Towards Self-Tuning Memory Management for Data Servers

Gerhard Weikum, Arnd Christian König, Achim Kraiss, and Marcus Sinnwell

Abstract

Although today’s computers provide huge amounts of main memory, the ever-increasing load of large data servers, imposed by resource-intensive decision-support queries and accesses to multimedia and other complex data, often leads to memory contention and may result in severe performance degradation. Therefore, careful tuning of memory mangement is crucial for heavy-load data servers. This paper gives an overview of self-tuning methods for a spectrum of memory management issues, ranging from traditional caching to exploiting distributed memory in a server cluster and speculative prefetching in a Web-based system. The common, fundamental elements in these methods include on-line load tracking, near-future access prediction based on stochastic models and the available on-line statistics, and dynamic and automatic adjustment of control parameters in a feedback loop.

Details

Publication typeArticle
Published inData Engineering Bulletin 22(2)
PublisherIEEE Computer Society
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