Online task and resource scheduling designing for container cloud queue based on Lyapunov optimization method

doi:10.11772/j.issn.1001-9081.2018061243

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (2): 494-500.DOI: 10.11772/j.issn.1001-9081.2018061243

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Online task and resource scheduling designing for container cloud queue based on Lyapunov optimization method

LI Lei¹, XUE Yang¹, LYU Nianling¹, FENG Min²

1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou Guangdong 510641, China;
2. 21 CN Limited Liability Company, Guangzhou Guangdong 510630, China

Received:2018-06-14 Revised:2018-09-07 Online:2019-02-10 Published:2019-02-15
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61472144), the Guangdong Science and Technology Project (2017A010101027, 2015B010131004).

基于李雅普诺夫优化的容器云队列在线任务和资源调度设计

李磊¹, 薛洋¹, 吕念玲¹, 冯敏²

1. 华南理工大学电子与信息学院, 广州 510641;
2. 世纪龙信息网络有限责任公司, 广州 510630

通讯作者: 李磊
作者简介:李磊(1983-),男,云南昆明人,高级实验师,博士,CCF会员,主要研究方向:云计算、分布式系统的优化;薛洋(1977-),女,辽宁沈阳人,副教授,博士,CCF会员,主要研究方向:机器学习、最优化计算;吕念玲(1970-),女,广东湛江人,高级实验师,硕士,主要研究方向:电路系统、实验教学系统优化;冯敏(1979-),男,广东广州人,工程师,主要研究方向:移动互联网应用、多媒体服务。
基金资助:
国家自然科学基金资助项目（61472144）；广东省科技计划项目（2017A010101027，2015B010131004）。

Abstract

Abstract: To improve the resource utilization with Quality of Service (QoS) guarantee, a task and resource scheduling method under Lyapunov optimization for container cloud queue was proposed. Firstly, based on the queueing model of cloud computing, the Lyapunov function was used to analyze the variety of the task queue length. Secondly, the minimum energy consumption objective function was constructed under the task QoS guarantee. Finally, Lyapunov optimization method was used to solve the minimum cost objective function to obtain an optimization scheduling policy for the online tasks and container resources, improving the resource utilization and guaranteeing the QoS. The CloudSim simulation results show that, the proposed task and resource scheduling policy achieves high resource utilization under the QoS guarantee, which realizes the online task and resource optimization scheduling of container cloud and provides necessary reference for cloud computing task and resource overall optimization based on queuing model.

Key words: cloud computing, resource scheduling, modeling and analysis, Quality of Service (QoS) guarantee, energy consumption optimization, Lyapunov optimization

摘要： 为在保证任务服务质量（QoS）的条件下提高容器云资源利用率，提出一种基于李雅普诺夫的容器云队列任务和资源调度优化策略。首先，在云计算服务排队模型的基础上，通过李雅普诺夫函数分析任务队列长度的变化；然后，在任务QoS的约束下，构建资源功耗的最小化目标函数；最后，利用李雅普诺夫优化方法求解最小资源功耗目标函数，获得在线的任务和容器资源的优化调度策略，实现对任务和资源调度进行整体优化，从而保证任务的QoS并提高资源利用率。CloudSim仿真结果表明，所提的任务和资源调度策略在保证任务QoS的条件下能获得高的资源利用率，实现容器云在线任务和资源优化调度，并且为基于排队模型的云计算任务和资源整体优化提供必要的参考。

关键词: 云计算, 资源调度, 建模与分析, 服务质量保证, 功耗优化, 李雅普诺夫优化

CLC Number:

TP393.09

LI Lei, XUE Yang, LYU Nianling, FENG Min. Online task and resource scheduling designing for container cloud queue based on Lyapunov optimization method[J]. Journal of Computer Applications, 2019, 39(2): 494-500.

李磊, 薛洋, 吕念玲, 冯敏. 基于李雅普诺夫优化的容器云队列在线任务和资源调度设计[J]. 计算机应用, 2019, 39(2): 494-500.

