Pythia and Pythia/WK: tools for the performance analysis of mass storage systems

Abstract

SUMMARYThe constant growth on the demands imposed on hierarchical mass storage systems creates a need for frequent reconfiguration and upgrading to ensure that the response times and other performance metrics are within the desired service levels. This paper describes the design and operation of two tools, Pythia and Pythia/WK, that assist system managers and integrators in making cost-effective procurement decisions. Pythia automatically buids and solves an analytic model of a mass storage system based on a graphical description of the architecture of the system, and on a description of the workload imposed on the system. The use of a modeling wizard to perform this conversion from a graphical description of a mass storage system to an analytic model makes Pythia unique among analytic performance tools. Pythia/WK uses clustering algorithms to characterize the workload from the log files of the mass storage system. The resulting workload characterization is used as input to Pythia.