Workflow Scheduling in Cloud Computing Using Customised Weights Grey Wolf Optimisation
Keywords:
Optimisation Algorithms, Metaheuristic Algorithms, Customised Weights Grey wolf optimisation ,Cloud Computing, Workflow SchedulingAbstract
This study proposes a workflow scheduling approach that incorporates varying service quality demands in cloud environments. The main target of scheduling in the cloud is to minimize total execution time (TET) and total execution cost (TEC) while ensuring compliance with the service level agreement (SLA). Workflow scheduling is an NP-hard problem (Nondeterministic Polynomial time) that presents significant challenges in cloud computing systems. Traditional algorithms cannot efficiently solve the workflow scheduling problem in polynomial time. This paper presents a multi objective system for workflow scheduling using a customized weights grey wolf optimization (CWGWO) approach in cloud computing. The outcome of this algorithm is effectively balances between exploration and exploitation capabilities. It is designed to minimize the TET and TEC when tasks are interdependent. Experimental results indicate that the CWGWO algorithm outperforms both the Ant Colony Optimization (ACO) and Grey Wolf Optimization (GWO) algorithms in terms of TET and TEC.
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