基于仿生学原理的云资源自主监控系统设计与实现

doi:10.11772/j.issn.1001-9081.2016.07.2051

计算机应用 ›› 2016, Vol. 36 ›› Issue (7): 2051-2055.DOI: 10.11772/j.issn.1001-9081.2016.07.2051

基于仿生学原理的云资源自主监控系统设计与实现

孙鹏, 许瀚, 陈晶晶, 曹旭东

电子科技大学计算机科学与工程学院, 成都 611731

收稿日期:2016-01-19 修回日期:2016-03-04 出版日期:2016-07-10 发布日期:2016-07-14
通讯作者: 许瀚
作者简介:孙鹏(1979-),男,河南洛阳人,讲师,博士研究生,主要研究方向:云计算、自主计算、智能调度;许瀚(1990-),男,四川成都人,博士研究生,CCF会员,主要研究方向:云计算;陈晶晶(1988-),女,安徽宿州人,硕士研究生,主要研究方向:云计算;曹旭东(1988-),男,四川成都人,硕士,主要研究方向:云计算。
基金资助:
国家自然科学基金资助项目（61170042）。

Design and implementation of cloud resource monitoring system based on bionics

SUN Peng, XU Han, CHEN Jingjing, CAO Xudong

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, China

Received:2016-01-19 Revised:2016-03-04 Online:2016-07-10 Published:2016-07-14
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61170042).

摘要/Abstract

摘要： 为了解决现有监控系统因主控节点功能太过集中而导致某些时刻网络流量过大、系统扩展性差和无法及时应对节点失效的问题，提出了一种基于仿生自主神经系统（BANS）的新型云资源监控系统。首先，系统中引入了多级存储、分批上报的机制，将需要上报的监控信息分批次分时段上传汇总，使得在任何单一时刻系统内不会产生过大的流量和负载，保证了系统的稳定性；其次，系统中加入了类动态主机配置协议（DHCP）的主动发现机制以及定期轮询策略，使得系统在处理新节点加入，节点故障问题时，获得了类似仿生自主神经系统一样的自组织、自修复能力。实验结果表明，基于BANS的云资源监控系统实现了自组织与自修复的功能，并且可以有效降低系统内的通信流量，某些单一时刻能将流量降低到仅有原来的三分之一。

关键词: 云计算, 资源监控, 仿生自主神经系统, 云监控, 自组织, 自修复

Abstract: Concerning the problems of heavy network traffic, which is caused by the excessive load of master node, bad expansibility and poor ability to handle node failure in existing monitoring system, a novel Cloud Monitoring System based on Bionic autonomic nervous system (B-CMS) was proposed. Firstly, B-CMS imported hierarchical storage and batch-wise report mechanism to upload the monitoring information, which can decrease the network traffic at any moment to ensure monitor system's stability. In addition, the use of similar Dynamic Host Configuration Protocol (DHCP) and polling-driven heartbeat checking mechanism enabled the monitor system to get the self-organizing and self-repairing ability which is similar to Bionic Autonomic Nervous System (BANS) when adding new node and handling node failure in autonomic way. The experimental results show that B-CMS achieves self-organizing and self-repairing, and effectively decreases network traffic. In some special moment, the network traffic is only one-third of the original system.

Key words: cloud computing, resource monitoring, Bionic Autonomic Nervous System (BANS), cloud monitoring, self-organizing, self-repairing

中图分类号:

TP391.8

孙鹏, 许瀚, 陈晶晶, 曹旭东. 基于仿生学原理的云资源自主监控系统设计与实现[J]. 计算机应用, 2016, 36(7): 2051-2055.

SUN Peng, XU Han, CHEN Jingjing, CAO Xudong. Design and implementation of cloud resource monitoring system based on bionics[J]. Journal of Computer Applications, 2016, 36(7): 2051-2055.

