Method and device for collecting computing power operation task resource indexes of intelligent computing center cloud platform

Abstract

The invention provides a computing power operation task resource index collection method and device for an intelligent computing center cloud platform, and the method comprises the steps: S1, receiving a request inputted by a user, and enabling the request to be used for requesting the processing of a computing power operation task; s2, scheduling computing power resources to process the computing power operation task; and S3, when the computing power resource processes the computing power operation task, collecting a resource index corresponding to the computing power resource. Therefore, the computing power operation task resource index acquisition method of the intelligent computing center cloud platform can be provided, and when the computing power resource processes the computing power operation task, the resource indexes consumed in the processing process of the computing power operation task can be acquired in real time, namely, the resource indexes are acquired while the computing power operation task is processed, so that the computing power operation task processing efficiency is improved. Therefore, the accuracy of the collected resource indexes is very high.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent computing centers, intelligent computing center cloud platforms and computing power infrastructure, and in particular to a method and device for collecting computing power operation task resource indicators of an intelligent computing center cloud platform. Background Art

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

[0003] An "Intelligent Computing Center" is a facility that uses large-scale heterogeneous computing resources, including general-purpose and intelligent computing power, to provide the computing power, data, and algorithms required for AI applications (such as AI deep learning model development, model training, and model inference). The Intelligent Computing Center encompasses facilities, hardware, and software, and provides a full stack of 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 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.

[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 perform certain computing needs. It is 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. It mainly provides services to society through computing power infrastructure.

[0007] When the current intelligent computing center receives a request for processing a computing power running task, it usually needs to process the above computing power running task according to the request. However, when processing the above computing power running task, the current intelligent computing center lacks a method for collecting the computing power resource indicators consumed by the computing power running task. It can be seen that since the emergence of the intelligent computing center, the lack of a method for collecting the computing power resource indicators consumed by the computing power running task is a problem that needs to be solved urgently. Summary of the Invention

[0008] The present invention provides a method and device for collecting computing power operation task resource indicators of an intelligent computing center cloud platform, which are used to solve the problem of lack of a method for collecting computing power resource indicators consumed by computing power operation tasks.

[0009] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0010] In a first aspect, the present invention provides a method for collecting computing power operation task resource indicators of an intelligent computing center cloud platform, comprising:

[0011] Step S1: receiving a request input by a user, wherein the request is used to request processing computing power to run a task;

[0012] Step S2: Dispatching computing resources to process the computing operation task;

[0013] Step S3: When the computing power resource processes the computing power operation task, resource indicators corresponding to the computing power resource are collected.

[0014] Optionally, step S3 includes:

[0015] Step S31: When the computing power resource processes the computing power operation task, a first resource indicator corresponding to the computing power resource is collected, where the first resource indicator is a portion of the resource indicators;

[0016] Step S32: Calculate a second resource indicator based on the first resource indicator, where the second resource indicator is an indicator of the resource indicators other than the first resource indicator.

[0017] Optionally, step S32 includes:

[0018] Step S321: Obtain a calculation model, where the calculation model is a trained model used to calculate resource indicators;

[0019] Step S322: input the first resource indicator into the calculation model, and obtain the second resource indicator output by the calculation model.

[0020] Optionally, the resource indicators include: computing resource indicators, storage resource indicators and network resource indicators.

[0021] Optionally, step S2 includes:

[0022] Step S21: Obtaining a scheduling plan corresponding to the computing power operation task, where the scheduling plan is a scheduling plan for computing power resources used to process the computing power operation task;

[0023] Step S22: Scheduling the computing resources to process the computing operation tasks according to the scheduling plan.

[0024] Optionally, after step S3, the method further includes:

[0025] Step S4: generating a scheduling solution optimization suggestion based on the resource indicators;

[0026] Step S5: Modifying the scheduling plan according to the scheduling plan optimization suggestion;

[0027] Step S6: Scheduling computing resources to process the computing operation task according to the revised scheduling plan.

