Cluster analysis and workload classification

Abstract

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.