DocumentCode
3435074
Title
C2MS: Dynamic Monitoring and Management of Cloud Infrastructures
Author
McGilvary, G.A. ; Rius, Juan Manuel ; Goiri, Inigo ; Solsona, Francesc ; Barker, Adam ; Atkinson, Malcolm
Author_Institution
Edinburgh Data-Intensive Res. Group, Univ. of Edinburgh, Edinburgh, UK
Volume
1
fYear
2013
fDate
2-5 Dec. 2013
Firstpage
290
Lastpage
297
Abstract
Server clustering is a common design principle employed by many organisations who require high availability, scalability and easier management of their infrastructure. Servers are typically clustered according to the service they provide whether it be the application(s) installed, the role of the server or server accessibility for example. In order to optimize performance, manage load and maintain availability, servers may migrate from one cluster group to another making it difficult for server monitoring tools to continuously monitor these dynamically changing groups. Server monitoring tools are usually statically configured and with any change of group membership requires manual reconfiguration, an unreasonable task to undertake on large-scale cloud infrastructures. In this paper we present the Cloudlet Control and Management System (C2MS), a system for monitoring and controlling dynamic groups of physical or virtual servers within cloud infrastructures. The C2MS extends Ganglia - an open source scalable system performance monitoring tool - by allowing system administrators to define, monitor and modify server groups without the need for server reconfiguration. In turn administrators can easily monitor group and individual server metrics on large-scale dynamic cloud infrastructures where roles of servers may change frequently. Furthermore, we complement group monitoring with a control element allowing administrator-specified actions to be performed over servers within service groups as well as introduce further customized monitoring metrics. This paper outlines the design, implementation and evaluation of the C2MS.
Keywords
cloud computing; network servers; pattern clustering; resource allocation; software performance evaluation; C2MS; Ganglia; administrator-specified actions; cloudlet control and management system; customized monitoring metrics; dynamic cloud infrastructure management; dynamic cloud infrastructure monitoring; dynamically changing group monitoring; large-scale cloud infrastructures; large-scale dynamic cloud infrastructures; load management; open source scalable system performance monitoring tool; performance optimization; physical servers; server accessibility; server clustering; server monitoring tools; server reconfiguration; static configuration; virtual servers; Clouds; Educational institutions; Monitoring; Servers; Temperature measurement; Temperature sensors; cloud computing; cluster; ganglia; grid; management; monitoring;
fLanguage
English
Publisher
ieee
Conference_Titel
Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on
Conference_Location
Bristol
Type
conf
DOI
10.1109/CloudCom.2013.45
Filename
6753810
Link To Document