Energy, Cost and Reliability-Aware Workflow Scheduling on Multi-Cloud Systems: A Multi-Objective Evolutionary Approach

Nowadays, cloud computing has become a suitable platform for hosting and executing workflow applications. As the diversity and scale of these applications continue to increase, single-cloud environments are becoming insufficient to meet users’ requirements. Instead, multi-cloud environments have emerged as an ideal solution. However, the complexity of workflow scheduling in multi-cloud environments increases significantly due to the diversified billing mechanisms, heightened reliability demands, and the requirements for reducing energy consumption. To address these challenges, this paper proposes a multi-objective evolutionary algorithm called ECRWSM for workflow scheduling on multi-cloud systems. First, ECRWSM utilizes the population initialization strategy to generate a population with excellent uniformity and sufficient randomness. Then, the diversification strategy is employed to thoroughly explore the solution space. Next, the individual enhancement strategy is used to further improve the solutions. Additionally, an external archive is maintained to store non-dominated solutions throughout the evolutionary process. Comprehensive experiments are conducted to validate the performance of ECRWSM. The experimental results demonstrate that our proposed algorithm ECRWSM outperforms both classical and recent scheduling algorithms.