Multi-region computing power scheduling method and device for intelligent computing center oriented to common computing power

Abstract

The invention relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures, and provides a multi-region computing power scheduling method and device for a common computing power-oriented intelligent computing center, and the method comprises the steps: determining and determining at least one selectable intelligent computing center in all alternative intelligent computing centers according to a task to be migrated, and according to the task to be migrated, determining at least one selectable intelligent computing center in all alternative intelligent computing centers; determining whether a target intelligent computing center exists in the at least one selectable intelligent computing center; if yes, the current intermediate operation result of the to-be-migrated task is migrated to the target intelligent computing center; and continuing to operate the to-be-migrated task based on the computing power of the target intelligent computing center and the intermediate operation result. Therefore, efficient scheduling and optimal configuration of the computing power resources are realized through an intelligent task migration mechanism. On the premise of ensuring the smooth execution of the task, the economic cost of using the computing power resource of the intelligent computing center by the user is greatly reduced, the cross-regional sharing and collaboration of the computing power resource are promoted, and the wide application of the general computing power is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent computing centers, smart computing centers and computing power infrastructure, and specifically to a multi-region computing power scheduling method and device for a universal computing power intelligent computing center. Background Art

[0002] With the rapid development of artificial intelligence technology, "intelligent computing centers" and "intelligent computing centers" have come into being.

[0003] "Intelligent computing center" refers to a facility that provides the required computing power, data and algorithms for artificial intelligence applications (such as artificial intelligence deep learning model development, model training and model reasoning) by using large-scale heterogeneous computing resources, including general computing power and intelligent computing power. The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from bottom-level computing power to top-level application enablement.

[0004] “Intelligent Computing Center” includes but is not limited to “Smart Computing Center”.

[0005] "Intelligent Computing Center" refers to an artificial intelligence computing center. It is a type of computing infrastructure that is based on artificial intelligence theory, adopts artificial intelligence computing architecture, and provides computing power services, data services, and algorithm services required for artificial intelligence applications.

[0006] "Computing power" is the core of "intelligent computing center" and "intelligent computing center". It is the ability of computer equipment or computing / data center to process information. It is the ability of computer hardware and software to work together to execute certain computing needs. It is the computing power to process information data and achieve target result output. It is a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity. It mainly provides services to society through computing power infrastructure.

[0007] However, at present, when executing tasks based on the computing resources of the intelligent computing center (such as model training tasks), the tasks are usually fixed to a single intelligent computing center, causing users to bear huge electricity bills in peak electricity prices or high-cost areas. At the same time, the ability to coordinate computing resources across regions is lacking, some intelligent computing centers are overloaded for a long time, while the computing resources of other intelligent computing centers are idle, exacerbating the uneven distribution of computing power.

[0008] In summary, in the existing technology, when users rely on the computing power resources of the intelligent computing center to perform tasks, they still face computing power resource scheduling bottlenecks and high economic costs, which restricts the promotion and application of inclusive computing power. Summary of the invention

[0009] The present invention provides a multi-region computing power scheduling method and device for an inclusive computing power intelligent computing center to solve the technical problem in the prior art that when users rely on the computing power resources of the intelligent computing center to perform tasks, they still face computing power resource scheduling bottlenecks and high economic costs, which restricts the promotion and application of inclusive computing power.

[0010] In order to solve the above-mentioned technical problems, the present invention is achieved as follows: In a first aspect, the present invention provides a multi-region computing power scheduling method for a universal computing power intelligent computing center, the method comprising: Step S1: determining a task to be migrated, wherein the task to be migrated needs to be executed based on the computing power of the intelligent computing center, and the task to be migrated is currently running on the source intelligent computing center; Step S2: determining at least one optional intelligent computing center from among the candidate intelligent computing centers according to the task to be migrated, wherein each candidate intelligent computing center is located in a different preset area; Step S3: determining whether there is a target intelligent computing center in the at least one optional intelligent computing center according to the task to be migrated; Wherein, the step S3 comprises: Step S31: Calculate the migration cost of migrating the task to be migrated from the source intelligent computing center to each optional intelligent computing center, wherein the migration cost is determined according to the amount of data to be migrated of the task to be migrated and the traffic cost of the amount of data to be migrated; Step S32: Calculate a first continuing execution cost of the task to be migrated on the source intelligent computing center, wherein the first continuing execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of the preset area where the source intelligent computing center is located; Step S33: calculating the second continuing execution cost of the task to be migrated on each optional intelligent computing center respectively, wherein the second continuing execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of the preset area where the corresponding optional intelligent computing center is located; Step S34: determining a minimum second continuing execution cost among the second continuing execution costs; Step S35: If the first continuing execution cost is greater than the sum of the minimum second continuing execution cost, the migration cost and a preset threshold, it is determined that the target intelligent computing center exists; wherein the target intelligent computing center is an optional intelligent computing center corresponding to the minimum second continuing execution cost; Step S36: if the first continuing execution cost is less than or equal to the sum of the minimum second continuing execution cost, the migration cost and the preset threshold, determining that the target intelligent computing center does not exist; Step S4: if the target intelligent computing center exists, migrating the current intermediate running result of the task to be migrated to the target intelligent computing center; Step S5: Based on the computing power of the target intelligent computing center and the intermediate operation results, continue to run the task to be migrated.