[0028] In a second aspect, the present invention provides a computing power operation task resource indicator collection device for an intelligent computing center cloud platform, comprising:

[0029] A receiving module, configured to receive a request input by a user, wherein the request is used to request processing of a computing power execution task;

[0030] A first scheduling module is used to schedule computing resources to process the computing operation task;

[0031] The collection module is used to collect resource indicators corresponding to the computing power resources when the computing power resources process the computing power operation tasks.

[0032] In a third aspect, the present invention provides an electronic device comprising: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, the steps of the method for collecting computing power operation task resource indicators of the intelligent computing center cloud platform as described in the first aspect above are implemented.

[0033] 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 the method for collecting computing power operation task resource indicators of the intelligent computing center cloud platform as described in the first aspect above are implemented.

[0034] 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 the method for collecting computing power operation task resource indicators of the intelligent computing center cloud platform as described in the first aspect above.

[0035] In the present invention, a user input request is received, the request being used to request the processing of a computing power operation task; computing power resources are dispatched to process the computing power operation task; and resource indicators corresponding to the computing power resources when the computing power resources process the computing power operation task are collected. In this way, a method for collecting computing power operation task resource indicators for an intelligent computing center cloud platform can be provided. When computing power resources process computing power operation tasks, resource indicators consumed during the processing of computing power operation tasks can be collected in real time, that is, resource indicators are collected while computing power operation tasks are processed, thereby ensuring that the accuracy of the collected resource indicators is very high. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0037] Figure 1 A flowchart of a method for collecting computing power operation task resource indicators of an intelligent computing center cloud platform provided by the present invention;

[0038] Figure 2 A schematic diagram of the structure of a computing power operation task resource indicator collection device for an intelligent computing center cloud platform provided by the present invention;

[0039] Figure 3 This is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0040] The following is a clear and complete description of the technical solutions of the present invention, in conjunction with the accompanying drawings. Obviously, the description is only a portion of the present invention, not all of it. All other contents derived by persons of ordinary skill in the art based on the contents of the present invention without inventive effort are within the scope of protection of the present invention.

[0041] The "computing power" mentioned in the present invention refers to: the ability of computer equipment or computing / data centers 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 mainly provides services to society through computing power infrastructure.

[0042] The "computing power" (Computational Power, CP) mentioned in the present invention refers to: the ability of a data center server to process data and output results. It is a comprehensive indicator to measure 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=10^18FLOPS). The larger the value, the stronger the comprehensive computing power. According to calculations, 1EFLOPS 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 通用 +CP 智能 +CP 超级 .

[0043] The "carrying capacity" (Network Power, NP) mentioned in the present invention refers to: it is the performance of the data transmission capability of the computing power facility, which includes comprehensive capabilities such as network architecture, network bandwidth, transmission latency, 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.

[0044] The "Storage Power" (SP) described in this invention refers to the comprehensive capabilities of a data center in terms of data storage capacity, performance, security and reliability, and environmental friendliness. It is a comprehensive indicator for measuring a data center's data storage capacity, encompassing both external storage devices such as storage arrays and internal server storage. Storage capacity is commonly measured in exabytes (EB, 1EB = 2^60 bytes), while performance is commonly measured in IOPS / TB (Input / Output Operations Per Second / TB). Disaster recovery ratio is a key indicator of security and reliability.

[0045] 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, and can realize the centralized calculation, storage, transmission and application of information.

[0046] 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.

[0047] The "computing power" mentioned in the present invention includes: general computing power, intelligent computing power and super computing power.

[0048] 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.

[0049] The "intelligent computing power" mentioned in this invention refers to: a computing platform based on specialized chips such as GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), and ASIC (Application Specific Integrated Circuit) for various innovative artificial intelligence applications, such as natural language processing and machine vision.

[0050] The "supercomputing power" mentioned in the present invention refers to the computing power provided by high-performance computing clusters such as supercomputers. It utilizes 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, genetic analysis, etc.

[0051] The "intelligent computing center" described in this article refers to a facility that provides the computing power, data, and algorithms required for artificial intelligence applications (such as AI deep learning model development, model training, and model inference) by utilizing large-scale heterogeneous computing resources, including general-purpose computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.). The intelligent computing center encompasses facilities, hardware, and software, and can provide a full stack of capabilities, from bottom-level computing power to top-level application enablement.

