Towards Scheduling Data-Intensive and Privacy-Aware Workflows in Clouds

Nowadays, business or scientific workflows with a massive of data are springing up in clouds. To avoid security and privacy leakage issues, users' privacy or sensitive data may be restricted to being processed in some specified and trusted cloud datacenters. Meanwhile, users may also pay attention to the cost incurred by renting cloud resources. Therefore, new workflow scheduling algorithms should be developed to achieve a balance between economically utilizing the cloud resources and protection of users' data privacy and security. In this paper, we propose a cost-aware scheduling algorithm for executing multiple data-intensive and privacy-aware workflow instances in clouds. Our proposed algorithm is based on the strategy of batch processing, the ideas of simulated annealing algorithm and the particle swarm optimization, the coding strategy of which is devised to minimize the total execution cost while meeting specified privacy protection constraints. The experimental results demonstrate the effectiveness of our algorithm.