[0011] Optionally, after step S3, the method further includes: Step S6: If the target intelligent computing center does not exist, continue to run the task to be migrated on the source intelligent computing center, and repeat step S3 every first preset time period until the task to be migrated is completed on the source intelligent computing center, or until it is determined that the target intelligent computing center exists.

[0012] Optionally, step S2 includes: Step S21: determining a requirement tag of the task to be migrated, wherein the requirement tag includes at least one of the following: GPU model, amount of data to be migrated; Step S22: Determine the optional intelligent computing center based on the demand label and the estimated computing power of each alternative intelligent computing center in the future second preset time period, wherein the estimated computing power includes at least one of the following: the GPU model available in the future second preset time period and the GPU utilization rate in the future second preset time period.

[0013] Optionally, step S5 includes: Step S51: Determine the task content of the task to be migrated, wherein the task content includes: training script, reasoning code and container image; Step S52: On the target intelligent computing center, based on the intermediate running result and the task content, continue to run the task to be migrated.

[0014] In a second aspect, the present invention provides a multi-region computing power scheduling device for a universal computing power intelligent computing center, the device comprising: A determination module, used to determine a task to be migrated, wherein the task to be migrated needs to be executed based on the computing power of the intelligent computing center, and the task to be migrated is currently running on the source intelligent computing center; An execution module, configured to determine at least one optional intelligent computing center from among the candidate intelligent computing centers according to the task to be migrated, wherein each candidate intelligent computing center is located in a different preset area; According to the task to be migrated, determining whether there is a target intelligent computing center in the at least one optional intelligent computing center; If the target intelligent computing center exists, migrating the current intermediate running result of the task to be migrated to the target intelligent computing center; Based on the computing power of the target intelligent computing center and the intermediate operation result, continue to run the task to be migrated; The execution module is further used to calculate the migration cost of migrating the task to be migrated from the source intelligent computing center to each optional intelligent computing center, wherein the migration cost is determined according to the amount of data to be migrated of the task to be migrated and the traffic cost of the amount of data to be migrated; Calculating a first continuing execution cost of the task to be migrated on the source intelligent computing center, wherein the first continuing execution cost is determined by a remaining task execution time of the task to be migrated and an estimated electricity price of a preset area where the source intelligent computing center is located; Calculate the second continuing execution cost of the task to be migrated on each optional intelligent computing center respectively, wherein the second continuing execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of the preset area where the corresponding optional intelligent computing center is located; Among the second continuing execution costs, determining a minimum second continuing execution cost; If the first continuing execution cost is greater than the sum of the minimum second continuing execution cost, the migration cost and a preset threshold, it is determined that the target intelligent computing center exists; wherein the target intelligent computing center is an optional intelligent computing center corresponding to the minimum second continuing execution cost; If the first continuing execution cost is less than or equal to the sum of the minimum second continuing execution cost, the migration cost and the preset threshold, it is determined that the target intelligent computing center does not exist.

[0015] Optionally, the execution module is also used to, after determining whether there is a target intelligent computing center in the optional intelligent computing center based on the task to be migrated, continue to run the task to be migrated on the source intelligent computing center if the target intelligent computing center does not exist, and repeat the step of determining whether there is a target intelligent computing center in the optional intelligent computing center based on the task to be migrated once every first preset time period until the task to be migrated is completed in the source intelligent computing center, or until it is determined that the target intelligent computing center exists.

[0016] Optionally, the execution module is further used to determine a requirement tag of the task to be migrated, wherein the requirement tag includes at least one of the following: GPU model, amount of data to be migrated; The optional intelligent computing center is determined based on the demand label and the estimated computing power conditions of each alternative intelligent computing center in the second preset time period in the future, wherein the estimated computing power conditions include at least one of the following: the GPU model available in the second preset time period in the future and the GPU utilization rate in the second preset time period in the future.

[0017] Optionally, the execution module is also used to determine the task content of the task to be migrated, and the task content includes: training script, inference code and container image; on the target intelligent computing center, based on the intermediate running results and the task content, continue to run the task to be migrated.

[0018] In a third aspect, the present invention provides a server comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of a multi-region computing power scheduling method for a universal computing power intelligent computing center as described in the first aspect above are implemented.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of a multi-region computing power scheduling method for a universal computing power intelligent computing center as described in the first aspect above are implemented.

[0020] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of a multi-region computing power scheduling method for a universal computing power intelligent computing center as described in the first aspect above.

[0021] In the present invention, firstly, the alternative intelligent computing centers in different regions are screened to determine the optional intelligent computing center, and the migration cost (determined according to the amount of data to be migrated and the traffic cost) and the continued execution cost of the optional intelligent computing center (determined according to the remaining time of task execution and the estimated electricity price of the region) are quantified and compared with the continued execution cost of the source intelligent computing center. When the continued execution cost of the source intelligent computing center is higher than the sum of the minimum execution cost, migration cost and preset threshold of the target intelligent computing center, the intermediate state of the task is migrated to the target intelligent computing center with the best electricity price for continued operation.

