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云计算中多层次公平性QoS约束任务调度算法 被引量:10

Multiple-level fairness QoS constraint task scheduling algorithm in cloud computing
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摘要 针对传统云任务调度算法只注重执行效率忽略分配公平性的问题,提出了一种满足多重公平性约束的任务调度QoS算法CTS_QFC.该算法利用社会资源分配的公平性理论模型,从用户任务与云资源提供方两个角度,将云任务调度问题建模为一种多重公平性QoS约束模型.第一层QoS按用户QoS偏好对任务分类,并按照任务分类建立一般期望效用函数.第二层QoS定义资源公平性评估函数,评估资源分配的公平性.结果表明,CTS_QFC算法不仅可以确保用户任务的高效执行,还可以提高资源分配与任务调度方案的公平性. Aiming at the problem that the traditional cloud task scheduling algorithms only pay attention to the execution efficiency with ignoring the fairness of resource allocation,a cloud task scheduling QoS algorithm meeting multiple-level fairness constraints(CTS_QFC)was proposed.With the fairness theory model for the social resource allocation and from two aspects of user tasks and cloud resource providers,the cloud task scheduling problem was modeled as a multiple-level fairness QoS constraint model.The first level QoS classified the tasks according to the QoS preference of users,and established the general expected utility function according to the task classification.The second level QoS defined the resource fairness evaluation function to evaluate the fairness of resource allocation.The results showthat the CTS_QFC algorithm can not only ensure the efficient execution of user tasks,but can also improve the fairness of resource allocation and task scheduling.
作者 郑迎凤 宋朝 赵文彬 ZHENG Ying-feng;SONG Chao;ZHAO Wen-bin(Institute of Network and Information Technology,Huanghe Science and Technology College,Zhengzhou 450000,China;College of Information Science and Technology,Shijiazhuang Railway University,Shijiazhuang 050043,China)
出处 《沈阳工业大学学报》 EI CAS 北大核心 2019年第3期309-314,共6页 Journal of Shenyang University of Technology
基金 河南省科技厅科技攻关重点项目(20140666)
关键词 云计算 任务调度 公平性约束 服务质量约束 满意度 资源分配 执行效率 公平性评估 cloud computing task scheduling fairness constraint quality of service(QoS)constraint satisfaction resource allocation execution efficiency fairness evaluation
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  • 1刘正伟,文中领,张海涛.云计算和云数据管理技术[J].计算机研究与发展,2012,49(S1):26-31. 被引量:170
  • 2Bertsekas D, Gallager R. Data Networks [M]. New Jersey: Prentice Hall, 1992.
  • 3Charny A, Clark D D, Jain R. Congestion Control with International Conference on Communications: 3. Piscataway: Explicit Rate Indication [C]//Proceedings of the IEEE IEEE, 1995: 1954-1963.
  • 4Tan L, Pugh A C, Yin M, Rate-based Congestion Control in ATM Switching Networks Using a Recursive Digital Filter [J]. Control Engineering Practice, 2003(11) : 1171-1181.
  • 5Zukerman M, Tan L, Wang H, et al. Efficiency-Fairness Tradeoff in Telecommuflication Networks [J]. IEEE Communications Letters, 2005, 9(7): 643-645.
  • 6Kelly F. Charging and Rate Control for Elastic Traffic [J]. European Transaction on Telecommunications, 1997, 8(1) : 33-37.
  • 7Massoulie L, Roberts J. Bandwidth Sharing: Objectives and Algorithms [C] //Proceedings of 18th Annual Joint Conference of the IEEE Computer and Communications Societies: 3. Piscataway: IEEE, 1999: 1395-1403.
  • 8Baruah S K, Cohen N K, Plaxton C G, et al. Proportionate Progress: a Notion of Fairness in Resource Allocation [J]. Algorithmica, 1996, 15(6): 600-625.
  • 9Baruah S K, Gehrke J, Plaxton C G. Fast Scheduling of Periodic Tasks on Multiple Resources [C] //Proceedings of Parallel Processing Symposium. Piscataway: IEEE, 1995: 280-288.
  • 10Zhu D, Mosse D, Melhem R. Multiple-Resource Periodic Scheduling Problem: How Much Fairness is Necessary? [C]// 24th IEEE International Real-Time Systems Symposium. Piscataway: IEEE, 2003: 142-151.

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