Scheduling a Metacomputer with Uncooperative Sub-schedulers
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
Simulating Load Balancing on Heterogeneous Workstation Clusters
ParNum '99 Proceedings of the 4th International ACPC Conference Including Special Tracks on Parallel Numerics and Parallel Computing in Image Processing, Video Processing, and Multimedia: Parallel Computation
Workload Characterization Issues and Methodologies
Performance Evaluation: Origins and Directions
Workload Modeling for Performance Evaluation
Performance Evaluation of Complex Systems: Techniques and Tools, Performance 2002, Tutorial Lectures
On grid performance evaluation using synthetic workloads
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
Optimizing the access to read-only data in grid computing
DAIS'05 Proceedings of the 5th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
Cost-Benefit Analysis of Virtualizing Batch Systems: Performance-Energy-Dependability Trade-Offs
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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The knowledge of workload fluctuations is fundamental in all performance studies in which the dynantic characteristics of the resource demands must be taken into account. Among the several workload data sequences that may be considered, the arrival pattern of workload components is certainly one of the most important. An approach to the identification of the arrival rate functions through a numerical fitting technique that allows one to have concise representations of the arrival patterns during one-day periods is presented. The variability of the arrival pattern over different days is also investigated. The patterns which may be considered as "representatives" of the analyzed workload are found through the application of the clustering technique. The parametric model of the arrival process may easily be used to forecast the load of the system in the near future or to drive system simulations when several dynamic control policies are to be investigated. The proposed modeling approach has been validated, in the sense of establishing its predictive value, through the analysis of workload data for the same installation collected during different periods.