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Static Probabilistic Timing Analysis of Random Replacement Caches using Lossy Compression

Published: 08 October 2014 Publication History

Abstract

The analysis of random replacement caches is an area that has recently attracted considerable attention in the field of probabilistic real-time systems. A major problem with performing static analysis on such a cache is that the relatively large number of successor states on a cache miss (equal to the cache associativity) renders approaches such as Collecting Semantics intractable. Other approaches must contend with non-trivial behaviours, such as the non-independence of accesses to the cache, which tends to lead to overly pessimistic or computationally expensive analyses.
Utilising techniques from the field of Lossy Compression, where compactly representing large volumes of data without losing valuable data is the norm, this paper outlines a technique for applying compression to the Collecting Semantics of a Random Replacement Cache. This yields a Must and May analysis. Experimental evaluation shows that, with appropriate parameters, this technique is more accurate and significantly faster than current state-of-the-art techniques.

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RTNS '14: Proceedings of the 22nd International Conference on Real-Time Networks and Systems
October 2014
335 pages
ISBN:9781450327275
DOI:10.1145/2659787
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 the author(s) 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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  • CEA: Commissariat à l'énergie atomique et aux énergies alternatives
  • GDR ASR: GDR Architecture, Systèmes et Réseaux

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Association for Computing Machinery

New York, NY, United States

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Published: 08 October 2014

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