Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
A sensitivity study of the clustering approach to workload modeling
Performance Evaluation
Clustering Algorithms
A package for the implementation of static workload models
SIGMETRICS '82 Proceedings of the 1982 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Quantifying behavioral differences between multimedia and general-purpose workloads
Journal of Systems Architecture: the EUROMICRO Journal
Finding representative workloads for computer system design
Finding representative workloads for computer system design
Proceedings of the 13th International Workshop on Software & Compilers for Embedded Systems
Surveying the landscape: an in-depth analysis of spatial database workloads
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Towards building performance models for data-intensive workloads in public clouds
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
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Clustering techniques are widely recommended tools for workload classification. The k-means algorithm is widely accepted as the "standard" technique of detecting workload classes automatically from measurement data. This paper examines validity of the obtained workload classes, when the current system and workload is analyzed by a queueing network model and mean value analysis. Our results, based on one week's accounting data of a VAX 8600, indicate that the results of queueing network analysis are not stable when the classes of workload are constructed through the k-means algorithm. Therefore, we cannot recommended that the most widely used clustering technique should be used in any workload characterization study without careful validation.