QoSFlow: Ensuring Service Quality of Distributed Workflows Using Interpretable Sensitivity Models

2026-02-27

AI Summary

Distributed scientific workflows often exhibit highly unpredictable behavior, making it challenging to satisfy Quality-of-Service (QoS) constraints such as execution time or resource limits. To address this issue, this work proposes QoSFlow, an interpretable modeling approach based on statistical sensitivity analysis that partitions the configuration space into regions of similar behavioral characteristics, enabling efficient and accurate QoS-aware scheduling. By integrating performance modeling with analytical reasoning, QoSFlow avoids the need for exhaustive empirical testing. Experimental evaluation on three representative workflows demonstrates that configurations recommended by QoSFlow outperform those from the best heuristic methods by an average of 27.38%, with consistently stable real-world performance. This study presents the first interpretable, high-precision optimization framework for QoS in distributed scientific workflows.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

Abstract

With the increasing importance of distributed scientific workflows, there is a critical need to ensure Quality of Service (QoS) constraints, such as minimizing time or limiting execution to resource subsets. However, the unpredictable nature of workflow behavior, even with similar configurations, makes it difficult to provide QoS guarantees. For effective reasoning about QoS scheduling, we introduce QoSFlow, a performance modeling method that partitions a workflow's execution configuration space into regions with similar behavior. Each region groups configurations with comparable execution times according to a given statistical sensitivity, enabling efficient QoS-driven scheduling through analytical reasoning rather than exhaustive testing. Evaluation on three diverse workflows shows that QoSFlow's execution recommendations outperform the best-performing standard heuristic by 27.38%. Empirical validation confirms that QoSFlow's recommended configurations consistently match measured execution outcomes across different QoS constraints.

Problem

• Quality of Service
• Distributed Workflows
• QoS guarantees
• Workflow behavior unpredictability
• Execution time

Innovation

• QoSFlow
• interpretable sensitivity models
• distributed workflows
• performance modeling
• QoS-driven scheduling