The 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. © 1997 John Wiley & Sons, Ltd.

Mass storage systems are finding greater use in scientific computing research environments for retrieving and archiving the large volumes of data generated and manipulated by scientific computations. This paper presents a queuing network model that can be used to carry out capacity planning studies of hierarchical mass storage systems. Measurements taken on a Unitree mass storage system and a detailed workload characterization provided the workload intensity and resource demand parameters for the various types of read and write requests. The performance model developed here is based on approximations to multiclass Mean Value Analysis of queuing networks. The approximations were validated through the use of discrete event simulation and the complete model was validated and calibrated through measurements. The resulting model was used to analyze three different scenarios: effect of workload intensity increase, use of file compression at the server and client, and use of file abstractions.

Network attached storage devices improve I/O performance by separating control and data paths and eliminating host intervention during data transfer. Devices are attached to a high speed network for data transfer and to a slower network for control messages. Hierarchical mass storage systems use disks to cache the most recently used files and tapes (robotic and manually mounted) to store the bulk of the files in the file system. This paper shows how queuing network models can be used to assess the performance of hierarchical mass storage systems that use network attached storage devices. The analytic model validated through simulation was used to analyze many different scenarios.