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Scientific workflow scheduling in multi-cloud computing using a hybrid multi-objective optimization algorithm - 2021

Research Area:  Cloud Computing


Multi-cloud is the use of multiple cloud computing in a single heterogeneous architecture. Workflow scheduling in multi-cloud computing is an NP-Hard problem for which many heuristics and meta-heuristics are introduced. This paper first presents a hybrid multi-objective optimization algorithm denoted as HGSOA-GOA, which combines the Seagull Optimization Algorithm (SOA) and Grasshopper Optimization Algorithm (GOA). The HGSOA-GOA applies chaotic maps for producing random numbers and achieves a good trade-off between exploitation and exploration, leading to an improvement in the convergence rate. Then, HGSOA-GOA is applied for scientific workflow scheduling problems in multi-cloud computing environments by considering factors such as makespan, cost, energy, and throughput. In this algorithm, a solution from the Pareto front is selected using a knee-point method and then is applied for assigning the scientific workflows tasks in a multi-cloud environment. Extensive comparisons are conducted using the CloudSim and WorkflowSim tools and the results are compared to the SPEA2 algorithm. The achieved results exhibited that the HGSOA-GOA can outperform other algorithms in terms of metrics such as IGD, coverage ratio, and so on.

Author(s) Name:  Ali Mohammadzadeh & Mohammad Masdari

Journal name:  Journal of Ambient Intelligence and Humanized Computing

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s12652-021-03482-5

Volume Information:  Volume 2021