Autonomic Computing, International Conference on (2005)
June 13, 2005 to June 16, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICAC.2005.50
Mohamed N. Bennani , George Mason University
Daniel A. Menasc? , George Mason University
Large data centers host several application environments (AEs) that are subject to workloads whose intensity varies widely and unpredictably. Therefore, the servers of the data center may need to be dynamically redeployed among the various AEs in order to optimize some global utility function. Previous approaches to solving this problem suffer from scalability limitations and cannot easily address the fact that there may be multiple classes of workloads executing on the same AE. This paper presents a solution that addresses these limitations. This solution is based on the use of analytic queuing network models combined with combinatorial search techniques. The paper demonstrates the effectiveness of the approach through simulation experiments. Both online and batch workloads are considered.
D. A. Menasc? and M. N. Bennani, "Resource Allocation for Autonomic Data Centers using Analytic Performance Models," Autonomic Computing, International Conference on(ICAC), Seattle, Washington, 2005, pp. 229-240.