[0022] Therefore, through the intelligent task migration mechanism, efficient scheduling and optimal configuration of computing resources are achieved. On the premise of ensuring the smooth execution of tasks, the economic cost of users using the computing resources of the intelligent computing center is significantly reduced, the cross-regional sharing and coordination of computing resources is promoted, and the widespread application of inclusive computing power is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flowchart of a multi-region computing power scheduling method for a universal computing power intelligent computing center provided by the present invention; Figure 2 A flowchart of a multi-region computing power scheduling method for a universal computing power intelligent computing center provided by the present invention; Figure 3 A structural block diagram of a multi-region computing power scheduling device for a universal computing power intelligent computing center provided by the present invention; Figure 4 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] First, the technical terms involved in the present invention are briefly explained below.

[0026] The "computing power" mentioned in the present invention refers to: the ability of computer equipment or computing / data center to process information, the ability of computer hardware and software to work together to execute certain computing requirements, and the computing power to achieve target result output by processing information data. It is a new type of productivity that integrates information computing power, network carrying capacity, and data storage capacity, and it mainly provides services to the society through computing power infrastructure.

[0027] The "computing power" (CP) described in the present invention refers to: the ability of a data center server to process data and output results. It is a comprehensive indicator for measuring the computing power of a data center, including general computing power, super computing power and intelligent computing power. The commonly used unit of measurement is the number of floating-point operations performed per second (FLOPS, 1EFLOPS=10^18 FLOPS). The larger the value, the stronger the comprehensive computing power. According to calculations, 1 EFLOPS is approximately the computing power output of 5 Tianhe-2A or 500,000 mainstream server CPUs or 2 million mainstream notebooks. The calculation formula is: CP=CP general+CP intelligent+CP super.

[0028] The "carrying capacity" (Network Power, NP) mentioned in the present invention refers to: it is the performance of the data transmission capacity of the computing power facilities, including the comprehensive capabilities of network architecture, network bandwidth, transmission delay, intelligent management and scheduling, etc. It involves network transmission within and between data centers, and is a comprehensive indicator for measuring network transmission scheduling capabilities.

[0029] The "Storage Power" (SP) mentioned in the present invention refers to: the comprehensive capabilities of a data center in terms of data storage capacity, performance, safety and reliability, and green and low-carbon. It is a comprehensive indicator for measuring the data storage capacity of a data center, including external storage devices such as storage arrays and server built-in storage devices. The commonly used unit of measurement for storage capacity is exabyte (EB, 1EB=2^60bytes), and the commonly used unit of measurement for performance is the number of reads and writes per second per unit capacity (IOPS / TB, Input / Output Operations Per Second / TB). The disaster recovery ratio is an important manifestation of safety and reliability.

[0030] The "computing power infrastructure" mentioned in the present invention refers to: a new type of information infrastructure that integrates information computing power, network carrying capacity, and data storage capacity, which can realize centralized computing, storage, transmission and application of information.

[0031] The "new information infrastructure" mentioned in the present invention refers to: mainly including network infrastructure such as 5G networks, fiber-optic broadband networks, backbone networks, international communication networks, satellite Internet, computing power infrastructure such as data centers, general computing power centers, intelligent computing centers, supercomputing centers, and new technology facilities such as artificial intelligence, blockchain, and quantum computing.

[0032] The “computing power” mentioned in the present invention includes: general computing power, intelligent computing power and super computing power.

[0033] The “general computing power” mentioned in the present invention refers to: the computing power provided by servers based on CPU (Central Processing Unit) chips, which is used to support basic general computing such as cloud computing and edge computing.

[0034] The "intelligent computing power" mentioned in the present invention refers to: a computing platform based on GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), ASIC (Application Specific Integrated Circuit) and other dedicated chips for various innovative artificial intelligence applications, such as natural language processing, machine vision, etc.

[0035] The "super computing power" mentioned in the present invention refers to: the computing power mainly provided by high-performance computing clusters such as supercomputers. It uses the centralized computing resources of multiple computer systems working in parallel and uses a dedicated operating system to handle extremely complex or data-intensive problems. It is mainly used for calculations in cutting-edge scientific fields, such as planetary simulation, drug molecule design, gene analysis, etc.

[0036] The "intelligent computing center" mentioned in the present invention refers to a facility that provides the required computing power, data and algorithms for artificial intelligence applications (such as artificial intelligence deep learning model development, model training and model reasoning) by using large-scale heterogeneous computing resources, including general computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.). The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from bottom-level computing power to top-level application enablement.

[0037] The “intelligent computing center” mentioned in the present invention includes but is not limited to the “intelligent computing center”.

[0038] The "intelligent computing center" mentioned in the present invention is an artificial intelligence computing center, which is a type of computing power infrastructure that is based on artificial intelligence theory, adopts artificial intelligence computing architecture, and provides computing power services, data services, and algorithm services required for artificial intelligence applications.

