CN116610457B - Resource scheduling method for AI cloud computing server group - Google Patents
Resource scheduling method for AI cloud computing server group Download PDFInfo
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- CN116610457B CN116610457B CN202310891636.9A CN202310891636A CN116610457B CN 116610457 B CN116610457 B CN 116610457B CN 202310891636 A CN202310891636 A CN 202310891636A CN 116610457 B CN116610457 B CN 116610457B
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- 238000012986 modification Methods 0.000 description 3
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- 238000013473 artificial intelligence Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5077—Logical partitioning of resources; Management or configuration of virtualized resources
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5022—Mechanisms to release resources
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5072—Grid computing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
- G06F2209/5011—Pool
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention relates to the field of data processing, and discloses a resource scheduling method for an AI cloud computing server group, which comprises the steps of obtaining historical computing task data of a client in a cloud database, obtaining computing task characteristics of the client according to the historical computing task data of the client, and generating a virtual computing resource pool corresponding to the client according to the computing task characteristics of the client; the method comprises the steps that the occupation of computing resources of computing tasks uploaded by clients is obtained, a computing resource module manager wakes up computing resource modules of the corresponding computing tasks, the computing resource modules apply for the corresponding computing resources from an AI cloud computing server group, the computing tasks are processed, after the processing is completed, the computing resource modules release the computing resources, and in a set period, the capacity of a virtual computing resource pool of each corresponding client is adjusted according to the utilization rate and the utilization ratio, so that the resource scheduling of the AI cloud computing server group is completed. The invention realizes reasonable scheduling of the computational resources.
Description
Technical Field
The invention relates to the field of data processing, in particular to a resource scheduling method for an AI cloud computing server group.
Background
The computing power resource scheduling refers to reasonably distributing and managing available computing resources under a distributed computing environment so as to realize efficient task execution and system performance optimization. In the fields of cloud computing, big data processing, artificial intelligence, distributed computing and the like, computing power resource scheduling is important for improving computing efficiency, reducing cost and improving user experience.
Therefore, how to reasonably schedule the computational resources and maximally utilize the existing computational resources is a problem that needs to be studied by the skilled person.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a resource scheduling method for an AI cloud computing server group, which comprises the following steps:
step one, acquiring historical calculation task data of a client in a cloud database, obtaining calculation task characteristics of the client according to the historical calculation task data of the client, and generating a virtual calculation resource pool corresponding to the client according to the calculation task characteristics of the client;
step two, acquiring the computing resource occupation of the computing task uploaded by the client, if the computing resource occupation is in the capacity range of the virtual computing resource pool of the corresponding client, matching whether a computing resource module of the corresponding computing task exists in the computing resource module sequence of the virtual computing resource pool of the corresponding client, if so, entering the step three, otherwise, entering the step four; if the occupation of the computing resources is not in the capacity range of the virtual computing resource pool of the corresponding client, entering a step five;
thirdly, the computing resource module manager wakes up a computing resource module of a corresponding computing task, the computing resource module applies for the corresponding computing resource from the AI cloud computing server group, processes the computing task, releases the computing resource after the processing is completed, and enters into dormancy to enter into step six;
step four, a computing resource module manager generates a computing resource module of a corresponding computing task in a virtual computing resource pool of a corresponding client, the computing resource module of the corresponding computing task calls computing resources from an AI cloud computing server group, the computing tasks are distributed to the computing resource modules of the corresponding computing tasks, the computing tasks are processed, after the processing is completed, the computing resource modules release the computing resources, the generated computing resource modules of the corresponding computing tasks are stored in a computing resource module sequence of the virtual computing resource pool of the corresponding client, and the step six is entered into;
step five, the computing resource module manager establishes the communication between the temporary computing resource module and the AI cloud computing server group, acquires computing resources corresponding to the computing tasks, processes the computing tasks, releases the computing resources after the processing is completed, and deletes the temporary computing resource module;
and step six, acquiring the utilization rate and the utilization ratio of the computing resources of the virtual computing resource pools of each corresponding client in a set period, and adjusting the capacity of the virtual computing resource pools of each corresponding client according to the utilization rate and the utilization ratio to complete the resource scheduling of the AI cloud computing server group.
