research-article

Working Set Analytics

Author:

Peter J. DenningAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 6

Article No.: 113, Pages 1 - 36

https://doi.org/10.1145/3399709

Published: 02 February 2021 Publication History

Abstract

The working set model for program behavior was invented in 1965. It has stood the test of time in virtual memory management for over 50 years. It is considered the ideal for managing memory in operating systems and caches. Its superior performance was based on the principle of locality, which was discovered at the same time; locality is the observed tendency of programs to use distinct subsets of their pages over extended periods of time. This tutorial traces the development of working set theory from its origins to the present day. We will discuss the principle of locality and its experimental verification. We will show why working set memory management resists thrashing and generates near-optimal system throughput. We will present the powerful, linear-time algorithms for computing working set statistics and applying them to the design of memory systems. We will debunk several myths about locality and the performance of memory systems. We will conclude with a discussion of the application of the working set model in parallel systems, modern shared CPU caches, network edge caches, and inventory and logistics management.

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Sultan SShakiba KLee AChen PStumm M(2024)TTLs Matter: Efficient Cache Sizing with TTL-Aware Miss Ratio Curves and Working Set SizesProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650066(387-404)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3650066
Reber BGould MKneipp ALiu FPrechtl IDing CChen LPatru D(2023)Cache Programming for Scientific Loops Using LeasesACM Transactions on Architecture and Code Optimization10.1145/360009020:3(1-25)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3600090
Yang JQiu ZZhang YYue YRashmi KBaumann ACrooks NSchwarzkopf M(2023)FIFO can be Better than LRU: the Power of Lazy Promotion and Quick DemotionProceedings of the 19th Workshop on Hot Topics in Operating Systems10.1145/3593856.3595887(70-79)Online publication date: 22-Jun-2023
https://dl.acm.org/doi/10.1145/3593856.3595887
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Index Terms

Working Set Analytics

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 6

Invited Tutorial and Regular Papers

November 2021

803 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3441629

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Copyright © 2021 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 February 2021

Accepted: 01 May 2020

Received: 01 October 2019

Published in CSUR Volume 53, Issue 6

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Cited By

Sultan SShakiba KLee AChen PStumm M(2024)TTLs Matter: Efficient Cache Sizing with TTL-Aware Miss Ratio Curves and Working Set SizesProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650066(387-404)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3650066
Reber BGould MKneipp ALiu FPrechtl IDing CChen LPatru D(2023)Cache Programming for Scientific Loops Using LeasesACM Transactions on Architecture and Code Optimization10.1145/360009020:3(1-25)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3600090
Yang JQiu ZZhang YYue YRashmi KBaumann ACrooks NSchwarzkopf M(2023)FIFO can be Better than LRU: the Power of Lazy Promotion and Quick DemotionProceedings of the 19th Workshop on Hot Topics in Operating Systems10.1145/3593856.3595887(70-79)Online publication date: 22-Jun-2023
https://dl.acm.org/doi/10.1145/3593856.3595887
Haris RKhan KNhlabatsi ABarhamgi M(2023)A machine learning-based optimization approach for pre-copy live virtual machine migrationCluster Computing10.1007/s10586-023-04001-127:2(1293-1312)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1007/s10586-023-04001-1
Ding CChen DLiu FReber BSmith W(2022)CARL: Compiler Assigned Reference LeasingACM Transactions on Architecture and Code Optimization10.1145/349873019:1(1-28)Online publication date: 17-Mar-2022
https://dl.acm.org/doi/10.1145/3498730
Haris RKhan KNhlabatsi A(undefined)A Machine Learning-Based Optimization Approach for Pre-Copy Live Virtual Machine MigrationSSRN Electronic Journal10.2139/ssrn.4136053
https://doi.org/10.2139/ssrn.4136053

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