[0052] The "intelligent computing center cloud platform" mentioned in the present invention refers to: a cloud computing platform that provides comprehensive services based on the hardware resources and software resources of the intelligent computing center.

[0053] The "intelligent computing center" mentioned in the present invention includes but is not limited to the "intelligent computing center".

[0054] The "intelligent computing center" mentioned in the present invention is an artificial intelligence computing center, which is a type of computing power infrastructure 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.

[0055] 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.

[0056] 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 large-scale computing, storage and network services and other functions, and is widely used in application scenarios such as aerospace, national defense, oil exploration, climate modeling and genome sequencing.

[0057] The "computing resources" mentioned in the present invention refer to: technologies and facilities with information calculation, 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.

[0058] The “model” mentioned in the present invention includes but is not limited to a “large language model” and a “multimodal large model”.

[0059] The "large language model" mentioned in the present invention refers to a large language model (LLM), which is a language model with a large parameter scale. It is designed to understand and generate human language. It is trained with a large amount of text data and can perform a wide range of tasks including text summarization, translation, sentiment analysis, etc.

[0060] The "Multimodal Large Models" mentioned in the present invention refer to models that combine multimodal information such as text, images, video, and audio for training, including but not limited to multimodal large language models.

[0061] The "computing power operation task" mentioned in the present invention refers to: a specific workload or job executed on computing power resources that requires a certain amount of computing power support, usually involving complex data processing, numerical calculations, model training or simulation scenarios.

[0062] See Figure 1 , Figure 1 This is a flow chart of a computing power scheduling method for an intelligent computing center cloud platform provided by the present invention. Figure 1 As shown, the following steps are included:

[0063] Step S1: Receive a request input by a user, wherein the request is used to request processing computing power to run a task.

[0064] Among them, the specific method of receiving the request is not limited here. Optionally, the present invention can be applied to an electronic device, and the electronic device can be called an intelligent computing center cloud platform. The above request can be a request input by the user on the intelligent computing center cloud platform. It should be noted that the specific method of the user inputting on the intelligent computing center cloud platform is not limited here. For example: the method of the user inputting on the intelligent computing center cloud platform can include touch input, press input or voice input.

[0065] Alternatively, the user may input a request through other electronic devices, which may be electrically connected to the intelligent computing center cloud platform. In this way, the user may input on the other electronic devices, thereby causing the other electronic devices to send a request to the intelligent computing center cloud platform.

[0066] For example, artificial intelligence (AI) models may be applied to other electronic devices, and users may input requests through the AI ​​models, thereby improving the accuracy and intelligence of the input requests.

[0067] For example, a user can input description information of a computing power running task into an AI model, and the AI ​​model generates a request and a computing power running task based on the above description information, and sends the above request to the electronic device in the present invention to request the electronic device in the present invention to process the above computing power running task. In this way, the user only needs to input description information, and the AI ​​model can automatically generate and send a request based on the description information, which simplifies the user's operation and, at the same time, improves the accuracy of the request and computing power running task generation results and the intelligence of the generation method.

[0068] The computing power operation task is not specifically limited here. Optionally, the computing power operation task can be a model training task, and the type of the above-mentioned model is not limited here. For example, the above-mentioned model can be a navigation model, a computing model, a large language model, etc. Optionally, the computing power operation task can be a data processing task, for example, a task for processing navigation data or a task for processing computing data.

[0069] Step S2: Scheduling computing resources to process the computing operation task.

[0070] The types of computing resources are not specifically limited here. Optionally, the computing resources may include computing resources corresponding to a central processing unit (CPU), a graphics processing unit (GPU), and memory.

[0071] Step S3: When the computing power resource processes the computing power operation task, resource indicators corresponding to the computing power resource are collected.

[0072] When computing resources process computing power operation tasks, the specific method of collecting resource indicators corresponding to computing resources is not limited here. It should be noted that the above resource indicators can also be referred to as computing power resource indicators.

[0073] Optionally, when computing power resources process computing power running tasks, resource indicators corresponding to the computing power resources can be collected in real time; optionally, when computing power resources process computing power running tasks, resource indicators corresponding to the computing power resources can be collected every preset period, and the preset period can be determined based on the computing power resources. For example: the more types of computing power resources included, the larger the preset period can be; that is, when the computing power resources include CPU, GPU and memory at the same time, the corresponding preset period can be greater than the corresponding preset period when the computing power resources include GPU and memory at the same time.