[0039] The "computing power center" mentioned in the present invention refers to: a facility that is mainly composed of infrastructure such as wind, fire, water, electricity, and IT hardware and software equipment, and has computing power, transportation capacity, and storage capacity, including general data centers, intelligent computing centers, supercomputing centers, etc.

[0040] The "supercomputing center" mentioned in the present invention refers to: a supercomputing data center, which is a data center based on a supercomputer or a large-scale computing cluster, which can provide functions such as large-scale computing, storage and network services, and is widely used in application scenarios such as aerospace, national defense, oil exploration, climate modeling and genome sequencing.

[0041] The "computing resources" mentioned in the present invention refer to: technologies and facilities with information computing, transmission, storage and application capabilities required for the development of a digital society, including but not limited to computing resources such as CPUs and GPUs, network resources such as switches and routers, storage resources such as storage arrays and distributed storage, security resources such as firewalls and intrusion detection systems, and supporting and guarantee resources such as wind, fire, water and electricity.

[0042] The "inclusive computing power" mentioned in the present invention refers to providing appropriate and effective computing power services at an affordable cost to all social classes and groups that have computing power service needs based on the requirements of equal opportunity and the principle of commercial sustainability.

[0043] Figure 1A multi-region computing power scheduling method for a universal computing power intelligent computing center provided by the present invention is shown. Figure 1 As shown, the method includes: Step S1: determine the tasks to be migrated; The tasks to be migrated need to be executed based on the computing power of the intelligent computing center, and the tasks to be migrated are currently running on the source intelligent computing center; Step S2: determining at least one optional intelligent computing center from among the candidate intelligent computing centers according to the task to be migrated; Wherein, each candidate intelligent computing center is located in a different preset area; Step S3: determining whether there is a target intelligent computing center in at least one optional intelligent computing center according to the task to be migrated; Step S4: if there is a target intelligent computing center, the current intermediate running result of the task to be migrated is migrated to the target intelligent computing center; Step S5: Based on the computing power and intermediate operation results of the target intelligent computing center, continue to run the task to be migrated.

[0044] In step S1, the computing tasks that need to be migrated (such as model training tasks) must first be determined. These tasks usually rely on the powerful computing power of the intelligent computing center and are currently being executed on the source intelligent computing center. By clarifying the tasks to be migrated, the foundation can be laid for subsequent steps to ensure the effectiveness and necessity of the migration process, thereby avoiding unnecessary waste of resources.

[0045] It should be noted that users can submit computing power usage tasks (tasks to be migrated) through the Web (World Wide Web) interface and / or RESTful API (Representational State Transfer Application Programming Interface), and enter the following parameters: computing tasks and requirement tags. Computing tasks include: training scripts, inference codes, and container images (i.e., the environment for code execution). Requirement tags include: GPU models (such as A100 and / or V100) and data size. The task to be migrated is executed on the source intelligent computing center.

[0046] In step S2, at least one optional intelligent computing center is selected from multiple candidate intelligent computing centers according to the characteristics of the task to be migrated. These candidate intelligent computing centers are located in different preset regions (such as the East China region and the North China region) so as to achieve load balancing and resource optimization between different geographical locations.

[0047] In one possible implementation, step S2 includes: step S21: determining a demand tag for the task to be migrated, wherein the demand tag includes at least one of the following: a GPU model and an amount of data to be migrated; step S22: determining an optional intelligent computing center based on the demand tag and an estimated computing power situation of each candidate intelligent computing center in a second preset time period in the future, wherein the estimated computing power situation includes at least one of the following: a GPU model available in the second preset time period in the future, and a GPU utilization rate in the second preset time period in the future (it should be noted that the GPU utilization rate can be determined every 30 seconds).

[0048] It should be noted that first, the characteristics of the tasks to be migrated need to be analyzed to extract their demand labels. The demand label is a key indicator that describes the computing resource requirements of the task, and includes at least one of the following: GPU model, that is, the task to be migrated requires a specific model of GPU to meet its computing needs, especially for tasks such as deep learning and graphics processing; the amount of data to be migrated, the amount of data that needs to be processed by the task to be migrated will affect the required storage and bandwidth resources, and thus affect the migration and execution efficiency of the task. After determining the demand labels of the tasks to be migrated, the optional intelligent computing centers will be screened out based on these labels and the estimated computing power of each candidate intelligent computing center in the second preset time period in the future. Therefore, by combining the demand labels and the estimated computing power, the optional intelligent computing centers can be preliminarily screened out more accurately, so that the target intelligent computing center can be determined later.

[0049] For example, the GPU model required for the task to be migrated is A100, and the estimated computing power of an intelligent computing center located in the East China region in the second preset time period in the future (for example, within 2 hours) does not match it, such as there is no GPU with model A100, then it cannot be used as an optional intelligent computing center; if the amount of data to be migrated of the task to be migrated is 500GB, and the estimated computing power of an intelligent computing center located in the North China region in the second preset time period in the future does not match it, such as the GPU utilization rate is too high and the number of GPUs is small, then it cannot be used as an optional intelligent computing center.

