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.
Birman outline their distributed self-configuring monitoring and adaptation tool called Astrolabe @cite_10 . Astrolabe works like any other monitoring tool by observing the state of an infrastructure where the tool is installed. However it differs by essentially creating a virtual system-wide hierarchical relational database based on a peer-to-peer protocol meaning no central server needs to exist to collect monitoring data. By performing distributed data analysis, Astrolabe can create performance summaries of --- machines typically grouped based on the shortest latency between one another or simply an administrator-specified group --- by data aggregation; a method we use to create graphs of cloudlets.
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The dramatic growth of computer networks creates both an opportunity and a daunting distributed computing problem for users seeking to build applications that can configure themselves and adapt as disruptions occur. The problem is that data often resides on large numbers of devices and evolves rapidly. Systems that collect data at a single location scale poorly and suffer from single-point failures. Here, we discuss the use of a new system, Astrolabe, to automate self-configuration, monitoring, and to control adaptation. Astrolabe operates by creating a virtual system-wide hierarchical database, which evolves as the underlying information changes. Astrolabe is secure, robust under a wide range of failure and attack scenarios, and imposes low loads even under stress.