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Practical prefetching via data compression

Published: 01 June 1993 Publication History

Abstract

An important issue that affects response time performance in current OODB and hypertext systems is the I/O involved in moving objects from slow memory to cache. A promising way to tackle this problem is to use prefetching, in which we predict the user's next page requests and get those pages into cache in the background. Current databases perform limited prefetching using techniques derived from older virtual memory systems. A novel idea of using data compression techniques for prefetching was recently advocated in [KrV, ViK], in which prefetchers based on the Lempel-Ziv data compressor (the UNIX compress command) were shown theoretically to be optimal in the limit. In this paper we analyze the practical aspects of using data compression techniques for prefetching. We adapt three well-known data compressors to get three simple, deterministic, and universal prefetchers. We simulate our prefetchers on sequences of page accesses derived from the OO1 and OO7 benchmarks and from CAD applications, and demonstrate significant reductions in fault-rate. We examine the important issues of cache replacement, size of the data structure used by the prefetcher, and problems arising from bursts of “fast” page requests (that leave virtually no time between adjacent requests for prefetching and book keeping). We conclude that prediction for prefetching based on data compression techniques holds great promise.

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cover image ACM Conferences
SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data
June 1993
566 pages
ISBN:0897915925
DOI:10.1145/170035
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 June 1993

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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  • (2023)ACEing the Bufferpool Management Paradigm for Modern Storage Devices2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00106(1326-1339)Online publication date: Apr-2023
  • (2021)RISE: Reducing I/O Contention in Staging-based Extreme-Scale In-situ Workflows2021 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/Cluster48925.2021.00021(146-156)Online publication date: Sep-2021
  • (2020)Segmented In-Advance Data Analytics for Fast Scientific DiscoveryIEEE Transactions on Cloud Computing10.1109/TCC.2016.25411428:2(432-442)Online publication date: 1-Apr-2020
  • (2019)Leveraging Machine Learning for Anticipatory Data Delivery in Extreme Scale In-situ Workflows2019 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER.2019.8891003(1-11)Online publication date: Sep-2019
  • (2018)StackerProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.5555/3291656.3291754(1-11)Online publication date: 11-Nov-2018
  • (2018)Toward an Understanding of Trust Repair in Human-Robot InteractionACM Transactions on Interactive Intelligent Systems10.1145/31816718:4(1-30)Online publication date: 16-Nov-2018
  • (2018)StackerProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC.2018.00076(1-11)Online publication date: 11-Nov-2018
  • (2017)An Adaptive IO Prefetching Approach for Virtualized Data CentersIEEE Transactions on Services Computing10.1109/TSC.2015.246929510:3(328-340)Online publication date: 1-May-2017
  • (2017)WA-Dataspaces: Exploring the Data Staging Abstractions for Wide-Area Distributed Scientific Workflows2017 46th International Conference on Parallel Processing (ICPP)10.1109/ICPP.2017.34(251-260)Online publication date: Aug-2017
  • (2016)PrefetchMLProceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems10.1145/2976767.2976775(318-328)Online publication date: 2-Oct-2016
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