Average-Case Competitive Ratio of Scheduling Algorithms of Multi-user Cache

Daniel Berend; Shlomi Dolev; Avinatan Hassidim; Marina Kogan-Sadetsky

doi:10.1007/978-3-030-49785-9_15

Average-Case Competitive Ratio of Scheduling Algorithms of Multi-user Cache

Daniel Berend, Shlomi Dolev, Avinatan Hassidim, Marina Kogan-Sadetsky

Department of Computer Science

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The goal of this paper is to present an efficient realistic metric for evaluating cache scheduling algorithms in multi-user multi-cache environments. In a previous work, the requests sequence was set deliberately by an opponent (offline optimal) algorithm in an extremely unrealistic way, leading to an unlimited competitive ratio and to extremely unreasonable and unrealistic cache management strategies. In this paper, we propose to analyze the performance of cache management in a typical scenario, i.e., we consider all possibilities with their (realistic) distribution. In other words, we analyze the average case and not the worst case of scheduling scenarios. In addition, we present an efficient, according to our novel average case analysis, online heuristic algorithm for cache scheduling. The algorithm is based on machine-learning concepts, it is flexible and easy to implement.

Original language	English
Title of host publication	Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings
Editors	Shlomi Dolev, Gera Weiss, Vladimir Kolesnikov, Sachin Lodha
Publisher	Springer
Pages	237-244
Number of pages	8
ISBN (Print)	9783030497842
DOIs	https://doi.org/10.1007/978-3-030-49785-9_15
State	Published - 2020
Event	4th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2020 - Beersheba, Israel Duration: 2 Jul 2020 → 3 Jul 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12161 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2020
Country/Territory	Israel
City	Beersheba
Period	2/07/20 → 3/07/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Funding

We thank the Lynne and William Frankel Center for Computer Science, the Rita Altura Trust Chair in Computer Science. This research was also supported by a grant from the Ministry of Science & Technology, Israel & the Japan Science and Technology Agency (JST), Japan, DFG German-Israeli collaborative projects, and the Milken Families Foundation Chair in Mathematics.

Funders	Funder number
Lynne and William Frankel Center for Computer Science
Ministry of Science & Technology, Israel & the Japan Science and Technology Agency
Rita Altura Trust
Milken Family Foundation
Deutsche Forschungsgemeinschaft
Japan Science and Technology Agency

Keywords

Competitive ratio
Concurrent cache
Scheduling algorithm

Access to Document

10.1007/978-3-030-49785-9_15

Cite this

Berend, D., Dolev, S., Hassidim, A., & Kogan-Sadetsky, M. (2020). Average-Case Competitive Ratio of Scheduling Algorithms of Multi-user Cache. In S. Dolev, G. Weiss, V. Kolesnikov, & S. Lodha (Eds.), Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings (pp. 237-244). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12161 LNCS). Springer. https://doi.org/10.1007/978-3-030-49785-9_15

Berend, Daniel ; Dolev, Shlomi ; Hassidim, Avinatan et al. / Average-Case Competitive Ratio of Scheduling Algorithms of Multi-user Cache. Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings. editor / Shlomi Dolev ; Gera Weiss ; Vladimir Kolesnikov ; Sachin Lodha. Springer, 2020. pp. 237-244 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The goal of this paper is to present an efficient realistic metric for evaluating cache scheduling algorithms in multi-user multi-cache environments. In a previous work, the requests sequence was set deliberately by an opponent (offline optimal) algorithm in an extremely unrealistic way, leading to an unlimited competitive ratio and to extremely unreasonable and unrealistic cache management strategies. In this paper, we propose to analyze the performance of cache management in a typical scenario, i.e., we consider all possibilities with their (realistic) distribution. In other words, we analyze the average case and not the worst case of scheduling scenarios. In addition, we present an efficient, according to our novel average case analysis, online heuristic algorithm for cache scheduling. The algorithm is based on machine-learning concepts, it is flexible and easy to implement.",

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Berend, D, Dolev, S, Hassidim, A & Kogan-Sadetsky, M 2020, Average-Case Competitive Ratio of Scheduling Algorithms of Multi-user Cache. in S Dolev, G Weiss, V Kolesnikov & S Lodha (eds), Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12161 LNCS, Springer, pp. 237-244, 4th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2020, Beersheba, Israel, 2/07/20. https://doi.org/10.1007/978-3-030-49785-9_15

Average-Case Competitive Ratio of Scheduling Algorithms of Multi-user Cache. / Berend, Daniel; Dolev, Shlomi; Hassidim, Avinatan et al.
Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings. ed. / Shlomi Dolev; Gera Weiss; Vladimir Kolesnikov; Sachin Lodha. Springer, 2020. p. 237-244 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12161 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Berend D, Dolev S, Hassidim A, Kogan-Sadetsky M. Average-Case Competitive Ratio of Scheduling Algorithms of Multi-user Cache. In Dolev S, Weiss G, Kolesnikov V, Lodha S, editors, Cyber Security Cryptography and Machine Learning - 4th International Symposium, CSCML 2020, Proceedings. Springer. 2020. p. 237-244. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-49785-9_15

Average-Case Competitive Ratio of Scheduling Algorithms of Multi-user Cache

Abstract

Publication series

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Bibliographical note

Funding

Keywords

Access to Document

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