Multi-objective particle swarm optimization for resource allocation in cloud computing

Cloud computing is now a hot topic of research that is assumed as the third revolution of IT after computer technology and the internet. In cloud computing field, a service-provider offers large number of resources like computing units, storage space and software etc for customers with a relatively low cost. As the number of customer increases, fulfilling their requirements may become an important yet intractable matter. Resource allocation is therefore a primary issue considered restriction in resource amount that could be afforded by a company. The problem of resource allocation in cloud computing is thought to be a combinatorial optimization problem to a large company for numbers of their customers and owned resources could be huge enough. A particle swarm optimization algorithm is designed for this problem. The algorithm aims at finding out a desired task scheduler on resources based on multiple considerations including total task executing time, resource reservation, and QOS of each task. Pareto-domination mechanism is introduced into the algorithm helping searching multi-objective optimal solutions. Experimental results verify effectiveness and efficiency of the presented algorithm.