[0074] Optionally, the collection content and collection method of the resource indicators corresponding to the computing power resources can be determined based on the processing progress of the computing power operation task. For example, when the processing progress of the computing power operation task is less than the first progress, it means that the processing progress of the computing power operation task is still relatively small, that is, the utilization rate of the computing power resources is still very low, and the content richness of the resource indicators is also relatively low. Therefore, at this time, all the resource indicators corresponding to the computing power resources can be obtained and determined as resource indicators. In this way, the accuracy of the resource indicators finally determined can be improved. In addition, the collection method of all the above resource indicators can be traversal collection. In this way, the occurrence of resource indicators being missed can be reduced, and the comprehensiveness of resource indicator collection can be improved.

[0075] For another example: when the processing progress of the computing power running task is greater than the second progress, and the second progress is greater than or equal to the first progress, it means that the processing progress of the computing power running task is larger at this time, that is, the utilization rate of the computing power resources is higher, and the content richness of the resource indicators is also higher. Therefore, some types of resource indicators corresponding to the computing power resources can be screened, and the above-mentioned some types of resource indicators can be determined as resource indicators. The above-mentioned some types of resource indicators can include resource indicators whose quality information is greater than the first preset quality indicator and resource indicators whose quality information is less than the second preset quality indicator. The resource indicators whose quality information is greater than the first preset quality indicator can be understood as resource indicators with better quality, and the resource indicators whose quality information is less than the second preset quality indicator can be understood as resource indicators with poorer quality. In this way, by analyzing the causes of the generation of resource indicators with better quality and the causes of the generation of resource indicators with poor quality, the process of computing power resources processing computing power running tasks can be corrected to reduce the occurrence of resource indicators with poor quality.

[0076] It should be noted that when the processing progress of the computing power running task is greater than the second progress, the method for collecting the above-mentioned some types of resource indicators can be to first screen the resource indicators corresponding to the processing progress of the computing power running task after the first progress, and then screen the above-mentioned some types of resource indicators from the resource indicators corresponding to the first progress. In this way, the accuracy of the above-mentioned some types of resource indicators can be higher and the screening efficiency can be higher.

[0077] In the present invention, through steps S1 to S3, a request input by a user is received, wherein the request is used to request the processing of a computing power operation task; computing power resources are dispatched to process the computing power operation task; and when the computing power resources process the computing power operation task, resource indicators corresponding to the computing power resources are collected. In this way, a method for collecting computing power operation task resource indicators of an intelligent computing center cloud platform can be provided, and when the computing power resources process the computing power operation task, resource indicators consumed during the processing of the computing power operation task can be collected in real time, that is, resource indicators are collected while the computing power operation task is processed, thereby making the accuracy of the collected resource indicators very high and also improving the efficiency of collecting resource indicators.

[0078] It should be noted that by collecting resource indicators and analyzing the above resource indicators, it is possible to analyze the computing power operation task resource indicators of the intelligent computing center cloud platform, that is, to achieve better analysis performance of the computing power operation task resource indicators of the intelligent computing center cloud platform.

[0079] In addition, in order to enhance the collection and analysis effects of the above-mentioned resource indicators, the above-mentioned resource indicators can be displayed in real time on the display interface of the intelligent computing center cloud platform, so that the staff can obtain the above-mentioned resource indicators accurately and quickly, thereby enhancing the efficiency of the staff in obtaining the above-mentioned resource indicators.

[0080] It should be noted that when the resource indicators include multiple resource indicators, the collection method of the above-mentioned multiple resource indicators is not specifically limited here. Optionally, the above-mentioned multiple resource indicators can be collected separately, and the collection method of each resource indicator is different, and the resource indicator can be collected using the collection method corresponding to each resource indicator. For example: when multiple resource indicators include network resource indicators and computing resource indicators, the network resource indicators can be collected through network monitoring sensors, and the computing resource indicators can be collected through computing resource indicator monitoring sensors.

