Abstract
We present an approach to optimal virtual machine placement within datacenters for predicable and time-constrained load peaks. A method for optimal load balancing is developed, based on binary integer programming. For tradeoffs between quality of solution and computation time, we also introduce methods to pre-process the optimization problem before solving it. Upper bound based optimizations are used to reduce the time required to compute a final solution, enabling larger problems to be solved. For further scalability, we also present three approximation algorithms, based on heuristics and/or greedy formulations. The proposed algorithms are evaluated through simulations based on synthetic data sets. The evaluation suggests that our algorithms are feasible, and that these can be combined to achieve desired tradeoffs between quality of solution and execution time.
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Li, W., Tordsson, J., Elmroth, E. (2012). Virtual Machine Placement for Predictable and Time-Constrained Peak Loads. In: Vanmechelen, K., Altmann, J., Rana, O.F. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2011. Lecture Notes in Computer Science, vol 7150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28675-9_9
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DOI: https://doi.org/10.1007/978-3-642-28675-9_9
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