imply a direct connection between the elements with no intervening elements/connections therebetween. All of these variations are considered a part of the present disclosure.
The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains” or “containing,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus.
Additionally, the term “exemplary” is used herein to mean “serving as an example, instance or illustration.” Any embodiment or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. The terms “at least one” and “one or more” are understood to include any integer number greater than or equal to one, i.e. one, two, three, four, etc. The terms “a plurality” are understood to include any integer number greater than or equal to two, i.e. two, three, four, five, etc. The term “connection” can include both an indirect “connection” and a direct “connection.”
The terms “about,” “substantially,” “approximately,” and variations thereof, are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” can include a range of ±8% or 5%, or 2% of a given value.
The present disclosure may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instruction by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
The descriptions of the various embodiments of the present disclosure have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments described herein.