Further, the obtaining the computing task characteristics of the client according to the historical computing task data of the client, and generating the virtual computing resource pool corresponding to the client according to the computing task characteristics of the client, includes: and in the set time length, the maximum value of the calculated force used in the calculation task uploaded by the client is the calculation task characteristic of the client, the range from the minimum value of the calculated force used in the calculation task uploaded by the client to the maximum value of the calculated force is the capacity of the virtual calculation resource pool, and the minimum value of the calculated force and the maximum value of the calculated force used in the calculation task uploaded by the client respectively form the lower limit of the calculated force and the upper limit of the calculated force of the virtual calculation resource pool.
Further, the obtaining the computing resource occupation of the computing task uploaded by the client includes obtaining the computing power required by the computing task uploaded by the client, that is, the computing resource occupation.
Further, the computing resource module sequence is the generated computing resource modules, and the computing resource modules are ordered according to the used computing power.
Further, in the set period, the obtaining the usage rate and the usage ratio of the computing resources in the virtual computing resource pool of each corresponding client, and adjusting the capacity of the virtual computing resource pool of each corresponding client according to the usage rate and the usage ratio, includes:
and after the set period is completed, respectively acquiring the calculation force use ranges from the calculation force use minimum value to the calculation force maximum value of the virtual calculation resource pools of the corresponding clients, and adjusting the capacity of the virtual calculation resource pools of the corresponding clients to the calculation force use ranges.
The beneficial effects of the invention are as follows: by the technical scheme provided by the invention, the corresponding computing resource scheduling can be realized for the use conditions of computing resources of different clients, the reasonable scheduling of the computing resources is realized, and the existing computing resources are utilized to the greatest extent.
Drawings
Fig. 1 is a flow chart of a resource scheduling method for an AI cloud computing server group.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the accompanying drawings, but the scope of the present invention is not limited to the following description.
For the purpose of making the technical solution and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention. It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The features and capabilities of the present invention are described in further detail below in connection with the examples.
As shown in fig. 1, a resource scheduling method for an AI cloud computing server group includes the following steps:
step one, acquiring historical calculation task data of a client in a cloud database, obtaining calculation task characteristics of the client according to the historical calculation task data of the client, and generating a virtual calculation resource pool corresponding to the client according to the calculation task characteristics of the client;
step two, acquiring the computing resource occupation of the computing task uploaded by the client, if the computing resource occupation is in the capacity range of the virtual computing resource pool of the corresponding client, matching whether a computing resource module of the corresponding computing task exists in the computing resource module sequence of the virtual computing resource pool of the corresponding client, if so, entering the step three, otherwise, entering the step four; if the occupation of the computing resources is not in the capacity range of the virtual computing resource pool of the corresponding client, entering a step five;
thirdly, the computing resource module manager wakes up a computing resource module of a corresponding computing task, the computing resource module applies for the corresponding computing resource from the AI cloud computing server group, processes the computing task, releases the computing resource after the processing is completed, and enters into dormancy to enter into step six;
step four, a computing resource module manager generates a computing resource module of a corresponding computing task in a virtual computing resource pool of a corresponding client, the computing resource module of the corresponding computing task calls computing resources from an AI cloud computing server group, the computing tasks are distributed to the computing resource modules of the corresponding computing tasks, the computing tasks are processed, after the processing is completed, the computing resource modules release the computing resources, the generated computing resource modules of the corresponding computing tasks are stored in a computing resource module sequence of the virtual computing resource pool of the corresponding client, and the step six is entered into;
step five, the computing resource module manager establishes the communication between the temporary computing resource module and the AI cloud computing server group, acquires computing resources corresponding to the computing tasks, processes the computing tasks, releases the computing resources after the processing is completed, and deletes the temporary computing resource module;
and step six, acquiring the utilization rate and the utilization ratio of the computing resources of the virtual computing resource pools of each corresponding client in a set period, and adjusting the capacity of the virtual computing resource pools of each corresponding client according to the utilization rate and the utilization ratio to complete the resource scheduling of the AI cloud computing server group.
According to the historical calculation task data of the client, obtaining the calculation task characteristics of the client, and generating a virtual calculation resource pool corresponding to the client according to the calculation task characteristics of the client, wherein the method comprises the following steps: and in the set time length, the maximum value of the calculated force used in the calculation task uploaded by the client is the calculation task characteristic of the client, the range from the minimum value of the calculated force used in the calculation task uploaded by the client to the maximum value of the calculated force is the capacity of the virtual calculation resource pool, and the minimum value of the calculated force and the maximum value of the calculated force used in the calculation task uploaded by the client respectively form the lower limit of the calculated force and the upper limit of the calculated force of the virtual calculation resource pool.
The method comprises the steps of obtaining the computing resource occupation of the computing task uploaded by the client, wherein the computing resource occupation comprises the computing power required by the computing task uploaded by the client.
The computing resource module sequence is the generated computing resource modules, and is ordered according to the used computing power, and the ordering mode is descending order from big to small.
In a set period, acquiring the utilization rate and the utilization ratio of the computing resources of the virtual computing resource pools of each corresponding client, and adjusting the capacity of the virtual computing resource pools of each corresponding client according to the utilization rate and the utilization ratio, wherein the method comprises the following steps: and after the set period is completed, respectively acquiring the calculation force use ranges from the calculation force use minimum value to the calculation force maximum value of the virtual calculation resource pools of the corresponding clients, and adjusting the capacity of the virtual calculation resource pools of the corresponding clients to the calculation force use ranges.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (5)
1. The resource scheduling method for the AI cloud computing server group is characterized by comprising the following steps of:
step one, acquiring historical calculation task data of a client in a cloud database, obtaining calculation task characteristics of the client according to the historical calculation task data of the client, and generating a virtual calculation resource pool corresponding to the client according to the calculation task characteristics of the client;
step two, acquiring the computing resource occupation of the computing task uploaded by the client, if the computing resource occupation is in the capacity range of the virtual computing resource pool of the corresponding client, matching whether a computing resource module of the corresponding computing task exists in the computing resource module sequence of the virtual computing resource pool of the corresponding client, if so, entering the step three, otherwise, entering the step four; if the occupation of the computing resources is not in the capacity range of the virtual computing resource pool of the corresponding client, entering a step five;
thirdly, the computing resource module manager wakes up a computing resource module of a corresponding computing task, the computing resource module applies for the corresponding computing resource from the AI cloud computing server group, processes the computing task, releases the computing resource after the processing is completed, and enters into dormancy to enter into step six;
step four, a computing resource module manager generates a computing resource module of a corresponding computing task in a virtual computing resource pool of a corresponding client, the computing resource module of the corresponding computing task calls computing resources from an AI cloud computing server group, the computing tasks are distributed to the computing resource modules of the corresponding computing tasks, the computing tasks are processed, after the processing is completed, the computing resource modules release the computing resources, the generated computing resource modules of the corresponding computing tasks are stored in a computing resource module sequence of the virtual computing resource pool of the corresponding client, and the step six is entered into;
step five, the computing resource module manager establishes the communication between the temporary computing resource module and the AI cloud computing server group, acquires computing resources corresponding to the computing tasks, processes the computing tasks, releases the computing resources after the processing is completed, and deletes the temporary computing resource module;
and step six, acquiring the utilization rate and the utilization ratio of the computing resources of the virtual computing resource pools of each corresponding client in a set period, and adjusting the capacity of the virtual computing resource pools of each corresponding client according to the utilization rate and the utilization ratio to complete the resource scheduling of the AI cloud computing server group.
