Efficient, economical and energy-saving multi-workflow scheduling in hybrid cloud.

Benefiting from the flexible, scalable and secure environment, hybrid cloud can overcome the shortage of limited resources in private cloud to simultaneously execute large-scale scientific workflows. In hybrid cloud, privacy-sensitive tasks are not allowed to be executed on public resources, while non-sensitive tasks are unrestricted. As an NP-Complete problem, it is extraordinarily challenging to schedule multiple workflows efficiently, economically and energy-savingly under quality-of-service constraints. This paper models the hybrid-cloud-based privacy-aware multi-workflow scheduling as a tri-objective optimization problem that optimizes workflow-oriented total tardiness, private-cloud-oriented total energy consumption, and public-cloud-oriented total monetary cost. To the best of authors’ knowledge, few studies have been conducted on the tri-objective privacy-aware multi-workflow scheduling in hybrid cloud (PMWS-HC). To solve this problem, we dissect various factors involved during task scheduling and devise a novel Heuristic Scheduling Algorithm based on 9 Factors (HSA9Fs), which dynamically selects the workflows and tasks to be scheduled, and the corresponding VMs to execute them. To optimize the three conflicting objectives simultaneously, we propose a nested algorithm called MSIA, which first employs a Multi-objective Salp swarm algorithm to explore for the Pareto solutions, and then uses an Iterative greedy Algorithm to perform a refined search on individuals to obtain high-quality solutions. Extensive Medium-Small-Scale and Large-Scale simulation experiments show that both HSA9Fs and MSIA outperform state-of-the-art scheduling algorithms in several multi-objective performance metrics.