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

Source: arXiv - 2602.23598v1

Overview

The paper presents QoSFlow, a novel performance‑modeling technique that lets engineers reason about the quality‑of‑service (QoS) guarantees of distributed scientific workflows without having to run every possible configuration. By automatically partitioning the massive configuration space into “behaviorally similar” regions, QoSFlow enables fast, analytical scheduling decisions that respect constraints such as deadline limits or resource‑usage caps.

Key Contributions

• Interpretable Sensitivity Modeling: Introduces a statistical method that quantifies how small changes in workflow parameters affect execution time, producing human‑readable “sensitivity regions.”
• Configuration Space Partitioning: Automatically divides the high‑dimensional configuration space into clusters where workflows exhibit comparable performance, dramatically reducing the search space.
• QoS‑Driven Scheduling Engine: Leverages the partitioned model to select configurations that satisfy arbitrary QoS constraints (e.g., deadline, resource subset) analytically rather than by brute‑force testing.
• Empirical Validation: Demonstrates on three real‑world scientific workflows that QoSFlow’s recommendations beat the strongest baseline heuristic by 27.38 % on average.
• Open‑Source Prototype: Provides a reference implementation that can be integrated with existing workflow management systems (e.g., Apache Airflow, Pegasus).

Methodology

1. Data Collection: Run a modest set of workflow executions across a diverse set of configurations (different numbers of compute nodes, memory allocations, data placement, etc.).
2. Statistical Sensitivity Analysis: For each configuration dimension, compute a sensitivity score that captures how much the execution time changes per unit change in that dimension. This is done using regression‑type models (e.g., Gaussian Process Regression) that remain interpretable.
3. Region Formation: Apply a clustering algorithm (e.g., DBSCAN) on the sensitivity vectors to group configurations that behave similarly. Each cluster defines a region with its own performance envelope (mean, variance).
4. QoS Query Engine: When a user specifies a QoS constraint (e.g., “finish within 2 h using ≤ 4 nodes”), the engine searches the region catalog for the smallest region that can satisfy the constraint, then picks a concrete configuration from that region.
5. Analytical Guarantees: Because each region is characterized by statistical bounds, the system can provide probabilistic guarantees (e.g., “99 % confidence the job finishes under the deadline”).

Results & Findings

• Prediction Accuracy: Across 200+ test runs, the predicted execution time fell within ±5 % of the measured time for 94 % of the cases.
• Search Space Reduction: Instead of exploring ~10⁶ possible configurations, QoSFlow needed only ~10³ sampled runs to build a reliable model.
• QoS Constraint Satisfaction: For deadline‑driven queries, the system met the deadline in 98 % of trials, compared to 71 % for the baseline heuristic.

Practical Implications

• Faster Workflow Deployment: DevOps teams can obtain near‑optimal resource allocations in seconds rather than hours of trial‑and‑error, accelerating time‑to‑science.
• Cost Savings: By avoiding over‑provisioning, cloud‑based scientific pipelines can reduce compute spend by up to a quarter while still meeting SLAs.
• Predictable Scheduling in Heterogeneous Environments: QoSFlow’s region‑based model works across on‑prem clusters, public clouds, and hybrid setups, enabling consistent QoS guarantees despite underlying hardware variability.
• Integration Path: The prototype exposes a REST API that can be plugged into existing workflow orchestrators, allowing automatic “QoS‑aware” task placement without rewriting workflow definitions.
• Beyond Science: Any distributed data‑processing pipeline (e.g., ETL jobs, ML model training pipelines) that faces variable runtime characteristics can adopt QoSFlow to meet latency or budget constraints.

Limitations & Future Work

• Sampling Overhead: The initial profiling phase still requires a non‑trivial number of runs; for extremely large workflows the cost may be prohibitive.
• Static Sensitivity Assumption: QoSFlow assumes that sensitivity patterns remain stable across runs; sudden changes in underlying hardware (e.g., new CPU generation) may invalidate existing regions.
• Limited to Quantitative QoS: The current model focuses on execution time and resource count; extending it to other QoS dimensions such as energy consumption or network bandwidth is left for future research.
• Scalability of Clustering: For workflows with thousands of tunable parameters, more sophisticated dimensionality‑reduction techniques may be needed to keep region formation tractable.

Overall, QoSFlow offers a compelling bridge between academic performance modeling and day‑to‑day workflow engineering, giving developers a practical tool to guarantee service quality without drowning in exhaustive experimentation.

Authors

• Md Hasanur Rashid
• Jesun Firoz
• Nathan R. Tallent
• Luanzheng Guo
• Meng Tang
• Dong Dai

Paper Information

• arXiv ID: 2602.23598v1
• Categories: cs.DC, cs.PF
• Published: February 27, 2026
• PDF: Download PDF