Cache capacity-aware content centric networking under flash crowds. (April 2015)

• Record Type: Journal Article
• Authors: Kim, Dabin; Lee, Sung-Won; Ko, Young-Bae; Kim, Jae-Hoon
• Is Part Of: Journal of network and computer applications. Volume 50(2015)
• Page Start: 101
• Page End: 113
• Publication Date: 2015-04
• Subjects: Selective caching -- Cache-aware routing -- Content centric networking -- Flash crowds; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2014.06.008
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Content-centric networking (CCN) is a new networking paradigm to resolve the explosion of data traffic on the Internet caused by the rapid increase in file sharing and video streaming traffic. Networks with CCN avoid repeatedly delivering the same content of a link every time it is requested, as the content can be stored and transferred by CCN router caches. Two major features that are currently considered in CCN are in-network caching and content-aware routing. Even though both aspects are important, there is little comprehensive work on the interaction between them. In this paper, we propose a cache capacity-aware CCN that consists of selective caching and cache-aware routing methods that interact with each other to encompass cache management and cache-aware request forwarding. The main motivation of the proposed scheme is to utilize the network caches evenly and redirect a content request based on the previous forwarding of the desired contents (or chunks). To enable this function, we utilize a cache capacity metric based on recent cache memory consumption and reflect it to content replication according to the popularity information on the content server. We evaluate the proposed scheme with respect to existing cache replication algorithms and show that it leads to significant performance improvements and a better utilization of network caches.