[0050] In step S3, after confirming at least one optional intelligent computing center, it will be further checked whether there is a suitable target intelligent computing center among these intelligent computing centers. The target center must have sufficient computing power and resources to receive the task to be migrated and be able to continue to execute the task.

[0051] In step S4, if the target intelligent computing center is determined, the current intermediate running results of the task to be migrated need to be migrated to the target intelligent computing center. By migrating the intermediate running results, the continuity of the task can be guaranteed, and recalculation and data loss can be avoided, thereby saving time and computing power resources and improving the overall execution efficiency of the task.

[0052] In step S5, after the target intelligent computing center receives the intermediate operation result, it will use the computing power of the target intelligent computing center to continue to execute the task to be migrated. This process can be dynamically adjusted according to the needs of the task to ensure the best computing performance. Therefore, by continuing to run the task to be migrated in the target intelligent computing center, not only can the computing power resources of the target intelligent computing center be fully utilized, but also the computing resources can be flexibly scheduled and optimally configured, thereby reducing economic costs and promoting the application of inclusive computing power.

[0053] In one possible implementation, step S5 includes: step S51: determining the task content of the task to be migrated, the task content including: training script, reasoning code and container image; step S52: continuing to run the task to be migrated on the target intelligent computing center based on the intermediate running results and task content.

[0054] It should be noted that the intermediate running results can be saved in the high-cost area (the area where the source intelligent computing center is located), and the intermediate running results can be copied and migrated. In the low-cost area (the area where the target intelligent computing center is located), the container computing power resources can be started to load the intermediate running results and continue to run the tasks to be migrated.

[0055] In one possible implementation, Figure 2 As shown, step S3 includes: Step S31: Calculate the migration cost of migrating the task to be migrated from the source intelligent computing center to each optional intelligent computing center; The migration cost is determined based on the amount of data to be migrated and the traffic cost of the data to be migrated. Step S32: Calculate the first continued execution cost of the task to be migrated on the source intelligent computing center; The first continued execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of the preset area where the source intelligent computing center is located; Step S33: respectively calculating the second continuing execution cost of the task to be migrated on each optional intelligent computing center; The second continued execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of the preset area where the corresponding optional intelligent computing center is located; Step S34: determining a minimum second continuing execution cost among the second continuing execution costs; Step S35: if the first continuing execution cost is greater than the sum of the minimum second continuing execution cost, the migration cost and the preset threshold, it is determined that the target intelligent computing center exists; The target intelligent computing center is the optional intelligent computing center corresponding to the minimum second continued execution cost; Step S36: If the first continuing execution cost is less than or equal to the sum of the minimum second continuing execution cost, the migration cost and the preset threshold, it is determined that the target intelligent computing center does not exist.

[0056] It should be noted that, first, it is necessary to calculate the migration cost of migrating the task to be migrated from the source intelligent computing center to each optional intelligent computing center. The migration cost is mainly determined based on the amount of data to be migrated and the traffic cost of the task to be migrated. Next, calculate the first continued execution cost of the task to be migrated in the source intelligent computing center. This cost is determined by the remaining execution time of the task to be migrated and the estimated electricity price in the area where the source intelligent computing center is located. This cost evaluates the economic feasibility of continuing to execute the task in the source intelligent computing center. Subsequently, the second continued execution cost of the task to be migrated on each optional intelligent computing center can be calculated separately. The factors considered are also the remaining execution time of the task to be migrated and the estimated electricity price in the area where the corresponding optional intelligent computing center is located. The electricity price is estimated. This process can compare the continued execution costs of different optional intelligent computing centers. After calculating the second continued execution costs of all optional intelligent computing centers, the minimum value must be determined, that is, the minimum second continued execution cost is determined. This minimum second continued execution cost will serve as the basis for subsequent decisions. If the first continued execution cost is greater than the sum of the minimum second continued execution cost, the migration cost, and the preset threshold, it will be determined that there is a target intelligent computing center. If the first continued execution cost is less than or equal to the sum of the minimum second continued execution cost, the migration cost, and the preset threshold, it is determined that there is no suitable target intelligent computing center, and migration will not be performed temporarily, and the task to be migrated is still executed on the source intelligent computing center.

[0057] Through this series of steps, the economic efficiency between the source intelligent computing center and the optional intelligent computing center can be fully evaluated, so as to make a more reasonable migration decision. This method not only improves the utilization efficiency of computing resources, but also significantly reduces the economic cost of users using the computing resources of the intelligent computing center, promotes the cross-regional sharing and coordination of computing resources, and realizes the widespread application of inclusive computing power.

[0058] Figure 2 The method shown can be expressed as follows: Migration cost = amount of data to be migrated (G) × traffic cost (G / yuan); First, the cost of continuing execution = the remaining task execution time × the estimated electricity price in the current area (hours / yuan); The second cost of continued execution = remaining task execution time × estimated electricity price in other areas (hours / yuan); The conditions for triggering migration are: first continued execution cost>min(second continued execution cost)+migration cost+migration cost jitter prevention threshold.

