Towards self-tuning memory management for data servers

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 degrada-tion. 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 track-ing, 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. 1 The Need for Memory Tuning Although memory is relatively inexpensive and modern computer systems are amply equipped with it, memory contention on heavily loaded data servers is a common cause of performance problems. The reasons are threefold: Servers are operating with a multitude of complex software, ranging from the operating system to database systems, object request brokers, and application services. Much of this software has been written so as t