Multi-task reinforcement learning method for realizing parallel task scheduling

A technology of reinforcement learning and task scheduling, applied in the fields of information, distribution and parallel computing, it can solve problems such as the difficulty of accurate modeling and the difficulty of heuristic algorithms to show scheduling performance, and achieve the effect of improving generalization.

Inactive Publication Date: 2019-12-17
BEIJING UNIV OF POSTS & TELECOMM
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However, the computing platform environment is always dynamic and large-scale, and it is very difficult to

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  • Multi-task reinforcement learning method for realizing parallel task scheduling
  • Multi-task reinforcement learning method for realizing parallel task scheduling
  • Multi-task reinforcement learning method for realizing parallel task scheduling

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[0038] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0039] see figure 1 , introduce a kind of multi-task reinforcement learning method that realizes parallel task scheduling that the present invention proposes, realize based on the Asynchronous Advantage Actor-Critic algorithm of asynchronous advantage performer critic, described method comprises the following operation steps:

[0040] (1) Perform the following setting operations on the Asynchronous Advantage Actor-Critic algorithm model to better solve the parallel multi-task scheduling problem:

[0041] (1.1) set the state space S as a set, that is: S={F task ,L,T,F node}, where,

[0042] f task ={f 1 , f 2 , f 3 ,..., f M} represents the CPU instruction number of a job, where M is a natural number, representing the maximum number of subtasks of a job; f ...

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Abstract

A multi-task reinforcement learning method for realizing parallel task scheduling is realized based on an asynchronous advantage actor-critic algorithm, and comprises the following operation steps: (1) setting an algorithm model to better solve the parallel multi-task scheduling problem, including setting a state space, setting an action space and setting an award definition; (2) improving the algorithm network as follows: using a deep neural network to represent a strategy function and a value function, wherein the global network is composed of an input layer, a shared sub-network and an output sub-network; (3) setting a new loss function of the algorithm; and (4) training the algorithm network by utilizing the collected and observed parallel task scheduling data, and applying the algorithm network to parallel task scheduling after algorithm convergence.

Description

technical field [0001] The invention relates to a multi-task reinforcement learning method for realizing parallel task scheduling, which belongs to the field of information technology, in particular to the technical field of distribution and parallel computing. Background technique [0002] In the era of data explosion, distribution and parallelization have become an effective way of data processing. Cloud computing, fog computing, edge computing, etc. are all typical distributed and parallel computing environments for big data processing. The computing resources of these computing systems are limited, so the rational allocation of resources is always a crucial research topic. In addition, sustainable development has become a global focus in recent years, and power consumption in computing centers can cause huge energy losses. In view of the above reasons, in a complex dynamic network environment, how to allocate tasks to effectively utilize distributed resources, achieve ...

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Application Information

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Owner BEIJING UNIV OF POSTS & TELECOMM
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