Asymptotically Exact TTL-Approximations of the Cache Replacement Algorithms LRU(m) and h-LRU

Computer system and network performance can be significantly improved by caching frequently used information. When the cache size is limited, the cache replacement algorithm has an important impact on the effectiveness of caching. In this paper we introduce time-to-live (TTL) approximations to determine the cache hit probability of two classes of cache replacement algorithms: the recently introduced h-LRU and LRU(m). These approximations only require the requests to be generated according to a general Markovian arrival process (MAP). This includes phase-type renewal processes and the IRM model as special cases. We provide both numerical and theoretical support for the claim that the proposed TTL approximations are asymptotically exact. In particular, we show that the transient hit probability converges to the solution of a set of ODEs (under the IRM model), where the fixed point of the set of ODEs corresponds to the TTL approximation. We further show, by using synthetic and trace-based workloads, that h-LRU and LRU(m) perform alike, while the latter requires less work when a hit/miss occurs. We also show that, as opposed to LRU, h-LRU and LRU(m) are sensitive to the correlation between consecutive inter-request times.

[1]  Florin Ciucu,et al.  Exact analysis of TTL cache networks , 2014, Perform. Evaluation.

[2]  Giuliano Casale Building accurate workload models using Markovian arrival processes , 2011, SIGMETRICS '11.

[3]  Donald F. Towsley,et al.  Analysis of TTL-based cache networks , 2012, 6th International ICST Conference on Performance Evaluation Methodologies and Tools.

[4]  Philippe Robert,et al.  A versatile and accurate approximation for LRU cache performance , 2012, 2012 24th International Teletraffic Congress (ITC 24).

[5]  Benny Van Houdt,et al.  TTL approximations of the cache replacement algorithms LRU(m) and h-LRU , 2017, Perform. Evaluation.

[6]  Predrag R. Jelenkovic,et al.  Least-recently-used caching with dependent requests , 2004, Theor. Comput. Sci..

[7]  Giuseppe Bianchi,et al.  Check before storing: what is the performance price of content integrity verification in LRU caching? , 2013, CCRV.

[8]  E. G. Coffman,et al.  Stochastic Analysis of Computer Storage , 1987 .

[9]  Michele Garetto,et al.  A unified approach to the performance analysis of caching systems , 2013, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[10]  Predrag R. Jelenkovic,et al.  Asymptotic insensitivity of least-recently-used caching to statistical dependency , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[11]  Hao Che,et al.  Hierarchical Web caching systems: modeling, design and experimental results , 2002, IEEE J. Sel. Areas Commun..

[12]  Donald F. Towsley,et al.  Approximate Models for General Cache Networks , 2010, 2010 Proceedings IEEE INFOCOM.

[13]  F. Baccelli,et al.  Elements of Queueing Theory: Palm Martingale Calculus and Stochastic Recurrences , 2010 .

[14]  Benny Van Houdt,et al.  Transient and steady-state regime of a family of list-based cache replacement algorithms , 2015, Queueing Systems.

[15]  Gábor Horváth,et al.  A minimal representation of Markov arrival processes and a moments matching method , 2007, Perform. Evaluation.