1. 湖北民族学院 机构 信息工程学院,湖北,恩施,445000
2. 华中科技大学 计算机科学与技术学院,湖北,武汉,430074
纸质出版:2017
移动端阅览
段昌敏, 沈济南, 周慧华. 一种高效云任务调度博弈算法[J]. 微电子学与计算机, 2017, 34(3): 40-45.
DUAN Chang-min, SHEN Ji-nan, ZHOU Hui-hua. An Efficient Cloud Tasks Scheduling Game Algorithm[J]. 2017, 34(3): 40-45.
为了更加高效安全的进行云任务调度
提出一种考虑资源可靠性的云任务调度博弈算法.该算法将任务执行代价、资源利用代价、安全可信代价和负载均衡代价综合考虑到博弈效用函数中
以最优化博弈效用函数为目标
将云任务调度问题形式化为非合作博弈模型进行求解.算法求解过程划分为两个层次
并分别利用GA算法和改进MCT算法对其进行求解
最终得到非合作博弈的纳什均衡.实验结果表明
算法在保证任务完成时间和任务响应时间的同时
可以降低任务执行的失效率
并在不降低负载均衡度的情况下以较快的收敛速度整体提高任务执行效率.
For implementing efficiently and safely cloud tasks scheduling
a task scheduling game alogrithm considering resource reliability is presented in this paper. Tasks execution cost
resource utilization cost
sacurity cost and load balance cost are considered comprehensively in the games utility function in our algorithm. With optimizing the games utility function as the goal
cloud tasks scheduling problem is formalized as a non-cooperative game model. This solving process is divided into two level. GA and improved MCT are used to solve the two level respectively
which enventually leads to get Nash equilibrium of non-cooperative game. Experimental results shows that our algorithm not only can guarantee task completion time and task response time
but can reduce the failure rate of task execution and improve the task execution efficiency integrally without reducing load balance degree and with a faster convergence.
Wang W, Zeng G, Tang D, et al. Cloud-DLS : dynamic trusted scheduling for cloud computing[J]. Expert systems with applications, 2012, 39(3): 2321-2329. DOI: 2321
Song S, Hwang K, Kwok Y. Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling[J]. IEEE Transactions on Computers, 2006, 55(6): 703-719. DOI: 703
Rodrigo C, Rajiv R, Anton B, et al. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J]. Software: practice and experience, 2011, 41(1): 23-50. DOI: 23
Randies M, Lamb D, Taleb A. A comparative study into distributed load balancing algorithm for cloud computing[C]// Proceedings of the IEEE 24th international conference on advanced information networking and applications Workship. Perth, Western Australia, 2010: 551-556.
He S, Sun H, Laszewski G, QoS guided Min-Min heuristic for grid task scheduling[J]. Journal of Computer Science and Technology, 2003, 18(4): 442-451. DOI: 442
胡德敏, 户静, 余星. 云计算环境下基于微粒群的虚拟机任务调度算法[J]. 计算机测量与控制, 2014, 22(4): 1189-1192. DOI: 1189
杨照峰, 王启明, 吕海莲. 基于任务延迟的云计算资源调度算法研究[J]. 计算机测量与控制, 2014, 22(2): 499-502. DOI: 499
沈恺涛, 胡德敏. 基于云计算和改进离散料子群的任务调度研究[J]. 计算机测量与控制, 2012, 20(11): 3070-3072. DOI: 3070
朱海, 王宇平. 安全驱动的实时任务调度遗传算法[J]. 系统工程与电子技术, 2010, 32(4): 854-859. DOI: 854
0
浏览量
8
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010602201714号