2. The method for scheduling resources for an AI cloud computing server group according to claim 1, wherein the obtaining computing task characteristics of the client according to historical computing task data of the client, and generating a virtual computing resource pool corresponding to the client according to the computing task characteristics of the client, comprises: and in the set time length, the maximum value of the calculated force used in the calculation task uploaded by the client is the calculation task characteristic of the client, the range from the minimum value of the calculated force used in the calculation task uploaded by the client to the maximum value of the calculated force is the capacity of the virtual calculation resource pool, and the minimum value of the calculated force and the maximum value of the calculated force used in the calculation task uploaded by the client respectively form the lower limit of the calculated force and the upper limit of the calculated force of the virtual calculation resource pool.
3. The method for scheduling resources for an AI cloud computing server group according to claim 2, wherein the obtaining of the computing resource occupation of the computing task uploaded by the client includes obtaining the computing power required by the computing task uploaded by the client, that is, the computing resource occupation.
4. The resource scheduling method for AI-oriented cloud computing server group of claim 3, wherein the sequence of computing resource modules is a sequence of generated computing resource modules, and the sequence is ordered according to the magnitude of the computing power used.
5. The method for scheduling resources for an AI-oriented cloud computing server group of claim 4, wherein the obtaining, in a set period, a computing resource usage rate and a usage ratio of a virtual computing resource pool of each corresponding client, and adjusting a capacity of the virtual computing resource pool of each corresponding client according to the usage rate and the usage ratio, includes:
and after the set period is completed, respectively acquiring the calculation force use ranges from the calculation force use minimum value to the calculation force maximum value of the virtual calculation resource pools of the corresponding clients, and adjusting the capacity of the virtual calculation resource pools of the corresponding clients to the calculation force use ranges.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104461744A (en) * | 2014-12-18 | 2015-03-25 | 曙光云计算技术有限公司 | Resource allocation method and device |
| JP2017142838A (en) * | 2017-04-06 | 2017-08-17 | 華為技術有限公司Ｈｕａｗｅｉ Ｔｅｃｈｎｏｌｏｇｉｅｓ Ｃｏ．，Ｌｔｄ． | Resource allocation method of central processing unit and calculation node |
| CN115328663A (en) * | 2022-10-10 | 2022-11-11 | 亚信科技（中国）有限公司 | Method, device, equipment and storage medium for scheduling resources based on PaaS platform |
| CN115348264A (en) * | 2022-07-28 | 2022-11-15 | 招商局金融科技有限公司 | Multi-tenant cloud service management method, device, equipment and storage medium |
| CN116263701A (en) * | 2022-11-29 | 2023-06-16 | 中移(苏州)软件技术有限公司 | Computing power network task scheduling method and device, computer equipment and storage medium |
| CN116450290A (en) * | 2023-03-24 | 2023-07-18 | 阿里巴巴（中国）有限公司 | Computer resource management method and device, cloud server and storage medium |
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- 2023-07-20 CN CN202310891636.9A patent/CN116610457B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104461744A (en) * | 2014-12-18 | 2015-03-25 | 曙光云计算技术有限公司 | Resource allocation method and device |
| JP2017142838A (en) * | 2017-04-06 | 2017-08-17 | 華為技術有限公司Ｈｕａｗｅｉ Ｔｅｃｈｎｏｌｏｇｉｅｓ Ｃｏ．，Ｌｔｄ． | Resource allocation method of central processing unit and calculation node |
| CN115348264A (en) * | 2022-07-28 | 2022-11-15 | 招商局金融科技有限公司 | Multi-tenant cloud service management method, device, equipment and storage medium |
| CN115328663A (en) * | 2022-10-10 | 2022-11-11 | 亚信科技（中国）有限公司 | Method, device, equipment and storage medium for scheduling resources based on PaaS platform |
| CN116263701A (en) * | 2022-11-29 | 2023-06-16 | 中移(苏州)软件技术有限公司 | Computing power network task scheduling method and device, computer equipment and storage medium |
| CN116450290A (en) * | 2023-03-24 | 2023-07-18 | 阿里巴巴（中国）有限公司 | Computer resource management method and device, cloud server and storage medium |
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