Multi-task reinforcement learning method for realizing parallel task scheduling

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 ...

AI Technical Summary

Problems solved by technology

However, the computing platform environment is always dynamic and large-scale, and it is very difficult to accurately model them, so it is difficult for heuristic algorithms to show excellent scheduling performance

Images