[0059] Among them, the estimated electricity price can be the average electricity price in the next two hours. Each optional intelligent computing center corresponds to a second continued execution cost. The migration cost jitter prevention threshold is equivalent to the above-mentioned preset threshold. By setting the migration cost jitter prevention threshold, frequent migrations caused by smaller cost fluctuations (such as electricity price fluctuations, etc.) can be avoided, thereby ensuring the stability of the task and the reliability of execution.

[0060] In a specific application scenario, assume that the type of task to be migrated is a deep learning model training task, the amount of data to be migrated is 50 GB, the traffic cost is 0.1 yuan / GB, the remaining task execution time is 20 hours, and the electricity price in the region where the source intelligent computing center is located (East China) is 1.5 yuan / hour (estimated value for the next 2 hours). The estimated electricity prices in the regions where the optional intelligent computing centers are located are: North China: 0.8 yuan / hour, South China: 1.0 yuan / hour, and the migration cost jitter prevention threshold is 5 yuan.

[0061] The migration cost is: 50GB×0.1 yuan / GB=5 yuan, the first execution cost is: 20 hours×1.5 / hour=30 yuan, the second execution cost in the North China region is: 20 hours×0.8 yuan / hour=16 yuan, the second execution cost in the South China region is: 20 hours×1 yuan / hour=20 yuan, and the minimum second execution cost is 16 yuan. Trigger condition=30 yuan>(16 yuan+5 yuan+5 yuan)=26 yuan, then the trigger condition is met, and the task to be migrated can be migrated from the source intelligent computing center (East China region) to the target intelligent computing center (North China region).

[0062] It should be noted that the migration cost includes not only the data flow cost (such as the transmission fee per GB), but also the time cost. The transmission rate directly affects the time of data migration. A low transmission rate will lead to a long migration time, which in turn increases the time cost. Therefore, the transmission rate can also be taken into account in the migration cost. If the transmission rate from the source intelligent computing center to a certain intelligent computing center is too low, carefully consider whether to select the intelligent computing center as the target intelligent computing center to ensure a more comprehensive scheduling decision.

[0063] In a possible implementation, after step S3, the method further includes: Step S6: If the target intelligent computing center does not exist, continue to run the task to be migrated on the source intelligent computing center, and repeat step S3 every first preset time period until the task to be migrated is completed on the source intelligent computing center, or until it is determined that the target intelligent computing center exists.

[0064] It should be noted that by continuing to run tasks in the source intelligent computing center, expensive data migration can be avoided when it is not necessary. Migration will only be carried out when the evaluation results show that migration is a more cost-effective option, thereby further effectively controlling economic costs. In addition, the mechanism of regular evaluation can flexibly respond to changing environmental conditions. For example, if the electricity price of an optional intelligent computing center is found to be lower or the network conditions are improved in subsequent evaluations, migration decisions can be made in a timely manner, thereby reducing the economic cost of users using computing resources and realizing the widespread application of inclusive computing power.

[0065] In the present invention, firstly, the alternative intelligent computing centers in different regions are screened to determine the optional intelligent computing center, and the migration cost (determined according to the amount of data to be migrated and the traffic cost) and the subsequent execution cost of the optional intelligent computing center (determined according to the remaining time of task execution and the estimated electricity price of the region) are quantified and compared with the continued execution cost of the source intelligent computing center. When the continued execution cost of the source intelligent computing center is higher than the sum of the minimum execution cost, migration cost and preset threshold of the target intelligent computing center, the intermediate state of the task is migrated to the target intelligent computing center with the best electricity price to continue running.

[0066] Therefore, through the intelligent task migration mechanism, efficient scheduling and optimal configuration of computing resources are achieved. On the premise of ensuring the smooth execution of tasks, the economic cost of users using the computing resources of the intelligent computing center is significantly reduced. It promotes the cross-regional sharing and coordination of computing resources and realizes the widespread application of inclusive computing power.

[0067] Figure 3 A multi-region computing power scheduling device for a universal computing power intelligent computing center provided by the present invention is shown. Figure 3 As shown, the device 30 includes: A determination module 301 is used to determine a task to be migrated, wherein the task to be migrated needs to be executed based on the computing power of the intelligent computing center, and the task to be migrated is currently running on the source intelligent computing center; An execution module 302 is used to determine at least one optional intelligent computing center from among the candidate intelligent computing centers according to the task to be migrated, wherein each candidate intelligent computing center is located in a different preset area; Determining whether there is a target intelligent computing center in at least one optional intelligent computing center according to the task to be migrated; If a target intelligent computing center exists, the current intermediate running result of the task to be migrated is migrated to the target intelligent computing center; Based on the computing power and intermediate operation results of the target intelligent computing center, continue to run the tasks to be migrated; The execution module 302 is further used to calculate the migration cost of migrating the task to be migrated from the source intelligent computing center to each optional intelligent computing center, wherein the migration cost is determined according to the amount of data to be migrated of the task to be migrated and the traffic cost of the amount of data to be migrated; Calculating a first continuing execution cost of the task to be migrated on the source intelligent computing center, wherein the first continuing execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of a preset area where the source intelligent computing center is located; Calculate the second continuing execution cost of the task to be migrated on each optional intelligent computing center respectively, wherein the second continuing execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of the preset area where the corresponding optional intelligent computing center is located; Among the second continuing execution costs, determining a minimum second continuing execution cost; If the first continuing execution cost is greater than the sum of the minimum second continuing execution cost, the migration cost and the preset threshold, it is determined that there is a target intelligent computing center; wherein the target intelligent computing center is an optional intelligent computing center corresponding to the minimum second continuing execution cost; If the first continuing execution cost is less than or equal to the sum of the minimum second continuing execution cost, the migration cost and the preset threshold, it is determined that the target intelligent computing center does not exist.

