Modeling correlated workloads by combining model based clustering and a localized sampling algorithm
Proceedings of the 21st annual international conference on Supercomputing
Future Generation Computer Systems
Long range dependent job arrival process and its implications in grid environments
Proceedings of the first international conference on Networks for grid applications
Model-based simulation and performance evaluation of grid scheduling strategies
Future Generation Computer Systems
Modeling resubmission in unreliable grids: the bottom-up approach
Euro-Par'09 Proceedings of the 2009 international conference on Parallel processing
Cloud resource usage: extreme distributions invalidating traditional capacity planning models
Proceedings of the 2nd international workshop on Scientific cloud computing
Cloud Resource Usage--Heavy Tailed Distributions Invalidating Traditional Capacity Planning Models
Journal of Grid Computing
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Pseudo-periodicity is one of the basic job arrival patterns on data-intensive clusters and Grids. In this paper, a signal decomposition methodology called matching pursuit is applied for analysis and synthesis of pseudoperiodic job arrival processes. The matching pursuit decomposition is well localized both in time and frequency, and it is naturally suited for analyzing non-stationary as well as stationary signals. The stationarity of the processes can be quantitatively measured by permutation en- tropy, with which the relationship between stationarity and modeling complexity is excellently explained. Quantitative methods based on the power spectrum are also provided to measure the degree of periodicity present in the data. Matching pursuit is further shown to be able to extract patterns from signals, which is an attractive feature from a modeling perspective. Real world workload data from production clusters and Grids are used to empirically evaluate the proposed measures and methodologies.