[1]鲁 伟,宋荣方.基于模拟退火的多核多用户任务卸载调度[J].计算机技术与发展,2021,31(06):76-80.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 014]
　LU Wei,SONG Rong-fang.Multi-core Multi-user Task Offloading Scheduling Based onSimulated Annealing Algorithm[J].,2021,31(06):76-80.[doi:10. 3969 / j. issn. 1673-629X. 2021. 06. 014]
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基于模拟退火的多核多用户任务卸载调度()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年06期
页码:
76-80
栏目:
系统工程
出版日期:
2021-06-10

文章信息/Info

Title:
Multi-core Multi-user Task Offloading Scheduling Based onSimulated Annealing Algorithm
文章编号:
1673-629X(2021)06-0076-05
作者:
鲁 伟; 宋荣方
南京邮电大学 通信与信息工程学院,江苏 南京 210003
Author(s):
LU Wei; SONG Rong-fang
School of Telecommunications & Information Engineering,Nanjing University of Posts and Telecommunications, Nanjing 210003,China
关键词:
移动边缘计算; 功率分配; 任务卸载调度策略; 混合流水车间; 模拟退火算法
Keywords:
mobile edge computing;  power allocation;  task offload scheduling strategy;  hybrid flow - shop scheduling;  simulatedannealing algorithm
分类号:
TP39
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 06. 014
摘要:
针对传统的集中式网络架构存在链路负载过重、时延较长的问题,将服务器下放至靠近用户端的移动边缘计算概念孕育而生。 在移动边缘计算系统中,任务卸载调度策略的好坏影响到系统时延和用户体验,因此任务卸载调度问题依旧是移动边缘计算领域中的研究热点。 在移动边缘计算的多用户多核系统中,该文对用户的多个独立任务的调度策略与功率分配进行了研究。 为了降低任务卸载时延,首先利用混合流水车间模型对任务卸载调度策略进行了建模,获得了系统时延的表达式,其次利用模拟退火算法对系统时延与能耗的加权和最小化的问题进行了求解,获得了最优的任务卸载甘特图。 与随机任务卸载调度策略相比,所提的卸载策略可以有效降低系统时延。 最后通过权重的变化,找到一个合适的权重,在不增加时延的情况下,实现了能耗的节约。
Abstract:
In view of the problem of excessive link load and long time delay in the traditional centralized network architecture,the concept of mobile edge computing that the server is decentralized close to the user side was born. In mobile edge computing system,the task offload scheduling strategy has a deep influence on the system delay and user experience. Therefore,task offload scheduling is still a hot topic in the field of mobile edge computing. In the multi-user and multi-core system of mobile edge computing,we study the scheduling strategy and power allocation of multiple independent tasks of users. In order to reduce the task offload delay,firstly,the hybrid flow-shop scheduling is used to model the task offload scheduling strategy and obtain the expression of the system delay. Secondly, the simulated annealing algorithm is used to minimize the weighted sum of the system delay and energy consumption. The optimal task offload Gantt chart was obtained. Compared with the random task offload scheduling strategy, the offload strategy proposed can effectively reduce the system delay. Finally,through the change of the weight,a suitable weight is found,and the energy consumption is saved without increasing the delay.

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更新日期/Last Update: 2021-06-10