Cloud-edge-end workflow scheduling with multiple privacy levels

• Published in: Journal of parallel and distributed computing Vol. 189; p. 104882
• Main Authors: Wang, Shuang, Yuan, Zian, Zhang, Xiaodong, Wu, Jiawen, Wang, Yamin
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2024
• Subjects: Cloud-edge-end computing; Multi privacy level constraints; Rental cost optimization; Workflow scheduling; Rental cost optimization; Cloud-edge-end computing; Workflow scheduling; Multi privacy level constraints
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2024.104882

The cloud-edge-end architecture satisfies the execution requirements of various workflow applications. However, owing to the diversity of resources, the complex hierarchical structure, and different privacy requirements for users, determining how to lease suitable cloud-edge-end resources, schedule multi-privacy-level workflow tasks, and optimize leasing costs is currently one of the key challenges in cloud computing. In this paper, we address the scheduling optimization problem of workflow applications containing tasks with multiple privacy levels. To tackle this problem, we propose a heuristic privacy-preserving workflow scheduling algorithm (PWHSA) designed to minimize rental costs which includes time parameter estimation, task sub-deadline division, scheduling sequence generation, task scheduling, and task adjustment, with candidate strategies developed for each component. These candidate strategies in each step undergo statistical calibration across a comprehensive set of workflow instances. We compare the proposed algorithm with modified classical algorithms that target similar problems. The experimental results demonstrate that the PWHSA algorithm outperforms the comparison algorithms while maintaining acceptable execution times. •We investigated the workflow scheduling with end-edge-cloud severs to minimize the rental cost with deadline constraints.•An algorithm is proposed for workflows with multi-privacy level tasks including partition, generation, scheduling, and adjustment.•The experimental results demonstrate that the proposed algorithm outperforms the comparison algorithms with acceptable execution time.