References

[1] MELL P M, GRANCE T. The NIST definition of cloud computing, SP 800-145[R]. Gaithersburg, MD:National Institute of Standards & Technology, 2011:2-4.
[2] SERRANO D, BOUCHENAK S, KOUKI Y, et al. SLA guarantees for cloud services[J]. Future Generation Computer Systems, 2016, 54:233-246.
[3] RODRIGUES E, ASSUNCAO R, PAPPA G L, et al. Exploring multiple evidence to infer users' location in Twitter[J]. Neurocomputing, 2016, 171:30-38.
[4] CHEN T, GAO X, CHEN G. The features, hardware, and architectures of data center networks:a survey[J]. Journal of Parallel and Distributed Computing, 2016, 96:45-74.
[5] CHIANG Y-J, OUYANG Y-C, HSU C-H. An optimal cost-efficient resource provisioning for multi-servers cloud computing[C]//Proceedings of the 2013 International Conference on Cloud Computing and Big Data. Washington, DC:IEEE Computer Society, 2013:225-231.
[6] SLADESCU M, FEKETE A, LEE K, et al. Event aware workload prediction:a study using auction events[C]//WISE 2012:Proceedings of the 2012 International Conference on Web Information Systems Engineering. Berlin:Springer-Verlag, 2012:368-381.
[7] ISLAM S, KEUNG J, LEE K, et al. Empirical prediction models for adaptive resource provisioning in the cloud[J]. Future Generation Computer Systems, 2012, 28(1):155-162.
[8] CALHEIROS R N, MASOUMI E, RANJAN R, et al. Workload prediction using ARIMA model and its impact on cloud applications' QoS[J]. IEEE Transactions on Cloud Computing, 2015, 3(4):449-458.
[9] PACHECO-SANCHEZ S, CASALE G, SCOTNEY B, et al. Markovian workload characterization for QoS prediction in the cloud[C]//CLOUD'11:Proceedings of the 2011 IEEE International Conference on Cloud Computing. Washington, DC:IEEE Computer Society, 2011:147-154.
[10] 徐达宇,丁帅.改进GWO优化SVM的云计算资源负载短期预测研究[J]. 计算机工程与应用, 2017, 53(7):68-73. (XU D Y, DING S. Research on improved GWO-optimized SVM-based short-term load prediction for cloud computing[J]. Computer Engineering and Applications, 2017, 53(7):68-73.)
[11] 赵宏伟,申德荣,田力威.云计算环境下资源需求预测与调度方法的研究[J]. 小型微型计算机系统,2016,37(4):659-663. (ZHAO H W, SHEN D R, TIAN L W. Research on resources forecasting and scheduling method in cloud computing environment[J]. Journal of Chinese Computer Systems, 2016, 37(4):659-663.)
[12] 李丹程,王晓晨,宋晓雪,等.基于OpenStack的资源负载预测方法研究[J]. 计算机应用研究,2014,31(7):2178-2182. (LI D C, WANG X C, SONG X X, et al. Study on load prediction method based on OpenStack[J]. Application Research of Computers, 2014, 31(7):2178-2182.)
[13] ZHU Q, AGRAWAL G. Resource provisioning with budget constraints for adaptive applications in cloud environments[J]. IEEE Transactions on Services Computing, 2012, 5(4):497-511.
[14] BONVIN N, PAPAIOANNOU T G, ABERER K. Autonomic SLA-driven provisioning for cloud applications[C]//CCGRID'11:Proceedings of the 2011 IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. Washington, DC:IEEE Computer Society, 2011:434-443.
[15] KHAZAEI H, MISIC J, MISIC V B. A fine-grained performance model of cloud computing centers[J]. IEEE Transactions on Parallel & Distributed Systems, 2013, 24(11):2138-2147.
[16] 李腾耀,张凤琴,王梦非.使用遗传算法改进的两阶段云任务调度算法研究[J].小型微型计算机系统,2017,38(6):1305-1310. (LI T Y, ZHANG F Q, WANG M F. Improved two period cloud task scheduling algorithm with genetic algorithm[J]. Journal of Chinese Computer Systems, 2017, 38(6):1305-1310.)
[17] 朱海,王洪峰,廖貅武.云环境下能耗优化的任务调度模型及虚拟机部署算法[J]. 系统工程理论与实践,2016,36(3):768-778. (ZHU H, WANG H F, LIAO X W. Task scheduling model and virtual machine deployment algorithm for energy consumption optimization in cloud computing[J]. Systems Engineering-Theory & Practice, 2016, 36(3):768-778.)
[18] ANDERSON C. Docker[Software engineering] [J]. IEEE Software, 2015, 32(3):102-c3.
[19] ENTICKNAP N. Von Neumann architecture[J]. Computer Jargon Explained, 1989(Suppl 2):128-129.
[20] KUMAR S, BUYYA R. Green cloud computing and environmental sustainability[M]//Harnessing Green IT:Principles and Practices. Hoboken:John Wiley & Sons, 2012:315-339.
[21] CHIANG Y-J, OUYANG Y-C, HSU C-H. An efficient green control algorithm in cloud computing for cost optimization[J]. IEEE Transactions on Cloud Computing, 2015, 3(2):145-155.
[22] CALHEIROS R N, RANJAN R, BUYYA R. Virtual machine provisioning based on analytical performance and QoS in cloud computing environments[C]//ICPP'11:Proceedings of the 2011 International Conference on Parallel Processing. Washington, DC:IEEE Computer Society, 2011:295-304.
[23] LI S, ZHOU Y, JIAO L, et al. Towards operational cost minimization in hybrid clouds for dynamic resource provisioning with delay-aware optimization[J]. IEEE Transactions on Services Computing, 2015, 8(3):398-409.
[24] LU P, SUN Q, WU K, et al. Distributed online hybrid cloud management for profit-driven multimedia cloud computing[J]. IEEE Transactions on Multimedia, 2015, 17(8):1297-1308.
[25] KHAZAEI H, MISIC J, MISIC V B. Performance analysis of cloud computing centers using M/G/m/m+r queuing systems[J]. IEEE Transactions on Parallel and Distributed Systems, 2012, 23(5):936-943.
[26] NEELY M. Stochastic Network Optimization with Application to Communication and Queueing Systems[M]. Williston, VT:Morgan and Claypool Publishers, 2010:15-16.
[27] LIU J, STOLYAR A L, CHIANG M, et al. Queue back-pressure random access in multihop wireless networks:optimality and stability[J]. IEEE Transactions on Information Theory, 2009, 55(9):4087-4098.
[28] RASMUSSEN R V, TRICK M A. Round robin scheduling-a survey[J]. European Journal of Operational Research, 2008, 188(3):617-636.
[29] ATIEWI S, YUSSOF S, RUSLI M E. A comparative analysis of task scheduling algorithms of virtual machines in cloud environment[J]. Journal of Computer Science, 2015, 11(6):804-812.
[30] CALHEIROS R N, RANJAN R, BELOGLAZOV A, et al. CloudSim:a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms[J]. Software Practice & Experience, 2011, 41(1):23-50.

Online task and resource scheduling designing for container cloud queue based on Lyapunov optimization method

基于李雅普诺夫优化的容器云队列在线任务和资源调度设计

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