Task Scheduling and Resource Management Strategy for Edge Cloud Computing Using Improved Genetic Algorithm
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초록
To address the problems of large system overhead and low timeliness when dealing with task scheduling inmobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computingbased on an improved genetic algorithm was proposed. First, a user task scheduling system model based onedge cloud computing was constructed using the Shannon theorem, including calculation, communication, andnetwork models. In addition, a multi-objective optimization model, including delay and energy consumption,was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operationsof the genetic algorithm were improved using the best reservation selection algorithm and normal distributioncrossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objectiveproblem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimentalanalysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss doesnot exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.
Abstract
To address the problems of large system overhead and low timeliness when dealing with task scheduling inmobile edge cloud computing, a task scheduling and resource management strategy for edge cloud computingbased on an improved genetic algorithm was proposed. First, a user task scheduling system model based onedge cloud computing was constructed using the Shannon theorem, including calculation, communication, andnetwork models. In addition, a multi-objective optimization model, including delay and energy consumption,was constructed to minimize the sum of two weights. Finally, the selection, crossover, and mutation operationsof the genetic algorithm were improved using the best reservation selection algorithm and normal distributioncrossover operator. Furthermore, an improved legacy algorithm was selected to deal with the multi-objectiveproblem and acquire the optimal solution, that is, the best computing task scheduling scheme. The experimentalanalysis of the proposed strategy based on the MATLAB simulation platform shows that its energy loss doesnot exceed 50 J, and the time delay is 23.2 ms, which are better than those of other comparison strategies.
참고문헌 (14)
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