Workflow scheduling becomes difficult and demanding in multi-cloud systems because of the variety of billing models and resource kinds, as well as the susceptibility of processes to time limitations. In which the exec...
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Workflow scheduling becomes difficult and demanding in multi-cloud systems because of the variety of billing models and resource kinds, as well as the susceptibility of processes to time limitations. In which the execution duration of the workflow needs to be flexibly adjusted according to the urgency or otherwise of the real situation, in this work, we model the workflow scheduling problem with deadline constraints as a dynamically constrained multi-objective optimization problem (DCMOP), where the disruption rate of the special cloud resources and the failure probability in different cloud environments are expressed as the disruption probability of the workflow in a comprehensive manner while considering the execution time and cost. Dynamics arise from price changes for specialized cloud resources and changes in the execution duration of workflows. In addition, an algorithm for dynamically constrained multi-objective optimization (DC-MOEADPDS) is proposed in this paper. The algorithm based on two-population synergy as well as diversity selection. The auxiliary population ignores constraint limitations to help the main population speed up convergence, and diversity selection enables the auxiliary population to have better diversity to assist the general public in looking for more viable areas. Through dynamic constrained workflow simulation experiments in a multi-cloud environment, our algorithm reduces the execution time by an average of 13.29%, the cost by an average of 32.26%, and the interruption probability by 56.75%. In addition, our algorithm outperforms other algorithms in experiments on a dynamicallyconstrained benchmark set.
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