FFBAT: A security and cost-aware workflow scheduling approach combining firefly and bat algorithms

Cloud computing is distributed computing on a large scale driven by practical and effective operations, in which a pay-per-use framework provides dynamic scaling in response to the needs of workflow applications. Many existing cloud computing environments do not effectively employ security measures to counter security threats in task scheduling. To improve the scheduling system, we include security service to the scheduling process. However, adding security services to applications inevitably causes overhead in terms of computation time. The tradeoff between achieving high computing performance and providing the desired level of security protection imposes a big challenge for task scheduling. To solve this problem, we propose a security and cost aware scheduling algorithm for heterogeneous tasks in scientific workflow executed in a cloud. Our proposed algorithm is based on the hybrid optimization approach, which combines Firefly and Bat algorithms. The coding strategy is to minimize the total execution cost while meeting the deadline and risk rate constraints. The proposed system uses a multi-objective function, and the results indicate that our algorithm always outperforms the traditional algorithms.