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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers.

Mohamed S Halawa1, Rebeca P Díaz Redondo2, Ana Fernández Vilas2

  • 1Business Information Systems Department, Arab Academy for Science Technology and Maritime Transport, Cairo 11799, Egypt.

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|July 29, 2020
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Summary

Network traffic metrics are key for classifying high-performance computing (HPC) job behaviors. Hierarchical clustering effectively groups these jobs based on performance indicators.

Keywords:
clusteringhigh-performance computingtime series analysisunsupervised learning

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Area of Science:

  • High-performance computing (HPC)
  • Data science
  • System monitoring

Background:

  • Performance analysis is crucial in HPC for anomaly detection, resource allocation, and budget planning.
  • HPC systems generate numerous key performance indicators (KPIs) from CPU, memory, and network traffic to monitor job status.
  • Analyzing these KPIs provides insights into job characteristics, performance, and potential failures.

Purpose of the Study:

  • To identify the most effective key performance indicators (KPIs) for classifying high-performance computing (HPC) job behaviors.
  • To evaluate the suitability of different clustering techniques for HPC job classification.

Main Methods:

  • Application of partition and hierarchical clustering algorithms.
  • Utilized real-world datasets from the Galician Computation Center (CESGA).
  • Validated the approach with a second independent dataset from the same center.

Main Results:

  • Network (interface) traffic monitoring metrics demonstrated the highest cohesion and separation for clustering HPC jobs.
  • Hierarchical clustering algorithms proved most effective for this classification task.
  • The findings were consistent across two distinct real-world datasets.

Conclusions:

  • Network traffic KPIs are the most informative for understanding and classifying HPC job behavior.
  • Hierarchical clustering is the recommended method for analyzing and grouping HPC jobs based on performance data.
  • This research offers a validated approach for enhancing HPC system management and performance analysis.