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An analytical model of the working-set sizes in decision-support systems

Published: 01 June 2000 Publication History

Abstract

This paper presents an analytical model to study how working sets scale with database size and other applications parameters in decision-support systems (DSS). The model uses application parameters, that are measured on down-scaled database executions, to predict cache miss ratios for executions of large databases.
By applying the model to two database engines and typical DSS queries we find that, even for large databases, the most performance-critical working set is small and is caused by the instructions and private data that are required to access a single tuple. Consequently, its size is not affected by the database size. Surprisingly, database data may also exhibit temporal locality but the size of its working set critically depends on the structure of the query, the method of scanning, and the size and the content of the database.

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Published In

cover image ACM Conferences
SIGMETRICS '00: Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
June 2000
329 pages
ISBN:1581131941
DOI:10.1145/339331
  • cover image ACM SIGMETRICS Performance Evaluation Review
    ACM SIGMETRICS Performance Evaluation Review  Volume 28, Issue 1
    Special issue on proceedings of ACM SIGMETRICS 2000
    June 2000
    327 pages
    ISSN:0163-5999
    DOI:10.1145/345063
    Issue’s Table of Contents
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 2000

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SIGMETRICS '00 Paper Acceptance Rate 28 of 165 submissions, 17%;
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Cited By

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  • (2011)Estimating Application Cache Requirement for Provisioning Caches in Virtualized SystemsProceedings of the 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems10.1109/MASCOTS.2011.67(55-62)Online publication date: 25-Jul-2011
  • (2010)Data locality characterization of OLTP applications and its effects on cache performance2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE)10.1109/ICACTE.2010.5579552(V3-580-V3-584)Online publication date: Aug-2010
  • (2010)Generalized ERSS tree modelPerformance Evaluation10.1016/j.peva.2010.08.00467:11(1139-1154)Online publication date: 1-Nov-2010
  • (2007)Tashkent+ACM SIGOPS Operating Systems Review10.1145/1272998.127303741:3(399-412)Online publication date: 21-Mar-2007
  • (2007)Tashkent+Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 200710.1145/1272996.1273037(399-412)Online publication date: 21-Mar-2007
  • (2006)A page fault equation for modeling the effect of memory sizePerformance Evaluation10.1016/j.peva.2005.01.00763:2(99-130)Online publication date: 1-Feb-2006
  • (2005)The implications of working set analysis on supercomputing memory hierarchy designProceedings of the 19th annual international conference on Supercomputing10.1145/1088149.1088193(332-340)Online publication date: 20-Jun-2005
  • (2005)A methodology for detailed performance modeling of reduction computations on SMP machinesPerformance Evaluation10.1016/j.peva.2004.10.01760:1-4(73-105)Online publication date: 1-May-2005
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  • (2004)Dynamic tracking of page miss ratio curve for memory managementACM SIGARCH Computer Architecture News10.1145/1037947.102441532:5(177-188)Online publication date: 7-Oct-2004
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