[0081] Optionally, step S3 includes:

[0082] Step S31: When the computing power resource processes the computing power operation task, a first resource indicator corresponding to the computing power resource is collected, where the first resource indicator is a portion of the resource indicators;

[0083] Step S32: Calculate a second resource indicator based on the first resource indicator, where the second resource indicator is an indicator of the resource indicators other than the first resource indicator.

[0084] Among them, optionally, the first resource indicator can be an indicator obtained by randomly allocating resource indicators in the resource indicators; optionally, the first resource indicator can be an indicator that is more convenient to obtain directly from the resource indicators, and the second resource indicator can be an indicator that is more convenient to calculate directly from the resource indicators. For example: the first resource indicator can be information such as the number and model of CPUs, GPUs and memories, and the second resource indicator can be information such as the utilization rate of each CPU, each GPU and each memory, the time period with higher frequency of use, the time period prone to failure, etc.

[0085] In the present invention, the first resource indicator is collected and the second resource indicator is calculated based on the first resource indicator. In this way, compared with the method in which both the second resource indicator and the first resource indicator are obtained through collection, the present invention can reduce the computing resources consumed when collecting the second resource indicator and the first resource indicator. At the same time, it can also increase the rate at which the second resource indicator and the first resource indicator are obtained.

[0086] Optionally, step S32 includes:

[0087] Step S321: Obtain a calculation model, where the calculation model is a trained model used to calculate resource indicators;

[0088] Step S322: input the first resource indicator into the calculation model, and obtain the second resource indicator output by the calculation model.

[0089] The specific type of the computing model is not limited here. Optionally, the computing model may be an artificial intelligence (AI) model; further optionally, the computing model may be a large language model (LLM) model.

[0090] It should be noted that, optionally, sample data can be used to train an iterative AI model or LLM model, so that the AI ​​model or LLM model after the training iteration can more accurately calculate the second resource indicator, and the above-mentioned sample data and the first resource indicator can be homologous data, and the homologous data may include: data generated in the same usage scenario, or data belonging to the same intelligent computing center cloud platform.

[0091] In the present invention, the first resource indicator is input into the calculation model, and the second resource indicator output by the calculation model is obtained, that is, the first resource indicator is input into the calculation model, and the second resource indicator is calculated by the calculation model. In this way, the calculation efficiency and accuracy of the second resource indicator can be further improved.

[0092] Optionally, the resource indicators include computing resource indicators, storage resource indicators, and network resource indicators. This further increases the diversity and flexibility of resource indicators, thereby achieving better collection effects on resource indicators.

[0093] For example, resource indicators may include computing resource indicators, storage resource indicators, and network resource indicators, and computing resource indicators may include at least one of the following: CPU utilization and GPU utilization;

[0094] The storage resource indicator may include at least one of the following: memory quota (menory Limit), memory usage (menory usage), memory failure count (Memory Failcnt);

[0095] The network resource indicator may include at least one of the following: network error, network bandwidth usage, network socket, file system, network delay rate, network throughput, and other indicators.

[0096] Optionally, step S2 includes:

[0097] Step S21: Obtaining a scheduling plan corresponding to the computing power operation task, where the scheduling plan is a scheduling plan for computing power resources used to process the computing power operation task;

[0098] Step S22: Scheduling the computing resources to process the computing operation tasks according to the scheduling plan.

[0099] Among them, the specific content included in the scheduling plan is not limited here. Optionally, the computing power operation task includes multiple subtasks, and the scheduling plan includes: scheduling the first CPU to process the first subtask among the multiple subtasks at a first moment, scheduling the first GPU to process the second subtask among the multiple subtasks at a second moment, scheduling the second CPU to process the third subtask among the multiple subtasks at a third moment, and scheduling the second GPU to process the fourth subtask among the multiple subtasks at a fourth moment.

[0100] It should be noted that, optionally, the above-mentioned first subtask, second subtask, third subtask and fourth subtask can be executed in parallel, that is, the first moment, the second moment, the third moment and the fourth moment can be the same moment; optionally, the above-mentioned first subtask, second subtask, third subtask and fourth subtask can be executed linearly, that is, the first moment, the second moment, the third moment and the fourth moment can be increasing or decreasing in sequence.

