CEDCES: A Cost Effective Deadline Constrained Evolutionary Scheduler for Task Graphs in Multi-Cloud System

Abstract

Due to large computational resource requirements, a single cloud cannot meet the requirements of the workflow. Hence, a multi-cloud system, where multiple cloud providers pool their resources together becomes a good solution. The major objectives considered while scheduling the tasks present in a task graph include execution cost and makespan. In this paper, we present Cost Effective Deadline Constrained Evolutionary Scheduler (henceforth mentioned as CEDCES) which aims to minimize the execution cost under a given deadline constraint. CEDCES is a PSO based-method with novel initialization, crossover, and mutation schemes. Experiments on real-world workflows show that CEDCES outperforms the state-of-art algorithms, in particular, 60.41% on average in terms of execution cost. In cases where the deadline is violated, CEDCES gives the least overshoot in execution time and outperforming the others by 10.96% on average. © 2023 Owner/Author(s).