Genetic algorithms and tabu search: hybrids for optimization
Computers and Operations Research - Special issue on genetic algorithms
Colocation games: and their application to distributed resource management
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
IEEE Transactions on Evolutionary Computation
Embedding games: distributed resource management with selfish users
Embedding games: distributed resource management with selfish users
A novel approach for automated music composition using memetic algorithms
Proceedings of the 49th Annual Southeast Regional Conference
IEEE Transactions on Evolutionary Computation
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Cloud computing will revolutionize our way of life the same way the Internet did in the early nineties. Cloud computing users utilizing the infrastructure as a model service pay the provider based on a preset resource per unit time price whether they consumed the full capacity of this instance or not. To lower their cost, users could share their cloud resources with other users as long as they do not exceed the allocated capacity. The problem then is reduced to finding the optimal number of users that can share the resource given a certain capacity. In this paper we propose a solution to this problem using genetic algorithms. Our results show that our genetic algorithm converges to near optimal solutions, but it can take a long time to do so. It converges on less optimal solutions in much quicker time. Our studies have shown that memetic algorithms are more efficient than genetic algorithm approaches.