参考文献

[1] MA S. A review on cloud computing development[J]. Journal of Networks, 2012, 7(2):305-310.
[2] GAO J, XIAO Y, LIU J, et al. A survey of communication/networking in smart grids[J]. Future Generation Computer Systems, 2012, 28(2):391-404.
[3] ACETO G, BOTTA A, DE DONATO W, et al. Cloud monitoring:a survey[J]. Computer Networks, 2013, 57(9):2093-2115.
[4] MASSIE M L, CHUN B N, CULLER D E. The Ganglia distributed monitoring system:design, implementation, and experience[J]. Parallel Computing, 2004, 30(7):817-840.
[5] SACERDOTI F D, KATZ M J, MASSIE M L, et al. Wide area cluster monitoring with ganglia[C]//Proceedings of the 2003 IEEE International Conference on Cluster Computing. Piscataway, NJ:IEEE, 2003:289-298.
[6] YANG C T, CHEN T T, CHEN S Y. Implementation of monitoring and information service using Ganglia and NWS for grid resource brokers[C]//Proceedings of the 2nd IEEE Asia-Pacific Service Computing Conference. Piscataway, NJ:IEEE, 2007:356-363.
[7] HINCHEY M, DAI Y S, RASH J L, et al. Bionic autonomic nervous system and self-healing for NASA ANTS-like missions[C]//Proceedings of the 2007 ACM Symposium on Applied Computing. New York:ACM, 2007:90-96.
[8] KEPHART J O, CHESS D M. The vision of autonomic computing[J]. Computer, 2003, 36(1):41-50.
[9] CARAFFINI F, NERI F, PICINALI L. An analysis on separability for memetic computing automatic design[J]. Information Sciences, 2014, 265:1-22.
[10] DROMS R. Stateless Dynamic Host Configuration Protocol (DHCP) Service for IPv6[EB/OL].[2015-09-20]. http://tools.ietf.org/html/rfc3736.
[11] HUEBSCHER M C, MCCANN J A. A survey of autonomic computing-degrees, models, and applications[J]. ACM Computing Surveys, 2008, 40(3):Article No. 7.
[12] MARSHALL T, DAI Y S. Reliability improvement and models in autonomic computing[C]//Proceedings of the 11th International Conference on Parallel and Distributed Systems. Piscataway, NJ:IEEE, 2005:468-472.

[1]	陈家豪, 殷新春. 基于云雾计算的可追踪可撤销密文策略属性基加密方案[J]. 计算机应用, 2021, 41(6): 1611-1620.
[2]	葛丽娜, 胡雨谷, 张桂芬, 陈园园. 云计算环境基于客体属性匹配的逆向混合访问控制方案[J]. 计算机应用, 2021, 41(6): 1604-1610.
[3]	程美英, 钱乾, 倪志伟, 朱旭辉. 信息筛选多任务优化自组织迁移算法[J]. 计算机应用, 2021, 41(6): 1748-1755.
[4]	马建红, 曹文斌, 刘元刚, 夏爽. 基于功效特征的专利聚类方法[J]. 计算机应用, 2021, 41(5): 1361-1366.
[5]	杨翎, 姜春茂. 基于三支决策的虚拟机节能迁移策略[J]. 计算机应用, 2021, 41(4): 990-998.
[6]	孙晓玲, 杨光, 沈焱萍, 杨秋格, 陈涛. 基于可拆分倒排索引的可搜索加密方案[J]. 《计算机应用》唯一官方网站, 2021, 41(11): 3288-3294.
[7]	吕佳玉, 竺智荣, 姚志强. 云计算环境下的双通道数据动态加密策略[J]. 计算机应用, 2020, 40(8): 2268-2273.
[8]	陈程军, 毛莺池, 王绎超. 基于激活-熵的分层迭代剪枝策略的CNN模型压缩[J]. 计算机应用, 2020, 40(5): 1260-1265.
[9]	郭曙杰, 李志华, 蔺凯青. 云环境下基于模糊隶属度的虚拟机放置算法[J]. 计算机应用, 2020, 40(5): 1374-1381.
[10]	许英鑫, 孙磊, 赵建成, 郭松辉. 基于蚁群优化算法的虚拟现场可编程门阵列部署策略[J]. 计算机应用, 2020, 40(3): 747-752.
[11]	王庆永, 毛莺池, 王绎超, 王龙宝. 基于多微云协作的计算任务卸载[J]. 计算机应用, 2020, 40(2): 328-334.
[12]	刘福鑫, 李劲巍, 王熠弘, 李琳. 基于Kubernetes的云原生海量数据存储系统设计与实现[J]. 计算机应用, 2020, 40(2): 547-552.
[13]	杨哂哂, 吴慧珍, 庄黎丽, 吕宏武. 基于Markov过程的IaaS系统可用性建模与分析方法[J]. 计算机应用, 2020, 40(10): 3013-3018.
[14]	林立, 熊金波, 肖如良, 林铭炜, 陈秀华. Gaming@Edge:基于边缘节点的低延迟云游戏系统[J]. 计算机应用, 2019, 39(7): 2001-2007.
[15]	徐雅斌, 彭宏恩. 基于需求预测的PaaS平台资源分配方法[J]. 计算机应用, 2019, 39(6): 1583-1588.

基于仿生学原理的云资源自主监控系统设计与实现

Design and implementation of cloud resource monitoring system based on bionics

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics