LAUSR

With the increase in deployment of scientific workflow applications on an IaaS cloud computing environment, the distribution of workflow tasks to particular cloud instances to decrease runtime and cost has emerged as an important challenge. The cloud workflow scheduling is a well-known NP-hard problem. In this paper, we propose a new approach for multi-objective workflow scheduling in IaaS clouds offering a limited amount of instances and a flexible combination of instance types, and present a hybrid algorithm combining genetic algorithm, artificial bee colony optimization and decoding heuristic for scheduling workflow tasks over the available cloud resources while trying to optimize the workflow makespan and cost simultaneously. The proposed algorithm is evaluated for real-world scientific applications by a simulation process. The simulation results show that our proposed scheduling algorithm performs better than the current state-of-the-art algorithms. We validate the results by the Wilcoxon signed-rank test.