吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (3): 932-937.doi: 10.13229/j.cnki.jdxbgxb201503036

• • 上一篇    下一篇

基于效用最大化协商机制的云媒体资源分配算法

唐瑞春1, 2, 邱悦1, 丁香乾1, 李静1   

  1. 1.中国海洋大学 信息科学与工程学院,山东 青岛 266100;
    2. 海尔数字化家电国家重点实验室,山东 青岛 266101
  • 收稿日期:2013-09-17 出版日期:2015-05-01 发布日期:2015-05-01
  • 作者简介:唐瑞春(1968-),女,教授,博士.研究方向:网络流媒体.
  • 基金资助:
    国家科技支撑计划项目(2012BAH12F02)

Cloud media resource allocation algorithm based on utility maximization negotiation

TANG Rui-chun1, 2, QIU Yue1, DING Xiang-qian1, LI Jing1   

  1. 1.College of Information Science and Engineering, Ocean University of China, Qingdao 266100,China;
    2. State Key Laboratory of Digital Appliances, Qingdao 266101,China
  • Received:2013-09-17 Online:2015-05-01 Published:2015-05-01

PDF (PC)

216

摘要： 针对传统云媒体资源分配算法中没有考虑整体服务满意度和分配效用等因素对算法性能的影响,导致云媒体资源分配服务效率和可应用性不高的问题,引入云媒体服务提供者和云媒体服务请求者的效用函数模型,从服务价格、服务响应时间和服务带宽3个方面对该模型进行统一化描述。利用云媒体服务双方的效用函数值和让步策略,得到最大效用化的云媒体服务资源,并进行协商分配。基于协商机制效用模型,提出了资源分配算法RAANM(Resource allocation algorithm based on negotiation mechanism)。与传统调度算法相比,RAANM算法的云媒体资源分配的目标函数不再是最小化响应时间,而是最大化效用值,进而提高服务满意度。最后通过仿真实例验证了该算法的有效性。

关键词: 云媒体, 资源分配, SLAs协议, 服务效用

Abstract: Traditional cloud media resource allocation algorithm lacks service satisfaction and distribution utility etc, which results in the low efficiency of cloud media allocation and resource availability. In this work, first, the utility function of cloud media service provider and requester is introduced, which is uniformly described in three aspects: price, response time and bandwidth of the service. Then the cloud media resource is negotiatory allocated based on the utility function values of both sides of cloud media service and concession-making strategy, which maximizes the utility of the cloud media service resource. Finally, a Resource Allocation Algorithm based on Negotiation Mechanism (RAANM) is proposed on the basis of the negotiation utility model. Comparing with traditional resource allocation algorithm, RAANM aims to maximize the utility instead of to minimize the longest finishing time, thus to enhance service satisfaction. Simulation results demonstrate the efficiency of the proposed algorithm.

Key words: cloud media, resource allocation, SLAs agreement, service utility

中图分类号: 

