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 INDEX TERMS Spark, privacy task, cost optimization, hybrid cloud.

Strategic Joining and Optimal Pricing in the Hybrid Cloud System with Risk-sensitive Service Requests

“…Many solutions have been proposed to ensure the security of data. Currently, most of the studies on risk control strategies for data security in hybrid cloud focus on security assessment (Hudic et al, [5], Munodawafa et al, [6], Lee et al, [7]), access certification (Saravanan et al, [8], Manoharan, [9], Yeh et al, [10]), encrypted transmission (He et al, [11], Liu et al, [12]) and data diversion (Vulapula et al, [13], Xu et al, [14], Ullah et al, [15], Hu et al, [16]). Data diversion serves as a key strategy to address security and privacy concerns in a hybrid cloud environment.…”