Hybrid Cloud Workflow Scheduling Method with Privacy Data

Abstract

Privacy protection is an important problem in workflow scheduling, for which scheduling tasks with privacy constraints to reliable resources is essential. In this article, we consider the scheduling problem of Spark applications in a hybrid cloud with deadline and privacy constraints. A scheduling algorithm framework is proposed, which consists of four algorithm components. Candidate strategies are developed for each algorithm component. The components and parameter values are statistically calibrated over a comprehensive set of random instances. The proposed algorithm is compared to modified classical algorithms for similar problems. The experimental results indicate that the proposed algorithm outperforms the compared algorithms under different application scales, deadlines, and private VMs