Multi-Objective Security-Aware Workflow Scheduling with Soft Deadlines in Hybrid Clouds

Hybrid clouds allow service providers to combine secure private resources with elastic public resources, but most schedulers still have three practical limitations: they commonly model task security as a binary constraint, focus on independent tasks rather than dependent workflows, and treat deadlines as hard feasibility constraints rather than service-level objectives with economic penalties. This paper studies multi-objective workflow scheduling in hybrid clouds under multi-level security and soft-deadline constraints. We formulate a multi-objective scheduling problem that captures workflow precedence, heterogeneous private servers and public virtual machines, security-level matching, inter-cloud data transfer, revenue, execution costs, and deadline-violation penalties. We then propose MSWSS, a Multi-level Security-aware Workflow Scheduling with Soft Deadlines algorithm. MSWSS first ranks workflow tasks by deadline pressure, upward rank, security level, and penalty sensitivity; it then filters resources by security clearance; it next selects resources using a profit-aware multi-objective score; and finally it performs local migration and compaction to reduce execution cost and deadline loss. Across 100 independent repetitions at each tested synthetic setting, MSWSS consistently achieves the lowest mean execution cost relative to the three adapted policies, while satisfaction, profit, and deadline violation show setting-dependent trade-offs. In a separate paired default-setting comparison, HEFT obtains slightly better service metrics while MSWSS reduces mean cost by 34.73%. A targeted high-contention ablation further shows that refinement reduces mean cost by 14.12% while decreasing satisfaction and profit and increasing the violation ratio, confirming a cost–service trade-off rather than universal dominance.