一种云环境下基于分级管理的自律计算模型

计算机科学 ›› 2014, Vol. 41 ›› Issue (3): 189-192.

一种云环境下基于分级管理的自律计算模型

刘文洁,李战怀

西北工业大学计算机学院西安710072;西北工业大学计算机学院西安710072

出版日期:2018-11-14 发布日期:2018-11-14
基金资助:
本文受国家高技术发展计划(863计划:2012AA011004),国家自然科学基金(61303037,7),西北工业大学基础研究基金(JC201261)资助

Autonomic Computing Model Based on Hierarchical Management in Cloud Environment

LIU Wen-jie and LI Zhan-huai

Online:2018-11-14 Published:2018-11-14

摘要/Abstract

摘要： 自律计算是实现复杂IT系统的自我修复、自我管理的有效手段。但是在云环境下,系统所管理的计算资源数量急剧增长,传统的资源调度算法和模型架构无法对资源进行合理分配,导致资源的利用率很低。为了提高云环境下的资源管理效率,提出了一种面向云环境的自律计算模型,它采用多自主管理器的分级管理模式来解决传统自律计算模型在处理大量请求时的瓶颈问题；并提出了混合策略管理模式来应对不同的故障修复请求。该模型能够降低云环境下的资源管理成本,满足云服务所需的SLA。

关键词: 云环境,自律计算,分级管理,故障修复中图法分类号TP311文献标识码A

Abstract: Autonomic Computing is an effective method to make complex IT system to realize self-recovery and self-management．But in cloud environment,with the rapid growth of the computing resources,traditional resources algorithms and models can not allocate the resources effectively,therefore the resources usage rate is very low．To enhance the resource management efficiency,this paper proposed an autonomic computing model for cloud environment,which divides the autonomic manager into master and slave,uses hierarchical management to solve the bottleneck problem when dealing with massive requests,and uses the mixed policies method for different fault recovery requests．This model can lower the resources management cost in cloud environment and meet the SLA that cloud services need.

Key words: Cloud environment,Autonomic computing,Hierarchical management,Fault recovery

刘文洁,李战怀. 一种云环境下基于分级管理的自律计算模型[J]. 计算机科学, 2014, 41(3): 189-192. https://doi.org/

LIU Wen-jie and LI Zhan-huai. Autonomic Computing Model Based on Hierarchical Management in Cloud Environment[J]. Computer Science, 2014, 41(3): 189-192. https://doi.org/

参考文献

[1] Kephart J,Chess D．The Vision of Autonomic Computing [J]．IEEE Computer Society,2003,36(1):41-50
[2] Shi C Y,Zhang W．Agent-Based Computing [M]．Beijing:Tsinghua University Press,2007:275-320
[3] Bogdan S,Dan I,Marin L,et al．Towards a real-time reference architecture for autonomic systems[C]∥Proceedings-ICSE 2007Workshops:International Workshop on Software Engineering for Adaptive and Self-Managing Systems．2007:1-10
[4] Yu Cheng,Alberto L-G,Source F I．Toward An AutonomicService Management Framework:A Holistic Vision of SOA,AON,and Autonomic Computing [J]．IEEE Communications Magazine,2008,46(5):138-146
[5] Wang Xiao-ying,Lan Dong-jun,Fang Xing,et al．A resourcemanagement framework for multi-tier service delivery in autonomic virtualized environments [C]∥Network Operations and Management Symposium,2008,NOMS 2008．IEEE,2008:310-316
[6] Michael M,Ivan B,Vincent C E,et al．Revealing the MAPE loop for the autonomic management of cloud infrastructures [C]∥Proceedings-IEEE Symposium on Computers and Communications, ISCC’11．2011:147-152
[7] Huebscher,Markus C．A survey of Autonomic Computing-De-grees,models,and applications [J]．ACM Computing Surveys,2008,40(3):7-28
[8] Xu Cheng-zhong．URL:A unified reinforcement learning ap-proach for autonomic cloud management [J]．Journal of Parallel and Distributed Computing,2012,72(2):95-105
[9] Emeakaroha,Vincent C．Towards autonomic detection of SLA violations in Cloud infrastructures [J]．Future Generation Computer Systems,2012,28(7):1017-1029
[10] Champrasert,Paskorn．Exploring self-optimization and self-stabilization properties in bio-inspired autonomic cloud applications [J]．Concurrency Computation Practice and Experience,2012,24(9):1015-1034
[11] You G-W,Hwang S-W,Jain N.Ursa:Scalable load and power management in cloud storage systems [J]．ACM Transactions on Storage,2013,9(1):1-29

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed