Tuning Genetic Algorithms for resource provisioning and scheduling in uncertain cloud environments: Challenges and findings

Cloud computing allows users to devise cost-effective solutions for deploying their applications. Nevertheless, the decisions about resource provisioning are very challenging because workloads are seriously affected by the uncertainty of cloud performance and their characteristics vary. In this paper we address these issues by explicitly modeling workload and cloud uncertainty in the decision process. For this purpose, we adopt a probabilistic formulation of the optimization problem aimed at minimizing the expected cost for deploying a parallel application under a deadline constraint. To find a sub-optimal solution of the problem we apply a Genetic Algorithm. By tuning its parameters we are able to assess their role and their impact on the effectiveness and efficiency of the algorithm for provisioning and scheduling in uncertain cloud environments.