Multiple Range Query Optimization with Distributed Cache Indexing
Multiple Range Query Optimization with Distributed Cache Indexing
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Date
2006-04-17
Authors
Nam, Beomseok
Sussman, Alan
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Abstract
MQO is a distributed multiple query processing middleware that can
optimize query processing for data analysis applications on the Grid. It
has one or more proxies that act as front-end to a collection of backend
servers. The basic idea behind this architecture is 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 well used for query planning.
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 planning and scheduling. This approach is shown
to produce better query plans and faster query response times. We
experimentally demonstrate that system throughput can be improved up to
66%, compared to either load-based or round-robin scheduling.