  • TP37
[1] Huu T T,Tham C K. An auction-based resource allocation model for green cloud computing[C]∥2013 IEEE International Conference on Cloud Engineering,Redwood City,CA,2013:269-278.
[2] Wang L J,Meng M Q H. A game theoretical bandwidth allocation mechanism for cloud robotics[C]∥2012 10th World Congress on Intelligent Control and Automation,Beijing,2012:3828-3833.
[3] Ye D S, Chen J H. Non-cooperative games on multi-dimensional resource allocation[J]. Future Generation Computer Systems,2013,29(6):1345-1352.
[4] Copil G,Moldovan D,Salomie I. Cloud SLA negotiation for energy saving-A particle swarm optimization approach[C]∥2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing,Cluj-Napoca,2012:289-296.
[5] Son S,Jung G,Jun S C. An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider[J]. Journal of Supercomputing, 2013,64(2):606-637.
[6] Minarolli D, Freisleben B. Utility-based resource allocation for virtual machines in Cloud computing[C]∥2011 IEEE Symposium on Computers and Communications,Kerkyra,2011:410-417.
[7] Nan X, He Y, Guan L. Optimal resource allocation for multimedia cloud based on queuing model[C]∥13th International Workshop on Multimedia Signal Processing,Hangzhou,2011:1-6.
[8] Li S Y,Yang D,Qin Y J,et al. Network cross- layer mapping based on utility maximization[J]. Journal of Software,2011,22(8):1855-1871.
[9] Hong Bo-hai,Tang Rui-chun,Zhai Yi-li,et al. A resources allocation algorithm based on media task QoS in cloud computing[C]∥The 4th IEEE International Conference on Software Engineering and Service Sciences,Beijing,2013:841-844.
[10] 唐瑞春,冯骁,丁香乾,等. 基于上下文感知的内容适应算法及其在UPnP AV 中的应用[J]. 吉林大学学报:工学版,2014,44(5):1441-1446.
Tang Rui-chun,Feng Xiao,Ding Xiang-qian,et al. Contest-based content adaptation algorithm and its application in UPnP AV[J]. Journal of Jilin University(Engineering and Technology Edition),2014,44(5):1441-1446.
[11] Son S, Sim K M. A price-and-time-slot- negotiation mechanism for cloud service reservations[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,2012,42(3):713-728.
[12] Ma L, Lu Y M, Zhang F WA, et al. Dynamic task scheduling in cloud computing based on greedy strategy[C]∥Trustworthy Computing and Services,Beijing, China,2013:156-162.
[1] 姜来为, 沙学军, 吴宣利, 张乃通. LTE-A异构网络中新的用户选择接入和资源分配联合方法[J]. 吉林大学学报(工学版), 2017, 47(6): 1926-1932.
[2] 赵晓晖， 杨伟伟， 金晓光. 基于不同时延业务的中继正交频分复用系统资源分配算法[J]. 吉林大学学报(工学版), 2015, 45(6): 2049-2055.
[3] 陈健, 樊光辉, 阔永红. 认知无线电中继协助网络资源分层优化算法[J]. 吉林大学学报(工学版), 2014, 44(5): 1498-1505.
[4] 游晓明, 刘升, 王裕明. 量子行为网络资源并行分配优化模型及其应用[J]. 吉林大学学报(工学版), 2012, 42(增刊1): 341-345.
[5] 丛犁, 张海林, 刘毅, 赵力强, 张国鹏. 基于粒子群优化的协作网络资源分配的博弈策略[J]. 吉林大学学报(工学版), 2012, 42(01): 207-212.
[6] 程翔, 李立. 单物品多单元双向拍卖环境下的网格资源分配仿真[J]. 吉林大学学报(工学版), 2010, 40(05): 1359-1365.
[7] 卢前溪,彭涛,王玮,王文博. 认知无线电网络上行链路子载波和功率分配[J]. 吉林大学学报(工学版), 2010, 40(04): 1144-1149.
[8] 张欢, 王文博, 高伟东, 彭木根. 基于动态帧结构的多跳中继链路的资源分配[J]. 吉林大学学报(工学版), 2010, 40(02): 571-0575.
[9] 刘雪洁,刘衍珩,李木山,李岩,官健. 移动环境下扩展的基于策略的网管（PBNM）模型的动态资源分配[J]. 吉林大学学报(工学版), 2010, 40(01): 176-0182.
[10] 朱美玲, 赵晓晖, 顾海军, 董墨. 基于QoS的多用户OFDM系统自适应资源分配算法[J]. 吉林大学学报(工学版), 2009, 39(05): 1347-1352.
[11] 梅芳，刘衍珩，张旭利，古天野，王旺 . 移动网络资源管理策略的动态冲突消解机制[J]. 吉林大学学报(工学版), 2009, 39(02): 430-0435.
[12] 李鹤;赵晓晖;刘熠 . 比例速率约束下基于遗传策略的多用户OFDM系统资源自适应分配算法[J]. 吉林大学学报(工学版), 2008, 38(03): 709-0714.
[13] 张成文;张中兆;马永奎 . 基于多用户空间相关性的MIMOOFDM 下行链路资源分配[J]. 吉林大学学报(工学版), 2008, 38(03): 719-0725.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发