[0101] Optionally, the scheduling plan may include a computing power resource allocation plan and a computing power resource scheduling plan. The computing power resource allocation plan is a strategy for allocating computing power resources to computing power running tasks, and the computing power resource scheduling plan is a strategy for scheduling the computing power resources allocated to the computing power running tasks to process the computing power running tasks. The scheduling plan optimization suggestions in the following text may be optimization suggestions for at least one of the computing power resource allocation plan and the computing power resource scheduling plan.

[0102] For example, a computing resource allocation plan can be used to determine the types and quantities of CPUs, GPUs, and memories allocated to computing tasks, while a computing resource scheduling plan can be used to determine the scheduling time, scheduling order, etc. of the CPUs, GPUs, and memories allocated to computing tasks.

[0103] In the present invention, computing power resources are scheduled to process computing power operation tasks according to a scheduling plan, so that the processing effect of computing power resources in processing computing power operation tasks can be higher.

[0104] Optionally, after step S3, the method further includes:

[0105] Step S4: generating a scheduling solution optimization suggestion based on the resource indicators;

[0106] Step S5: Modifying the scheduling plan according to the scheduling plan optimization suggestion;

[0107] Step S6: Scheduling computing resources to process the computing operation task according to the revised scheduling plan.

[0108] Among them, for example: when the network resource indicator included in the resource indicator is too high, it means that the network condition is poor at this time, so a scheduling scheme optimization suggestion can be generated. The scheduling scheme optimization suggestion indicates that it is recommended to replace other interfaces with better network conditions to schedule network resources to process computing power operation tasks. In this way, when the computing power resources are scheduled to process computing power operation tasks through the revised scheduling scheme, the processing efficiency of computing power operation tasks can be improved.

[0109] For example, if a resource indicator included in the resource metrics is too high, it indicates that the computing resources scheduled for processing the computing task are unreasonable. Therefore, the scheduling plan can be revised to ensure the stable operation of the computing task. Optionally, if it is determined that revising the scheduling plan cannot improve the processing efficiency of the computing task, the computing resources used to process the computing task can also be revised to improve the processing efficiency of the computing task.

[0110] In the present invention, computing power resources are scheduled to process computing power operation tasks according to the revised scheduling scheme, thereby further improving the processing efficiency of computing power operation tasks.

[0111] See also Figure 2 , Figure 2 This is a structural diagram of a computing power operation task resource indicator collection device for an intelligent computing center cloud platform provided by the present invention, such as Figure 2 As shown, the computing power operation task resource indicator collection device 200 of the intelligent computing center cloud platform includes:

[0112] Receiving module 201, configured to receive a request input by a user, wherein the request is for requesting processing of a computing power execution task;

[0113] A first scheduling module 202 is used to schedule computing resources to process the computing operation task;

[0114] The collection module 203 is used to collect resource indicators corresponding to the computing power resources when the computing power resources process the computing power operation tasks.

[0115] Optionally, the acquisition module 203 includes:

[0116] a collection submodule, configured to collect a first resource indicator corresponding to the computing power resource when the computing power resource processes the computing power operation task, where the first resource indicator is a portion of the resource indicators;

[0117] The calculation submodule is configured to calculate a second resource indicator based on the first resource indicator, where the second resource indicator is an indicator of the resource indicators other than the first resource indicator.

[0118] Optionally, the computing submodule includes:

[0119] A first acquisition unit is used to acquire a calculation model, where the calculation model is a trained model for calculating resource indicators;

[0120] The second acquisition unit is configured to input the first resource indicator into the calculation model and acquire the second resource indicator output by the calculation model.

[0121] Optionally, the resource indicators include: computing resource indicators, storage resource indicators and network resource indicators.

[0122] Optionally, the first scheduling module 202 includes:

[0123] An acquisition submodule is used to obtain a scheduling scheme corresponding to the computing power operation task, where the scheduling scheme is a scheduling scheme for computing power resources used to process the computing power operation task;

[0124] The scheduling submodule is used to schedule the computing power resources to process the computing power operation tasks according to the scheduling plan.

