首页|Multi-objective workflow scheduling based on genetic algorithm in cloud environment
Multi-objective workflow scheduling based on genetic algorithm in cloud environment
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NSTL
Elsevier
? 2022 Elsevier Inc.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 a small 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, in which each task's data size is considered when initializing its virtual machine (VM) instance. 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 the early evolution stage, traditional crossover and mutate operators are performed to keep the population's exploration. On the contrary, the longest common subsequence (LCS) of multiple elite individuals, which can be regarded as a favorable gene block, is saved during the later evolution stage. 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 in GA can satisfy different requirements in different evolution stages, and then to attain a balance between the exploration and the exploitation. Extensive experimental results verify that the proposed GA combined with LCS, named as GALCS in this paper, can find a better Pareto front than the ordinary GA as well as other state-of-the-art algorithms. Furthermore, effectivenesses of the new proposed strategies are also verified by a set of experiments.
NSTL
Elsevier
