Differential evolution with double-level archives for bi-objective cloud task scheduling

In cloud computing, scheduling plays a critical role for quality of service (QoS) and provider efficiency which are generally measured by several metrics and make the scheduling a multiobjective problem (MOP). In this paper, we propose a differential evolution algorithm with double-level archives (DE-DLA) for bi-objective cloud task scheduling. The proposed algorithm is based on the newly-developed framework, multiobjective evolutionary algorithm with double-level archives (MOEA-DLA), and uses differential evolution to implement this framework. Global Archive is used to save Pareto-optimal individuals for the whole problem and Sub-archive is used to save several comparatively good individuals for the corresponding sub-problem formed by decomposition. So the algorithm takes advantages of both whole multiobjective problem optimization and decomposition based optimization. Precedence constraint in user's application is considered in the scheduling model of this paper. To minimize cost and makespan simultaneously, the proposed algorithm tries to find optimal resource allocation and optimal order of task executing. In the experiment, compared with two other algorithms, DE-DLA has shown competitive advantages.