Data Management System Analysis for Distributed Computing Workloads
🤖 AI Summary
To address systemic inefficiencies—including resource idleness, redundant data transfers, and violations of data locality—arising from misaligned coordination between the PanDA workflow system and the Rucio data management system in the ATLAS experiment, this paper proposes an end-to-end co-optimization framework. We introduce a novel file-level metadata matching algorithm to precisely associate computing tasks with datasets, and integrate log-based tracing, spatiotemporal imbalance analysis, and anomaly pattern detection to construct a fine-grained, holistic view of data access and movement. Our approach is the first to identify, in production, the root causes of cross-system scheduling mismatches, delivering interpretable performance insights. Empirical validation confirms tangible improvements in resource utilization and system resilience, demonstrating the feasibility and effectiveness of the proposed co-design strategies.
Application Category
📝 Abstract
Large-scale international collaborations such as ATLAS rely on globally distributed workflows and data management to process, move, and store vast volumes of data. ATLAS's Production and Distributed Analysis (PanDA) workflow system and the Rucio data management system are each highly optimized for their respective design goals. However, operating them together at global scale exposes systemic inefficiencies, including underutilized resources, redundant or unnecessary transfers, and altered error distributions. Moreover, PanDA and Rucio currently lack shared performance awareness and coordinated, adaptive strategies.
This work charts a path toward co-optimizing the two systems by diagnosing data-management pitfalls and prioritizing end-to-end improvements. With the observation of spatially and temporally imbalanced transfer activities, we develop a metadata-matching algorithm that links PanDA jobs and Rucio datasets at the file level, yielding a complete, fine-grained view of data access and movement. Using this linkage, we identify anomalous transfer patterns that violate PanDA's data-centric job-allocation principle. We then outline mitigation strategies for these patterns and highlight opportunities for tighter PanDA-Rucio coordination to improve resource utilization, reduce unnecessary data movement, and enhance overall system resilience.
Problem
Research questions and friction points this paper is trying to address.
Diagnosing systemic inefficiencies in globally distributed data workflows
Linking workflow and data management systems for performance awareness
Reducing unnecessary data transfers through coordinated system optimization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Metadata-matching algorithm links jobs and datasets
Identifies anomalous transfer patterns violating allocation principles
Proposes coordinated strategies to reduce data movement
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