Data Mining Meets Performance Evaluation: Fast Algorithms for Modeling Bursty Traffic
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Fractal-Based Point Processes
DI-GRUBER: A Distributed Approach to Grid Resource Brokering
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Load Unbalancing to Improve Performance under Autocorrelated Traffic
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Analysis and modeling of job arrivals in a production grid
ACM SIGMETRICS Performance Evaluation Review
Analysis and Synthesis of Pseudo-Periodic Job Arrivals in Grids: A Matching Pursuit Approach
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A multifractal wavelet model with application to network traffic
IEEE Transactions on Information Theory
Future Generation Computer Systems
Model-based simulation and performance evaluation of grid scheduling strategies
Future Generation Computer Systems
Modeling Job Arrival Process with Long Range Dependence and Burstiness Characteristics
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
A Realistic Integrated Model of Parallel System Workloads
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
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Job arrivals can be described as point processes and it is shown that correlations and fractal behavior can be reliably revealed using the count/rate representation. Using real workload data from production Grids, we show that the second order properties such as the autocorrelation function (ACF) and the scaling behavior can be well reconstructed by a Multifractal Wavelet Model (MWM). A so-called controlled-variability integrate-and-fire (CV-InF) algorithm is applied to transform rates into interarrivals so that a full description of the arrival process can be obtained. The additive nature of rates makes it possible to model different patterns separately and aggregate them back to form a unified process. We further quantify the performance impacts of autocorrelated job arrivals in Grid scheduling using model-driven simulation. It is shown that autocorrelations in the arrival processes can cause performance degradation both at the local and the Grid level.