Multi-objective workflow scheduling based on genetic algorithm in cloud environment

Abstract In recent years, cloud computing plays a crucial role in many real applications. Thus, how to solve workflow scheduling problems, i.e., allocating and scheduling different resources, under the cloud computing environment becomes more important. Although some evolutionary algorithms (EAs) can solve workflow scheduling problems with smaller large scale, they show some disadvantages on larger scale workflow applications. In this paper, a multi-objective genetic algorithm (MOGA) is applied to optimize workflow scheduling problems. To enhance the search efficiency, this study proposes an initialization scheduling sequence scheme based on tasks’ data sizes. Relying on the initial scheduling sequence, a proper trade-off between the makespan and the energy consumption, which are two optimization objectives in this study, can be achieved. In addition, the longest common subsequence (LCS) of multiple elite individuals is saved. Based on the LCS, the probability of some favorable gene blocks being destroyed will be reduced when performing the crossover operator and the mutate operator. Hence, the integration of the LCS and the selection process in GA can promote the capability of finding the optimal scheduling sequence. The experimental results show that the GA combined with LCS, named as GALCS in this paper, can find a better Pareto front than the ordinary GA as well as other 3 state-of-the-art algorithms. Furthermore, the effectiveness of the new proposed strategies are also verified by a set of experiments.