MOWS: Multi-objective workflow scheduling in cloud computing based on heuristic algorithm

Abstract

Cloud computing is emerging with growing popularity in workflow scheduling, especially for scientific workflow. Deploying data-intensive workflows in the cloud brings new factors to be considered during specification and scheduling. Failure to establish intermediate data security may cause information leakage or data alteration in the cloud environment. Existing scheduling algorithms for the cloud disregard the interaction among tasks and its effects on application security requirements. To address this issue, we design a new systematic method that considers both tasks security demands and interactions in secure tasks placement in the cloud. In order to respect security and performance, we formulate a model for task scheduling and propose a heuristic algorithm which is based on task’s completion time and security requirements. In addition, we present a new attack response approach to reduce certain security threats in the cloud. To do so, we introduce task security sensitivity measurement to quantify tasks security requirements. We conduct extensive experiments to quantitatively evaluate the performance of our approach, using WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that compared with existing algorithms, our proposed solution can improved the overall system security in terms of quality of security and security risk under a wide range of workload characteristics. Additionally, our results demonstrate that the proposed attack response algorithm can effectively reduce cloud environment threats.