[0125] Optionally, it also includes:

[0126] A generation module, configured to generate scheduling scheme optimization suggestions based on the resource indicators;

[0127] A correction module, configured to correct the scheduling plan according to the scheduling plan optimization suggestion;

[0128] The second scheduling module is used to schedule computing resources to process the computing power operation task according to the revised scheduling plan.

[0129] The computing power operation task resource indicator collection device 200 of the intelligent computing center cloud platform provided by the present invention can execute each step of the computing power operation task resource indicator collection method of the above-mentioned intelligent computing center cloud platform, and thus has the same beneficial technical effect as the computing power operation task resource indicator collection method of the above-mentioned intelligent computing center cloud platform, and the details will not be repeated here.

[0130] Please refer to Figure 3 The present invention also provides an electronic device 30, including a processor 31, a memory 32, and a computer program stored in the memory 32 and executable on the processor 31. When the computer program is executed by the processor 31, the various processes shown in the above-mentioned method for collecting computing power operation task resource indicators of the intelligent computing center cloud platform are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be described here.

[0131] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements each process of the method for collecting computing power operation task resource indicators of the intelligent computing center cloud platform, and can achieve the same technical effect. To avoid repetition, the details are not described 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.

[0132] The present invention also provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the method for collecting computing power operation task resource indicators of the intelligent computing center cloud platform shown in the figure can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0133] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0134] Through the description of the above embodiments, those skilled in the art can clearly understand that the method provided by the above invention can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in many cases the former is a better embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the various methods provided by the present invention.

[0135] The present invention is described above with reference to 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 guidance 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 protected by the present invention.

Claims

1. A method for collecting computing power operation task resource indicators of an intelligent computing center cloud platform, characterized in that: include: Step S1: receiving a request input by a user, wherein the request is used to request processing computing power to run a task; Step S2: Dispatching computing resources to process the computing operation task; Step S3: When the computing power resource processes the computing power operation task, resource indicators corresponding to the computing power resource are collected.

2. The method according to claim 1, characterized in that The step S3 comprises: Step S31: When the computing power resource processes the computing power operation task, a first resource indicator corresponding to the computing power resource is collected, where the first resource indicator is a portion of the resource indicators; Step S32: Calculate a second resource indicator based on the first resource indicator, where the second resource indicator is a resource indicator other than the first resource indicator.

3. The method according to claim 2, characterized in that The step S32 includes: Step S321: Obtain a calculation model, where the calculation model is a trained model used to calculate resource indicators; Step S322: input the first resource indicator into the calculation model, and obtain the second resource indicator output by the calculation model.

4. The method according to claim 1, wherein The resource indicators include: computing resource indicators, storage resource indicators and network resource indicators.

5. The method according to any one of claims 1 to 4, characterized in that The step S2 comprises: Step S21: Obtaining a scheduling plan corresponding to the computing power operation task, where the scheduling plan is a scheduling plan for computing power resources used to process the computing power operation task; Step S22: Scheduling the computing resources to process the computing operation tasks according to the scheduling plan.

6. The method according to claim 5, characterized in that After step S3, the method further includes: Step S4: generating a scheduling solution optimization suggestion based on the resource indicators; Step S5: Modifying the scheduling plan according to the scheduling plan optimization suggestion; Step S6: Scheduling computing resources to process the computing operation task according to the revised scheduling plan.

7. A computing power operation task resource index collection device for an intelligent computing center cloud platform, characterized in that: include: A receiving module, configured to receive a request input by a user, wherein the request is used to request processing of a computing power execution task; A first scheduling module is used to schedule computing resources to process the computing operation task; The collection module is used to collect resource indicators corresponding to the computing power resources when the computing power resources process the computing power operation tasks.

8. An electronic device, 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 the method for collecting computing power operation task resource indicators of the intelligent computing center cloud platform as described in any one of claims 1 to 6 are implemented.

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 the method for collecting computing power operation task resource indicators of the intelligent computing center cloud platform according to any one of claims 1 to 6.

10. A computer program product, characterized in that It includes computer instructions, which, when executed by a processor, implement the steps of the method for collecting computing power operation task resource indicators of an intelligent computing center cloud platform as described in any one of claims 1 to 6.

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