Flexible resource allocation for reliable virtual cluster computing systems
Abstract
Virtualization and cloud computing technologies now make it possible to create scalable and reliable virtual high performance computing clusters. Integrating these technologies, however, is complicated by fundamental and inherent differences in the way in which these systems allocate resources to computational tasks. Cloud computing systems immediately allocate available resources or deny requests. In contrast, parallel computing systems route all requests through a queue for future resource allocation. This divergence of allocation polices hinders efforts to implement efficient, responsive, and reliable virtual clusters. Hence, the work develops a continuum of four scheduling polices along with an analytical resource prediction model for each policy to estimate the level of resources needed to provide a predictable grade of service for a realistic high performance computing workload and estimate the queue wait time for a partial or full resource allocation. To determine the performance of the models in a real system, they are simulated using the Haizea simulator. The models and results are useful for cloud computing providers seeking to operate efficient and cost-effective virtual cluster systems.
Degree
M.S.
Advisors
Hacker, Purdue University.
Subject Area
Computer Engineering|Information Technology
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