Doctor of Philosophy
Dr. Mike Bauer
With the increasing number of "machines" (either virtual or physical) in a computing environment, it is becoming harder to monitor and manage these resources. Relying on human administrators, even with tools, is expensive and the growing complexity makes management even harder. The alternative is to look for automated approaches that can monitor and manage computing resources in real time with no human intervention. One of the approaches to this problem is policy-based autonomic management. However, in large systems having one single autonomic manager to manage everything is almost impossible. Therefore, multiple autonomic managers will be needed and these will need to cooperate in the overall management. We propose a management model using multiple autonomic managers organized in a hierarchical fashion to monitor and manage the resources in a computing environment based on provided policies. We develop a communication protocol to facilitate collaboration between different autonomic managers, define the core operations of these managers and introduce algorithms to deal with their deployment and operation. We also introduce an approach for the inference of the communication messages from policies and develop several algorithms for joining and maintaining the management hierarchy. We propose a deployment system that can discover relevant resources in a computing environment automatically to facilitate the deployment of autonomic managers at different levels of a physical system. We then test our approach by implementing it in a small private Infrastructure-as-a-Service (IaaS) cloud and show how this collaboration of autonomic managers in a hierarchical way can help to adopt to high stress situations automatically and reduce the SLA violation rate without adding any new resource to the environment.
Mola, Omid, "Collaborative Policy-Based Autonomic Management in IaaS Clouds" (2013). Electronic Thesis and Dissertation Repository. 1595.
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