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Competitive algorithms for replication and migration problems

作者: David L Black; Daniel D Sleator
出版商: Pittsburgh, Pa. : Carnegie Mellon University, Computer Science Dept., 1989.
叢書: Carnegie-Mellon University.; Computer Science Department.; Research paper
版本/格式:   圖書 : 英語所有版本和格式的總覽
資料庫:WorldCat
提要:
Abstract: "In this paper we consider problems that arise in a shared memory multiprocessor in which memory is physically distributed among a number of memories local to each processor or cluster of processors. The issue we address is that of deciding which local memories should contain copies of pages of data. In the migration problem we operate under the constraint that a page must be kept in exactly one local
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資料類型: 網際網路資源
文件類型: 圖書, 網路資源
所有的作者/貢獻者: David L Black; Daniel D Sleator
OCLC系統控制編碼: 21050103
注意: "November 1989."
描述: 23 pages ; 28 cm.
叢書名: Carnegie-Mellon University.; Computer Science Department.; Research paper
責任: David L. Black and Daniel D. Sleator.

摘要:

Abstract: "In this paper we consider problems that arise in a shared memory multiprocessor in which memory is physically distributed among a number of memories local to each processor or cluster of processors. The issue we address is that of deciding which local memories should contain copies of pages of data. In the migration problem we operate under the constraint that a page must be kept in exactly one local memory. In the replication problem we allow a page to be kept in any subset of the local memories, but do not allow a local memory to drop a page once it has it.

For interconnection topologies that are complete graphs, or trees we have obtained efficient on-line algorithms for these problems. Our migration algorithms also extend to interconnections that are products of these topologies (e.g. a hypercube is a product of simple trees). An on-line algorithm decides how to process each request (which is a read or write request from a processor to a page) without knowing future requests. Our algorithms are also said to be competitive because their performance is within a small constant factor of that of any other algorithm, including algorithms that make use of knowledge of future requests."

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