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Towards A Better Understanding of Workload Dynamics on Data-Intensive Clusters and Grids
2007
2007 IEEE International Parallel and Distributed Processing Symposium
This paper presents a comprehensive statistical analysis of workloads collected on data-intensive clusters and Grids. The analysis is conducted at different levels, including Virtual Organization (VO) and user behavior. The aggregation procedure and scaling analysis are applied to job arrival processes, leading to the identification of several basic patterns, namely, pseudo-periodicity, long range dependence (LRD), and (multi)fractals. It is shown that statistical measures based on
doi:10.1109/ipdps.2007.370250
dblp:conf/ipps/LiW07
fatcat:34h5nnovazalfiu6pxunyi3wpi