Fast convergence caching replacement algorithm based on dynamic classification for content-centric networks

Abstract One of the key research fields of content-centric networking (CCN) is to develop more efficient cache replacement policies to improve the hit ratio of CCN in-network caching. However, most of existing cache strategies designed mainly based on the time or frequency of content access, can not properly deal with the problem of the dynamicity of content popularity in the network. In this paper, we propose a fast convergence caching replacement algorithm based on dynamic classification method for CCN, named as FCDC. It develops a dynamic classification method to reduce the time complexity of cache inquiry, which achieves a higher caching hit rate in comparison to random classification method under dynamic change of content popularity. Meanwhile, in order to relieve the influence brought about by dynamic content popularity, it designs a weighting function to speed up cache hit rate convergence in the CCN router. Experimental results show that the proposed scheme outperforms the replacement policies related to least recently used (LRU) and recent usage frequency (RUF) in cache hit rate and resiliency when content popularity in the network varies.

[1]  Gwendal Simon,et al.  Caching Policies for In-Network Caching , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[2]  Guangyu Shi,et al.  TECC: Towards collaborative in-network caching guided by traffic engineering , 2012, 2012 Proceedings IEEE INFOCOM.

[3]  Bengt Ahlgren,et al.  A survey of information-centric networking , 2012, IEEE Communications Magazine.

[4]  Raj Jain,et al.  Architectures for the future networks and the next generation Internet: A survey , 2011, Comput. Commun..

[5]  Aditya Akella,et al.  Redundancy in network traffic: findings and implications , 2009, SIGMETRICS '09.

[6]  Jussi Kangasharju,et al.  Content Routers: Fetching Data on Network Path , 2011, 2011 IEEE International Conference on Communications (ICC).

[7]  Massimo Gallo,et al.  Modeling data transfer in content-centric networking , 2011, 2011 23rd International Teletraffic Congress (ITC).

[8]  Sang Lyul Min,et al.  LRFU: A Spectrum of Policies that Subsumes the Least Recently Used and Least Frequently Used Policies , 2001, IEEE Trans. Computers.

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

[10]  László Böszörményi,et al.  A survey of Web cache replacement strategies , 2003, CSUR.

[11]  Xin Wang,et al.  Popularity-driven coordinated caching in Named Data Networking , 2012, 2012 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS).

[12]  Young-Bae Ko,et al.  A recent popularity based dynamic cache management for Content Centric Networking , 2012, 2012 Fourth International Conference on Ubiquitous and Future Networks (ICUFN).

[13]  Alexander Afanasyev,et al.  journal homepage: www.elsevier.com/locate/comcom , 2022 .

[14]  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).

[15]  Van Jacobson,et al.  Networking named content , 2009, CoNEXT '09.

[16]  George Pavlou,et al.  Modelling and Evaluation of CCN-Caching Trees , 2011, Networking.