云环境下面向暴发式任务请求的资源部署模型设计

计算机科学 ›› 2013, Vol. 40 ›› Issue (9): 84-88.

云环境下面向暴发式任务请求的资源部署模型设计

陈鹏,马自堂,孙磊,孙冬冬

解放军信息工程大学三院郑州450004;解放军信息工程大学三院郑州450004;解放军信息工程大学三院郑州450004;61579部队北京102400

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受武器装备预研重点基金项目(9140A15060311JB5201)资助

Resource Deployment Model Design in Cloud Computing under Bursty Workloads

CHEN Peng,MA Zi-tang,SUN Lei and SUN Dong-dong

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 针对云计算环境面临的暴发式任务请求对系统性能带来的影响,提出了一种资源部署模型BWA来应对上述问题。首先由模型的负载监听模块负责监测云计算系统任务请求的变化量,实时判断暴发式任务请求的始末。然后通过引入新的资源部署策略,来避免局部热点的产生,加快系统的响应速度。最后利用跟踪预测算法预置计算节点来进一步加快云计算系统为用户提供服务的速率。通过CloudSim对资源部署模型进行了实验仿真,结果证明,该模型可有效优化系统响应速度。

关键词: 云计算,资源部署,暴发式任务请求,负载监听,跟踪预测中图法分类号TP302.7文献标识码A

Abstract: Aiming at the degrading system performance that bursty workloads bring in cloud computing,BWA(Bursty Workloads Allocation)model was proposed to resolve resource deployment problems．Firstly,BWA’s workload monitor model is responsible of monitoring the variation of tasks in cloud computing,judging the on-off in real time about bursty workloads．Next,BWA tries to avoid the appearance of partial hot dots by using new deployment strategy to speed up the system response．At last,the forecasting algorithm is used to improve response speed by deploying the computing nodes in advance．The results of simulation in CloudSim prove that using BWA model can obtain better system perfor-mance．

Key words: Cloud computing,Resource allocation,Bursty workloads,Monitor workloads,Forecast workloads

陈鹏,马自堂,孙磊,孙冬冬. 云环境下面向暴发式任务请求的资源部署模型设计[J]. 计算机科学, 2013, 40(9): 84-88. https://doi.org/

CHEN Peng,MA Zi-tang,SUN Lei and SUN Dong-dong. Resource Deployment Model Design in Cloud Computing under Bursty Workloads[J]. Computer Science, 2013, 40(9): 84-88. https://doi.org/

参考文献

[1] 谭一鸣,曾国荪,王伟.随机任务在云计算平台能耗的优化管理方法[J].软件学报,2012,23(2):266-278
[2] Cui V,Zhou T,Chen J,et al.中国云计算发展之道 [R].北京:IDC中国,2010
[3] 杨星,马自堂,孙磊.云环境下基于改进蚁群算法的虚拟机批量部署研究[J].计算机科学,2012,39(9):33-37
[4] Tai Jiang-zhe,Meleis W,Zhang Jue-min,et al．ARA:Adaptive Resource Allocation for Cloud Computing Environments under Bursty Workloads[R]．978-1-4673．Northeastern University,Boston,USA,2011
[5] 罗军舟,金嘉晖,宋爱波,等.云计算:体系架构与关键技术[J].通信学报,2009,32(7):1337-1348
[6] 杨际祥,谭国真,王荣生.并行与分布式计算动态负载均衡策略综述[J].电子学报,2010,38(5):1122-1130
[7] Gusella R.Characterizing the Variability of Arrival Processeswith Indices of Dispersion[R]．TR-90-051．International Computer Science Institute,USA,1990
[8] Fang Mi-ning,Giuliano C,Cherkasova L,et al．Burstiness inMulti-Tier Applications:Symptoms,Causes,and New Models,CNS-0720699[R]．CCF-0811417．College of William and Mary,Williamsburg,USA,2008
[9] Lassnig M,Fahringer T.Identification,modeling and prediction of non-periodic bursts in workloads[C]∥201010^th IEEE/ACM International Conference on Cluster,Cloud and Grid Computing．Austria,IEEE DOI /CCGRID,2010:485-494
[10] Goodwin P.The Holt-Winters Approach to Exponential Smoo-thing:50years Old and Going Strong[J]．FORESIGHT,2010,Fall:30-33
[11] Prajakta S K.Time series Forecasting using Holt-Winters Exponential Smoothing[D].Kanwal Rekhi School of Information Technology,2004
[12] Rodrigo N C,Ranjan R,Beloglazov A,et al．CloudSim:A Toolkit for Modeling and Simulation of Cloud Computing Environmentsand Evaluation of Resource Provisioning Algorithms[R].Cloud Computing and Distributed Systems(CLOUDS)Laboratory,Australia,2010

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