[0068] In one possible implementation, the execution module 302 is also used to, after determining whether there is a target intelligent computing center in the optional intelligent computing centers based on the task to be migrated, if there is no target intelligent computing center, continue to run the task to be migrated on the source intelligent computing center, and repeat the step of determining whether there is a target intelligent computing center in the optional intelligent computing centers based on the task to be migrated every first preset time period until the task to be migrated is completed in the source intelligent computing center, or until it is determined that the target intelligent computing center exists.

[0069] In a possible implementation, the execution module 302 is further used to determine a requirement tag of the task to be migrated, wherein the requirement tag includes at least one of the following: GPU model, amount of data to be migrated; The optional intelligent computing center is determined based on the demand label and the estimated computing power of each alternative intelligent computing center in the second preset time period in the future, wherein the estimated computing power includes at least one of the following: the GPU model available in the second preset time period in the future and the GPU utilization rate in the second preset time period in the future.

[0070] In one possible implementation, the execution module 302 is also used to determine the task content of the task to be migrated, and the task content includes: training scripts, reasoning codes and container images; on the target intelligent computing center, based on the intermediate running results and task content, continue to run the task to be migrated.

[0071] In the present invention, firstly, the alternative intelligent computing centers in different regions are screened to determine the optional intelligent computing center, and the migration cost (determined according to the amount of data to be migrated and the traffic cost) and the subsequent execution cost of the optional intelligent computing center (determined according to the remaining time of task execution and the estimated electricity price of the region) are quantified and compared with the continued execution cost of the source intelligent computing center. When the continued execution cost of the source intelligent computing center is higher than the sum of the minimum execution cost, migration cost and preset threshold of the target intelligent computing center, the intermediate state of the task is migrated to the target intelligent computing center with the best electricity price to continue running.

[0072] Therefore, through the intelligent task migration mechanism, efficient scheduling and optimal configuration of computing resources are achieved. On the premise of ensuring the smooth execution of tasks, the economic cost of users using the computing resources of the intelligent computing center is significantly reduced. It promotes the cross-regional sharing and coordination of computing resources and realizes the widespread application of inclusive computing power.

[0073] Please refer to Figure 4 The present invention also provides an electronic device 40, including a processor 401, a memory 402, and a computer program stored in the memory 402 and executable on the processor 401. When the computer program is executed by the processor 401, the steps of the multi-region computing power scheduling method for the inclusive computing power intelligent computing center are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0074] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the multi-region computing power scheduling method for the inclusive computing power intelligent computing center are implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0075] The present invention also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the above-mentioned multi-region computing power scheduling method for the universal computing power intelligent computing center and can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0076] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0077] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above method can be implemented by means of software plus a necessary general hardware platform, or by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the method described in the present invention.

[0078] The present invention is described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A multi-region computing power scheduling method for inclusive computing power intelligent computing center, characterized in that: The method comprises: Step S1: determining a task to be migrated, wherein the task to be migrated needs to be executed based on the computing power of the intelligent computing center, and the task to be migrated is currently running on the source intelligent computing center; Step S2: determining at least one optional intelligent computing center from among the candidate intelligent computing centers according to the task to be migrated, wherein each candidate intelligent computing center is located in a different preset area; Step S3: determining whether there is a target intelligent computing center in the at least one optional intelligent computing center according to the task to be migrated; Wherein, the step S3 comprises: Step S31: Calculate the migration cost of migrating the task to be migrated from the source intelligent computing center to each optional intelligent computing center, wherein the migration cost is determined according to the amount of data to be migrated of the task to be migrated and the traffic cost of the amount of data to be migrated; Step S32: Calculate a first continuing execution cost of the task to be migrated on the source intelligent computing center, wherein the first continuing execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of the preset area where the source intelligent computing center is located; Step S33: calculating the second continuing execution cost of the task to be migrated on each optional intelligent computing center respectively, wherein the second continuing execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of the preset area where the corresponding optional intelligent computing center is located; Step S34: determining a minimum second continuing execution cost among the second continuing execution costs; Step S35: If the first continuing execution cost is greater than the sum of the minimum second continuing execution cost, the migration cost and a preset threshold, it is determined that the target intelligent computing center exists; wherein the target intelligent computing center is an optional intelligent computing center corresponding to the minimum second continuing execution cost; Step S36: if the first continuing execution cost is less than or equal to the sum of the minimum second continuing execution cost, the migration cost and the preset threshold, determining that the target intelligent computing center does not exist; Step S4: if the target intelligent computing center exists, migrating the current intermediate running result of the task to be migrated to the target intelligent computing center; Step S5: Based on the computing power of the target intelligent computing center and the intermediate operation results, continue to run the task to be migrated.

