计算机科学 ›› 2018, Vol. 45 ›› Issue (4): 131-136.doi: 10.11896/j.issn.1002-137X.2018.04.021

• 网络与通信 • 上一篇    下一篇

基于稳定匹配的容器部署策略的优化

施超,谢在鹏,柳晗,吕鑫   

  1. 河海大学计算机与信息学院 南京211100,河海大学计算机与信息学院 南京211100,河海大学计算机与信息学院 南京211100,河海大学计算机与信息学院 南京211100
  • 出版日期:2018-04-15 发布日期:2018-05-11
  • 基金资助:
    本文受国家自然科学基金面上项目(61272543),NSFC-广东联合基金重点项目(U1301252)资助

Optimization of Container Deployment Strategy Based on Stable Matching

SHI Chao, XIE Zai-peng, LIU Han and LV Xin   

  • Online:2018-04-15 Published:2018-05-11

摘要: Docker的发展使得操作系统级虚拟化的容器渐渐兴起,容器即服务(CaaS)也越来越普及。随着容器技术的发展,容器将成为云环境中的主要部署模型,但针对容器的整合部署技术还未得到广泛的研究。容器化云环境中的容器数量众多,如何将众多的容器部署到合适的虚拟机以降低 数据中心能耗,成为了一个亟待解决的问题。因此,文中创新性地将机器学习中的几种相似度计算方法作为稳定匹配算法的偏好规则,同时将已经拟分配过容器的虚拟机继续加入偏好列表,从而将一对一的稳定婚姻匹配算法改进为多对一的稳定匹配,解决了将容器整合到虚拟机上的初始化部署问题。仿真实验结果表明 , 采用优化的稳定匹配算法来初始化将部署容器时,不仅SLA违规较低,而且比FirstFit,MostFull以及Random算法分别约节能12.8%,34.6%和30.87%,其中使用欧氏距离作为稳定匹配算法偏好规则的节能效果最好。

关键词: 容器云,容器即服务,稳定匹配,容器整合,能耗

Abstract: With the development of Docker,virtualization containers of operating system level are on the rise,and contai-ner-as-a-service(CaaS) is also becoming more and more popular.With the development of container technology,the container will become the main deployment model in the cloud environment,but the integrated deployment technology for the container has not been widely studied.In the cloud environment,how to deploy a large number of containers to a suitable virtual machine to reduce the energy consumption of the data center becomes an problem which needs to to be solved urgently.Therefore,several similarity calculation methods in machine learning are used as the preference rules of the stabilization matching algorithm,and the virtual machines that have been allocated to the container are added to the preference list at the same time,which makes the one-to-one stable marriage matching algorithm update to many-to-one stable match,solving the initial deployment problem of integrate container into the virtual machine.The experimental results show that the optimal storage efficiency is about 12.8%,34.6% and 30.87% compared with the FirstFit,MostFull and Random methods respectively,and when the Euclidean distance is used as the preference rules of stabilization matching algorithm,the performance of energy saving is the best.

Key words: Containerized cloud,Container as a service,Stable matching,Container consolidation,Energy consumption

[1] SHEPHERD D.Containers as a Service(CaaS) is the cloud ope-rating system-i build the cloud[EB/OL].http://www.ibuildthecloud.com/blog/2014/08/19/containers-as-a-service-caas-is-the-cloud-operating-system.
[2] DUA R,RAJA A R,KAKADI D.Virtualization vs Containerization to Support PaaS[C]∥2014 IEEE International Confe-rence on Cloud Engineering(IC2E).IEEE,2014:610-614.
[3] RUAN B,HUANG H,WU S,et al.A Performance Study of Containers in Cloud Environment[C]∥Advances in Services Computing:10th Asia-Pacific Services Computing Conference(APSCC 2016).Springer International Publishing,2016:343-356.
[4] VAKILINIA S,HEIDARPOUR B,CHERIET M.Energy efficient resource allocation in cloud computing environments[J].IEEE Access,2017,4(99):8544-8577.
[5] SPICUGLIA S,CHEN L Y,BIRKE R,et al.Optimizing capacity allocation for big data applications in cloud datacenters[C]∥Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management(IM 2015).2015:511-517.
[6] DONG Z,ZHUANG W,ROJAS-CESSA R.Energy-aware sche-duling schemes for cloud data centers on Google trace data[C]∥2014 IEEE Online Conference on Green Communications(OnlineGreencomm 2014).2014:1-6.
[7] YAQUB E,YAHYAPOUR R,WIEDER P,et al.Metaheuristics-based planning and optimization for SLA-Aware resource managementin PaaS clouds[C]∥7th IEEE/ACM International Conferenceon Utility and Cloud Computing(UCC 2014).2014:288-297.
[8] DING S,XIA C,CAI Q,et al.QoS-aware resource matching and recommendation for cloud computing systems[J].Applied Mathematics and Computation,2014,247(C):941-950.
[9] MASS J.The theory of stable allocations and the practice of market design:The Nobel Prize in Economics 2012 for Alvin E.Roth and Lloyd S.Shapley[J].Contributions to Science,2015,11:103-112.
[10] ZHAO D Z,LI R.The Bilateral Matching Mechanism of Cloud Manufacturing Resources Considering the Main Body’s Psychological Expectation[J].Control and Decision,2017,2(5):871-878.(in Chinese) 赵道致,李锐.考虑主体心理预期的云制造资源双边匹配机制[J].控制与决策,2017,32(5):871-878.
[11] PRICE D,TUCKER A.Solaris Zones:Operating system support for consolidating commercial workloads[C]∥18th USENIX Conference on System Administration(LISA 2004).2004:241-254.
[12] NAGY G.Operating system containers vs.application containers.https://blog.risingstack.com/operating-system-contai-ners-vs-application-containers.
[13] ALI Q.Scaling web 2.0 applications using Docker containers on vSphere 6.0.http://blogs.vmware.com/performance/2015/04/scaling-web-2-0-applications-using-docker-containers-vsphere-6-0.html.
[14] GREENBERG A,HAMILTON J,MALTZ D A,et al.The cost of a cloud:research problems in data center networks[J].ACM SIGCOMM Computer Communication Review,2008,39(1):68-73.
[15] CARRERAS J M.The theory of stable allocations and the practice of market design:The Nobel Prize in Economics 2012 for Alvin E.Roth and Lloyd S.Shapley[J].Contributions to Scien-ce,2015,11(1):103-112.
[16] LEI L Z.An abstract argumentation based study of stable mat-ching problems[D].Hangzhou:Zhejiang University,2014.(in Chinese) 雷丽赟.基于抽象论辩理论的稳定匹配问题研究[D].杭州:浙江大学,2014.
[17] PIRAGHAJ S F,DASTJERDI A V,CALHEIROS R N,et al.ContainerCloudSim:An environment for modeling and simulation of containers in cloud data centers[J].Software:Practiceand Experience,2017,47(4):505-521.
[18] MISHRA A K,HELLERSTEIN J L,CIME W,et al.Towards characterizing cloud backend workloads:insights from Google compute clusters[J].ACM SIGMETRICS Performance Evaluation Review,2010,37(4):34-41.
[19] PARK K,PAI V S.CoMon:Amostly-scalable monitoring sys-tem for planetlab[J].SIGOPS Operating System Review,2006,40(1):65-74.
[20] PIRAGHAJ S F,DASTJERDI A V,CALHEIROS R N,et al.A framework and algorithm for energy efficient container consolidation in cloud data centers[C]∥2015 IEEE International Conference on Data Science and Data Intensive Systems(DSDIS).IEEE,2015:368-375.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!