An effective multi-objective workflow scheduling in cloud computing: A PSO based approach

Shubham, .; Gupta, Rishabh; Gajera, Vatsal; Jana, Prasanta K.

doi:10.1109/ic3.2016.7880196

Cited by 10 publications

(1 citation statement)

References 13 publications

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“…First of all, the popular list-based workflow scheduling methods were embedded into the multiobjective evolutionary optimization framework as evolution operators [14][15][16]. Secondly, bio-inspired evolution operators, such as artificial neural network [17][18][19], ant colony optimization [20], firefly algorithm [21], particle swarm optimization [22][23][24], and grey wolf optimization [25], were modified as evolution operators to solve the multi-objective workflow scheduling problem. Thirdly, integrating heuristic rules and bio-inspired optimization techniques to reproduce offspring populations has become a popular technological path.…”

Section: Introductionmentioning

confidence: 99%

Decision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling

Zhang

Xing

Zhong

et al. 2023

Complex Intell. Syst.

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Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.

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