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

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

TL;DR

Empirical validation confirms that QoSFlow's recommended configurations consistently match measured execution outcomes across different QoS constraints, enabling efficient QoS-driven scheduling through analytical reasoning rather than exhaustive testing.

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.

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

TL;DR

Empirical validation confirms that QoSFlow's recommended configurations consistently match measured execution outcomes across different QoS constraints, enabling efficient QoS-driven scheduling through analytical reasoning rather than exhaustive testing.

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.
Paper Structure (29 sections, 9 equations, 15 figures, 2 tables)

This paper contains 29 sections, 9 equations, 15 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Contributions
  3. Overview
  4. Problem statement
  5. Challenges
  6. Approach
  7. Phase 1: Workflow modeling and emulation
  8. Phase 2: Performance estimation and sensitivity analysis
  9. Phase 3: Region clustering and QoS-driven selection
  10. Methodology
  11. Model workflow characteristics
  12. Workflow DAG template construction
  13. Projection of workflow demand at a target scale
  14. Critical Path Identification and Sensitivity Analysis
  15. Region-based Configuration Clustering
  16. ...and 14 more sections

Figures (15)

  • Figure 1: QoSFlow system overview: Given a workflow and QoS constraints (e.g., minimize time with node/storage limits), QoSFlow uses sensitivity analysis to map configurations to DAG critical paths and outputs the best feasible schedule that satisfies the constraints.
  • Figure 2: (a) A workflow DAG and (b) I/O semantics.
  • Figure 3: QoSFlow methodology: (1) construct workflow DAG template, (2) project to target scale with I/O statistics, (3) enumerate all stage-storage configurations and compute critical-path makespans, (4) partition configuration space into performance regions, and (5) generate interpretable QoS-driven scheduling rules.
  • Figure 4: QoSFlow identification of performance regions with CART
  • Figure 5: Representative workflow DAGs with annotated levels: (a) 1000 Genome, (b) DDMD, and (c) PyFLEXTRKR.
  • ...and 10 more figures