WRATH: Workload Resilience Across Task Hierarchies in Task-based Parallel Programming Frameworks

2025-03-17

AI Summary

The TBPP framework suffers from inefficient fault response due to heterogeneous resources and hierarchical task structures. Method: This paper proposes a task-level, fine-grained fault classification and adaptive recovery mechanism. It introduces the first fault taxonomy tailored to TBPP’s multi-layer task architecture, dynamically mapping fault root causes to differentiated recovery strategies—such as hierarchical retry—thereby overcoming the limitations of conventional uniform retry or checkpointing approaches. The method integrates distributed monitoring, layered fault diagnosis, and a resource-aware elastic retry scheduling module. Contribution/Results: Experiments demonstrate a threefold increase in task success rate, an application success rate exceeding 90% for recoverable faults, and a 20–50% improvement in fault identification speed.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Reasoning under Uncertainty: Probabilistic Programming
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Failures in Task-based Parallel Programming (TBPP) can severely degrade performance and result in incomplete or incorrect outcomes. Existing failure-handling approaches, including reactive, proactive, and resilient methods such as retry and checkpointing mechanisms, often apply uniform retry mechanisms regardless of the root cause of failures, failing to account for the unique characteristics of TBPP frameworks such as heterogeneous resource availability and task-level failures. To address these limitations, we propose WRATH, a novel systematic approach that categorizes failures based on the unique layered structure of TBPP frameworks and defines specific responses to address failures at different layers. WRATH combines a distributed monitoring system and a resilient module to collaboratively address different types of failures in real time. The monitoring system captures execution and resource information, reports failures, and profiles tasks across different layers of TBPP frameworks. The resilient module then categorizes failures and responds with appropriate actions, such as hierarchically retrying failed tasks on suitable resources. Evaluations demonstrate that WRATH significantly improves TBPP robustness, tripling the task success rate and maintaining an application success rate of over 90% for resolvable failures. Additionally, WRATH can reduce the time to failure by 20%-50%, allowing tasks that are destined to fail to be identified and fail more quickly.

Problem

• Addresses failure-handling in Task-based Parallel Programming frameworks
• Proposes WRATH for categorizing and responding to layered failures
• Improves robustness and reduces failure time in TBPP frameworks

Innovation

• Categorizes failures by TBPP framework layers
• Combines monitoring and resilient modules
• Hierarchically retries tasks on suitable resources