Multiple Range Query Optimization with Distributed Cache Indexing

Abstract

MQO is a distributed multiple query processing middleware that can use resources available on the grid to optimize query processing for data analysis and visualization applications. It does so by introducing one or more proxies that act as front-ends to a collection of backend servers. The basic idea behind this architecture is active semantic caching, whereby queries can leverage available cached results in the proxy either directly or through transformations. While this approach has been shown to speed up query evaluation under multi-client workloads, the caching infrastructure in the backend servers is not used well for query processing. Because this collective caching infrastructure scales with the number of servers, it is an important asset. In this paper, we describe a distributed multidimensional indexing scheme that enables the proxy to directly consider the cache contents available at the backend servers for query planning and scheduling. This approach is shown to produce better query plans and faster query response times as we experimentally demonstrate