Analysis of latency-aware caching strategies in information-centric networking

5G has loudly ambitioned to achieve extremely low latency in mobile networks. To this aim, we have recently introduced two novel latency-aware caching heuristics, LAC and LAC+ and we showed through simulations in Information-Centric Networks their good performance figures. In this paper, we present an insight on their operations: a mathematical analysis of these caching systems led us to novel results that we validate in simulation. The advantages of these algorithms come (i) on one side from the fact they are distributed and lightweight and (ii) from the ability to quickly adapt to content popularity and network congestion, with no signaling nor explicit coordination between the network nodes. In this paper we provide analytical bounds of latency aware caching policies and evaluate their performance by network simulations. The proposed mechanisms can halve the mean and standard deviation of content delivery time with respect to approximations of LFU as leave a copy probabilistically.

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

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

[3]  Giovanna Carofiglio,et al.  FOCAL: Forwarding and Caching with Latency Awareness in Information-Centric Networking , 2015, 2015 IEEE Globecom Workshops (GC Wkshps).

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

[5]  Li Fan,et al.  Web caching and Zipf-like distributions: evidence and implications , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[6]  Giovanna Carofiglio,et al.  LAC: Introducing latency-aware caching in Information-Centric Networks , 2015, 2015 IEEE 40th Conference on Local Computer Networks (LCN).

[7]  Nikolaos Laoutaris,et al.  Meta algorithms for hierarchical Web caches , 2004, IEEE International Conference on Performance, Computing, and Communications, 2004.

[8]  Michele Garetto,et al.  A unified approach to the performance analysis of caching systems , 2014, INFOCOM.

[9]  Niklas Carlsson,et al.  Characterizing web-based video sharing workloads , 2009, WWW '09.

[10]  Patrick Crowley,et al.  Named data networking , 2014, CCRV.

[11]  Dharmendra S. Modha,et al.  CAR: Clock with Adaptive Replacement , 2004, FAST.

[12]  James F. Kurose,et al.  Congestion-aware caching and search in information-centric networks , 2014, ICN '14.

[13]  David Tse,et al.  Probabilistic methods for web caching , 2001, Perform. Evaluation.

[14]  GarettoMichele,et al.  A Unified Approach to the Performance Analysis of Caching Systems , 2016 .

[15]  Massimo Gallo,et al.  On the performance of bandwidth and storage sharing in information-centric networks , 2013, Comput. Networks.

[16]  Predrag R. Jelenkovic,et al.  Optimizing LRU Caching for Variable Document Sizes , 2004, Combinatorics, Probability and Computing.

[17]  Walter L. Smith.  On the elementary renewal theorem for non-identically distributed variables , 1964 .

[18]  Nimrod Megiddo,et al.  ARC: A Self-Tuning, Low Overhead Replacement Cache , 2003, FAST.