Fairness-Constrained Multiple-Workflow Scheduling Through Stochastic Ranking

Workflow scheduling has been extensively studied in distributed computing, with most research primarily focusing on single workflow scheduling problems. However, in real-world scenarios, multiple workflows from different individual users often need to be scheduled concurrently on shared computing resources, which raises significant fairness concerns among these workflows. Existing approaches typically overlook fairness in multiple workflow scheduling, leading to disproportionate completion time slowdowns across different workflows. To address this challenge, we introduce a novel fairness metric that quantitatively captures the slowdown disparity among multiple workflows and propose the FairFlowSR (Fair-Flow Stochastic Ranking) algorithm to ensure fairness among concurrent workflows. The FairFlowSR algorithm integrates two key components: a fair selection strategy that balances exploitation and exploration, and a stochastic ranking method that effectively handles fairness constraints. Extensive experimental results demonstrate that FairFlowSR significantly outperforms state-of-the-art algorithms, achieving superior fairness maintenance and competitive makespan optimization. These results validate the effectiveness of our approach in achieving a balanced trade-off between efficiency and fairness in multiple-workflow scheduling scenarios.