2. The method according to claim 1, characterized in that After step S3, the method further includes: Step S6: If the target intelligent computing center does not exist, continue to run the task to be migrated on the source intelligent computing center, and repeat step S3 every first preset time period until the task to be migrated is completed on the source intelligent computing center, or until it is determined that the target intelligent computing center exists.

3. The method according to claim 1, characterized in that The step S2 comprises: Step S21: determining a requirement tag of the task to be migrated, wherein the requirement tag includes at least one of the following: GPU model, amount of data to be migrated; Step S22: Determine the optional intelligent computing center based on the demand label and the estimated computing power of each alternative intelligent computing center in the future second preset time period, wherein the estimated computing power includes at least one of the following: the GPU model available in the future second preset time period and the GPU utilization rate in the future second preset time period.

4. The method according to any one of claims 1 to 3, characterized in that The step S5 comprises: Step S51: determining the task content of the task to be migrated, wherein the task content includes: training script, reasoning code and container image; Step S52: On the target intelligent computing center, based on the intermediate running result and the task content, continue to run the task to be migrated.

5. A multi-region computing power scheduling device for inclusive computing power intelligent computing center, characterized in that: The device comprises: A determination module, used to determine a task to be migrated, wherein the task to be migrated needs to be executed based on the computing power of the intelligent computing center, and the task to be migrated is currently running on the source intelligent computing center; An execution module, configured to determine at least one optional intelligent computing center from among the candidate intelligent computing centers according to the task to be migrated, wherein each candidate intelligent computing center is located in a different preset area; According to the task to be migrated, determining whether there is a target intelligent computing center in the at least one optional intelligent computing center; If the target intelligent computing center exists, migrating the current intermediate running result of the task to be migrated to the target intelligent computing center; Based on the computing power of the target intelligent computing center and the intermediate operation result, continue to run the task to be migrated; The execution module is further used to calculate the migration cost of migrating the task to be migrated from the source intelligent computing center to each optional intelligent computing center, wherein the migration cost is determined according to the amount of data to be migrated of the task to be migrated and the traffic cost of the amount of data to be migrated; Calculating a first continuing execution cost of the task to be migrated on the source intelligent computing center, wherein the first continuing execution cost is determined by a remaining task execution time of the task to be migrated and an estimated electricity price of a preset area where the source intelligent computing center is located; Calculate the second continuing execution cost of the task to be migrated on each optional intelligent computing center respectively, wherein the second continuing execution cost is determined by the remaining task execution time of the task to be migrated and the estimated electricity price of the preset area where the corresponding optional intelligent computing center is located; Among the second continuing execution costs, determining a minimum second continuing execution cost; If the first continuing execution cost is greater than the sum of the minimum second continuing execution cost, the migration cost and a preset threshold, it is determined that the target intelligent computing center exists; wherein the target intelligent computing center is an optional intelligent computing center corresponding to the minimum second continuing execution cost; If the first continuing execution cost is less than or equal to the sum of the minimum second continuing execution cost, the migration cost and the preset threshold, it is determined that the target intelligent computing center does not exist.

6. The device according to claim 5, characterized in that The execution module is further configured to, after determining whether there is a target intelligent computing center in the optional intelligent computing centers according to the task to be migrated, If the target intelligent computing center does not exist, the task to be migrated continues to run on the source intelligent computing center, and the step of determining whether there is a target intelligent computing center in the optional intelligent computing center based on the task to be migrated is repeated every first preset time period until the task to be migrated is completed in the source intelligent computing center, or until it is determined that the target intelligent computing center exists.

7. The device according to claim 5, characterized in that The execution module is further used to determine a requirement tag of the task to be migrated, wherein the requirement tag includes at least one of the following: GPU model and amount of data to be migrated; The optional intelligent computing center is determined based on the demand label and the estimated computing power conditions of each alternative intelligent computing center in the second preset time period in the future, wherein the estimated computing power conditions include at least one of the following: the GPU model available in the second preset time period in the future and the GPU utilization rate in the second preset time period in the future.

8. A server, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the steps of a multi-region computing power scheduling method for a universal computing power intelligent computing center are implemented as described in any one of claims 1 to 4.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a multi-region computing power scheduling method for a universal computing power intelligent computing center as described in any one of claims 1 to 4.

10. A computer program product, characterized in that It includes computer instructions, which, when executed by a processor, implement the steps of a multi-region computing power scheduling method for a universal computing power intelligent computing center as described in any one of claims 1-4.

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