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Distributed resource dynamic allocation method based on evolutionary game theory

A distributed resource and dynamic allocation technology, applied in resource allocation, program control design, instruments, etc., can solve problems such as insufficient consideration of balanced use of resources, lack of consideration of psychological expectations, single game process, etc., and achieve information processing capabilities. The effect of small requirements, improved robustness, and improved resource allocation efficiency

Active Publication Date: 2020-02-21
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] However, the existing game process only makes decisions based on game opponents, the game process is single, and psychological expectations are not considered
Moreover, the existing game models pay more attention to satisfying the individual's own needs, and the balanced use of resources has not been fully considered
In the previous game model, the exchange with a price ratio of 1:1 cannot make the result of resource allocation very balanced

Method used

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  • Distributed resource dynamic allocation method based on evolutionary game theory
  • Distributed resource dynamic allocation method based on evolutionary game theory
  • Distributed resource dynamic allocation method based on evolutionary game theory

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0066] Such as figure 1 As shown, this embodiment proposes a method for dynamically allocating distributed resources based on evolutionary game theory, including:

[0067] S1. Determine whether the supply and demand of the physical machine and the user task match, if yes, execute step S2, if not; execute step S3;

[0068] The two main categories of resource allocation are: the owner of various resources - physical machine, and the resource demander - user task. Resource resource allocation is to match the resources owned by the physical machine with the resources required by user tasks, so as to allocate the resources of the physical machine to the corresponding user tasks.

[0069] Specifically, the resource amount of physical machine i is P i =[p i1 ,p i2 ,...,p im , d i1 , d i2 ,...,d im ], where m is the number of resource types, p ij is the possession of resource j by physical machine i, d ij is the usage amount of resource j by physical machine i. The resource...

Embodiment 2

[0111] Such as image 3 As shown, on the basis of the first embodiment, this embodiment adds a game learning step. After allocating resources to user tasks, using the resource allocation results, the physical machine and user tasks learn from other subjects of the same kind to optimize the game process. Specifically:

[0112] S5. Calculate the fitness of the physical machine and user tasks;

[0113] Fitness reflects the result of resource allocation. For a physical machine, the higher the fitness, the more efficiently its resources are used, and the lower the fitness, the worse the resources are used. For user tasks, the higher the fitness, the more balanced resources the user gets, and vice versa, the less balanced resources the user gets. Therefore, the present invention calculates the fitness of the physical machine and the user task to evaluate the game result of the physical machine and the user task, and guides the next game process according to the feedback of the g...

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Abstract

The invention discloses a distributed resource dynamic allocation method based on an evolutionary game theory, which comprises the following steps: S1, judging whether supply and demand of a physicalmachine and a user task are matched, if yes, executing the step S2, and if not, executing the step S3; S2, performing a game between the physical machine and the user task based on the price bargaining strategy of the fraudulent sound potential behavior, judging whether the transaction between the physical machine and the user task can be successful, and if yes, executing the step S4; otherwise, executing the step S3; S3, acquiring a new physical machine or user task, and executing the step S1; and S4, determining a resource exchange price ratio, and allocating resources to the user task. According to the method, a bargaining phenomenon commonly existing in real life is added in a game link; according to the method, the fitness function is combined, so that the main body repeatedly plays agame with the purposes of resource balance and efficient resource utilization in the game link, the strategy of individuals with higher fitness in similar main bodies is learned in the evolutionary learning link, and the excellent performance of the group in the aspects of configuration efficiency, resource balance and the like is continuously promoted.

Description

technical field [0001] The invention relates to the field of resource allocation, in particular to a distributed resource dynamic allocation method based on evolutionary game theory. Background technique [0002] Cloud computing is to distribute computing tasks on a resource pool composed of a large number of computers, so that various application systems can obtain computing power, storage space and information services as needed. Because cloud computing is in a dynamic environment, and the group of cloud users is very large, the resource requirements of each user's application program are constantly changing, that is, the dynamic requirements of resources must meet their performance requirements . The continuous change of application resource requirements is at any time, without prior warning and unpredictable, making it impossible for suppliers to find rules and grasp them. In addition, in a multi-user environment, there are different user applications running on the sa...

Claims

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Application Information

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IPC IPC(8): G06F9/50
CPCG06F9/5027G06F9/5072
Inventor 柴跃廷孙骁钱君
Owner TSINGHUA UNIV
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