Cost and Reliability Aware Scheduling of Workflows Across Multiple Clouds with Security Constraints

Many real-world scientific workflows can be represented by a Directed Acyclic Graph (DAG), where each node represents a task and a directed edge signifies a dependency between two tasks. Due to the increasing computational resource requirements of these workflows, they are deployed on multi-cloud systems for execution. In this paper, we propose a complete framework for scheduling workflows in a multi-cloud system. In particular, we focus on minimizing the total execution time, and cost, and maximizing the reliability subject to security constraints. First, we use a Gravitational Search Algorithm (GSA) based approach to assign resources to tasks. Next, for a given task-resource mapping, we propose a cipher assignment algorithm that assigns security services to edges responsible for transferring data in a time-optimal manner subject to given security constraints. The proposed algorithms have been analyzed to understand their time and space requirements. We implement the algorithms and experiment with many real-world scientific workflows of varying sizes. We compare the performance of the proposed method with the state-of-art methods. We observe that our method outperforms the others in terms of cost and reliability, and is inferior in terms of makespan in some cases.