Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers

Sensors

Mohamed S. Halawa¹  Rebeca P.Díaz Redondo²  AnaFernández Vilas² 

[1] Business Information Systems Department, Arab Academy for Science Technology and Maritime Transport, Cairo 11799, Egypt;Information & Computing Lab, AtlanTTIC Research Center, Universidade de Vigo, 36310 Vigo, Spain;

关键词: high-performance computing;    time series analysis;    unsupervised learning;    clustering;   

DOI  :  10.3390/s20154111

来源: DOAJ

【 摘 要 】

Performance analysis is an essential task in high-performance computing (HPC) systems, and it is applied for different purposes, such as anomaly detection, optimal resource allocation, and budget planning. HPC monitoring tasks generate a huge number of key performance indicators (KPIs) to supervise the status of the jobs running in these systems. KPIs give data about CPU usage, memory usage, network (interface) traffic, or other sensors that monitor the hardware. Analyzing this data, it is possible to obtain insightful information about running jobs, such as their characteristics, performance, and failures. The main contribution in this paper was to identify which metric/s (KPIs) is/are the most appropriate to identify/classify different types of jobs according to their behavior in the HPC system. With this aim, we had applied different clustering techniques (partition and hierarchical clustering algorithms) using a real dataset from the Galician computation center (CESGA). We concluded that (i) those metrics (KPIs) related to the network (interface) traffic monitoring provided the best cohesion and separation to cluster HPC jobs, and (ii) hierarchical clustering algorithms were the most suitable for this task. Our approach was validated using a different real dataset from the same HPC center.

【 授权许可】

Unknown