Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (11): 3302-3308.DOI: 10.11772/j.issn.1001-9081.2020121932

• Advanced computing • Previous Articles     Next Articles

Task offloading method based on probabilistic performance awareness and evolutionary game strategy in “cloud + edge” hybrid environment

Ying LEI1, Wanbo ZHENG2(), Wei WEI3, Yunni XIA1, Xiaobo LI4, Chengwu LIU5, Hong XIE1   

  1. 1.College of Computer Science,Chongqing University,Chongqing 400044,China
    2.Faculty of Science,Kunming University of Science and Technology,Kunming Yunnan 650500,China
    3.School of Computer Science and Engineering,Xi’an University of Technology,Xi’an Shaanxi 710048,China
    4.Chongqing Animal Husbandry Techniques Extension Center,Chongqing 401121,China
    5.Chongqing Research Institute,Shanghai Jiao Tong University,Chongqing 401135,China
  • Received:2020-12-10 Revised:2021-08-07 Accepted:2021-08-18 Online:2021-08-07 Published:2021-11-10
  • Contact: Wanbo ZHENG
  • About author:LEI Ying,born in 1995,M. S. candidate. Her research interests include cloud computing,edge computing
    ZHENG Wanbo,born in 1981,Ph. D.,associate professor. His research interests include service computing,machine learning,big data,security engineering
    WEI Wei,born in 1978,Ph. D.,associate professor. His research interests include mobile computing,internet of things
    XIA Yunni,born in 1980,Ph. D.,professor. His research interests include service computing,performance evaluation
    LI Xiaobo,born in 1965,senior economist. His research interests include cloud computing,smart agriculture
    LIU Chengwu,born in 1981,M. S.,senior engineer. His research interests include random performance evaluation,service computing
    XIE Hong,born in 1985,Ph. D.,research fellow. His research interests include service computing,complex network analysis.
  • Supported by:
    the Technological Innovation Project of Chongqing Science and Technology Bureau(cstc2019jscx-fxyd0385)

基于概率性能感知演化博弈策略的“云+边”混合环境中任务卸载方法

雷鹰1, 郑万波2(), 魏嵬3, 夏云霓1, 李晓波4, 刘诚武5, 谢洪1   

  1. 1.重庆大学 计算机学院,重庆 400044
    2.昆明理工大学 理学院,昆明 650500
    3.西安理工大学 计算机科学与工程学院,西安 710048
    4.重庆市畜牧技术推广总站,重庆 401121
    5.上海交通大学 重庆研究院,重庆 401135
  • 通讯作者: 郑万波
  • 作者简介:雷鹰(1995—),女,四川绵阳人,硕士研究生,主要研究方向:云计算、边缘计算
    郑万波(1981—),男,四川自贡人,副教授,博 士,主要研究方向:服务计算、机器学习、大数据、安全工程
    魏嵬(1978—),男,陕西西安人,副教授,博士,CCF会员,主要研究方向:移动计算、 物联网
    夏云霓(1980—),男,重庆人,教授,博士,CCF会员,主要研究方向:服务计算、性能评估
    李晓波(1965—),男,四川武圣人,高级经 济师,主要研究方向:云计算、智慧农业
    刘诚武(1981—),男,重庆人,高级工程师,硕士,主要研究方向:随机性能评估、服务计算
    谢洪(1985—),男,重庆人,研究员,博士,CCF会员,主要研究方向:服务计算、复杂网络分析。
  • 基金资助:
    重庆市科技局技术创新项目(cstc2019jscx-fxyd0385)

Abstract:

Aiming at the problem of low multi-task offloading efficiency in the “cloud+edge” hybrid environment composed of “central cloud server and multiple edge servers”, a task offloading method based on probabilistic performance awareness and evolutionary game theory was proposed. Firstly, in a “cloud + edge” hybrid environment composed of “central cloud server and multiple edge servers”, assuming that all the edge servers distributed in it had time-varying volatility performance, the historical performance data of edge cloud servers was probabilistically analyzed by a task offloading method based on probabilistic performance awareness and evolutionary game theory for obtaining the evolutionary game model. Then, an Evolutionary Stability Strategy (ESS) of service offloading was generated to guarantee that each user could offload tasks on the premise of high satisfaction rate. Simulation experiments were carried out based on the cloud edge resource locations dataset and the cloud service performance test dataset, the test and comparison of different methods were carried out on 24 continuous time windows. Experimental results show that, the proposed method is better than traditional task offloading methods such as Greedy algorithm, Genetic Algorithm (GA), and Nash-based Game algorithm in many performance indexes. Compared with the three comparison methods, the proposed method has the average user satisfaction rate higher by 13.7%, 117.0%, 13.8% respectively, the average offloading time lower by 6.5%, 24.9%, 8.3% respectively, and the average monetary cost lower by 67.9%, 88.7%, 18.0% respectively.

Key words: task offloading, Mobile Edge Computing (MEC), evolutionary game theory, probabilistic Quality of Service (QoS), Evolutionary Stability Strategy (ESS)

摘要:

针对“中心云服务器+多个边缘服务器”构成的“云+边”混合环境中多任务卸载效率不足的问题,提出了一种基于概率性能感知演化博弈策略的任务卸载方法。首先,在一个“中心云服务器+多个边缘服务器”构成的“云+边”混合环境中,假设其中分布的边缘服务器具有时变波动的性能,采用一种基于概率性能感知演化博弈策略的任务卸载方法对边缘云服务器的历史性能数据进行概率分析,以获得演化博弈模型;然后,生成服务卸载的演化稳定策略(ESS),使每个用户都能在获得高满意度的前提下进行任务的卸载。基于云边缘资源位置数据集和云服务性能测试数据集进行模拟实验,在24个连续时间窗口上进行不同方法的测试比较。实验结果表明,所提方法在多个性能指标上都优于传统的贪婪(Greedy)算法、遗传算法(GA)和基于纳什均衡的博弈论算法等任务卸载方法。该方法的平均用户期望达成度相较于三个对比方法分别提升了13.7%、117.0%、13.8%,平均卸载时延分别降低了6.5%、24.9%、8.3%,平均货币成本分别降低了67.9%、88.7%、18.0%。

关键词: 任务卸载, 移动边缘计算, 演化博弈论, 概率服务质量, 演化稳定策略

CLC Number: