Hardware Support for Advanced Data Management Systems

Abstract

This thesis considers the problem of the optimal hardware architecture for advanced data management systems, of which the REL system can be considered a prototype. Exploration of the space of architectures requires a new technique which applies widely varying work loads, performance constraints, and heuristic configuration rules with an analytic queueing network model to develop cost functions which cover a representative range of organizational requirements. The model computes cost functions, which are the ultimate basis for comparison of architectures, from a technology forecast. Thc discussion shows the application of the modeling technique to thirty trial architectures which reflect the major classifications of data base machine architectures and memory technologies. The results suggest practical design considerations for advanced data management systems.