Dynamic scheduling approach for data-intensive cloud environment

The scheduling problem domain in cloud environment recently has been extended to deal with two new phenomenon such as data-intensive and security constraints in cloud environment. However traditional scheduling approaches have been failed to deal with these new addition. In this paper, the system architecture along with security constraint model for data-intensive cloud environment is designed. Moreover, a novel security constraints scheduling approach to schedule all jobs in cloud environment efficiently without compromising required security level for each job is presented in this paper. In the regard of cloud security, swarm intelligence is highly capable to provide better solutions for such potentially intractable problems. Therefore, an Ant Colony Optimization based scheduling algorithm is proposed in this paper. Several meta-heuristic mathematical models as well as explanations have been introduced to deal with effective security constraint scheduling strategy. Meanwhile, the experimental results shows that the overall performance of proposed scheduling algorithm is better than other existing scheduling algorithms on four basic measurements: the optimization rate of throughput, cost, CPU time and security constraints.