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Competitive algorithms for replication and migration problems

저자: David L Black; Daniel D Sleator
출판사: Pittsburgh, Pa. : Carnegie Mellon University, Computer Science Dept., 1989.
시리즈: Carnegie-Mellon University.; Computer Science Department.; Research paper
판/형식:   도서 : 영어모든 판과 형식 보기
데이터베이스:WorldCat
요약:
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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자료 유형: 인터넷 자료
문서 형식: 책, 인터넷 자원
모든 저자 / 참여자: David L Black; Daniel D Sleator
OCLC 번호: 21050103
메모: "November 1989."
설명: 23 pages ; 28 cm.
일련 제목: Carnegie-Mellon University.; Computer Science Department.; Research paper
책임: David L. Black and Daniel D. Sleator.

초록:

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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