Servicing Mixed Data Intensive Query Workloads

Abstract

When data analysis applications are employed in a multi-client environment, a data server must service multiple simultaneous queries, each of which may employ complex user-defined data structures and operations on the data. It is then necessary to harness inter- and intra-query commonalities and system resources to improve the performance of the data server. We have developed a framework and customizable middleware to enable reuse of intermediate and final results among queries, through an in-memory semantic cache and user-defined transformation functions. Since resources such as processing power and memory space are limited on the machine hosting the server, effective scheduling of incoming queries and efficient cache replacement policies are challenging issues that must be addressed. We have addressed the scheduling problem in earlier work, and in this paper we describe and evaluate several cache replacement policies. We present experimental evaluation of the policies on a shared-memory parallel system using two applications from different domains. Also UMIACS-TR-2002-21