在信息中心网络(Information-Centric Network, ICN)中,利用网络内置缓存提高内容获取及传输效率是该网络构架最重要的特性。然而,网络内置的缓存存在应对大量的需要转发的内容时能力相对弱小,对内容放置缺乏均衡分布的问题。该文提出基于内容流行度和节点中心度匹配的缓存策略(Popularity and Centrality Based Caching Scheme, PCBCS),通过对经过的内容进行选择性缓存来提高内容分发沿路节点的缓存空间使用效率,减少缓存冗余。仿真结果表明,该文提出的算法和全局沿路缓存决策方案,LCD(Leave Copy Down)以及参数为0.7及0.3的Prob(copy with Probability)相比较,在服务器命中率上平均减少30%,在命中缓存内容所需的跳数上平均减少20%,最重要的是,和全局沿路缓存决策方案相比总体缓存替换数量平均减少了40%。
Information-Centric Network (ICN) architectures seek to provide the necessary foundations for a more cost-efficient content acquirement and content distribution using universal in-network caching, also universal in-network caching is a key design principle of many such architectures. Given that caching capacity of ICN is relatively small in comparison to the amount of forwarded content, a key aspect is balanced distribution of content among the available caches. The in-network caching resolution scheme is proposed in this paper, based on content popularity and node’s centrality, called PCBCS. It reduces caching redundancy and in turn, make more efficient utilization of available cache resources along a delivery path through selective caching of content passing. The proposed algorithm is compared with universal on-path caching and Leave Copy Down (LCD), also Prob (copy with probability) scheme with parameter of 0.7 and 0.3. The results show reduction of up to 30% in server hits, and up to 20% in the number of hops required to hit cached contents, but, most importantly, reduction of cache replacements up to 40% in comparison to universal caching.
ZHANG Guo-qiang, LI Yang, LIN Tao, et al. Survey of in-network caching techniques in information-centric networks[J]. Journal of Software, 2014, 25(1): 154-175. doi: 10.13328/j.cnki.jos.004494.
[2]
KUROSE J. Information-centric networking: The evolution from circuits to packets to content[J]. Computer Networks, 2014, 66: 112-120.
[3]
TANG X and CHANSON S T. Coordinated en-route Web caching[J]. IEEE Transactions on Computers, 2002, 51(6): 595-607.
[4]
WANG S, BI J, and WU J. Collaborative caching based on hash-routing for information-centric networking[C]. Proceedings of the 2013 ACM SIGCOMM, Hong Kong, 2013: 535-536.
[5]
PAVIOU G, PSARAS I, and WEI K C. Probabilistic in-network caching for information-centric networks[C]. Proceedings of ACM SIGCOMM ICN Workshop, Helsinki, 2012: 55-60.
CUI Xiandong, LIU Jiang, HUANG Tao, et al. A novel in-network caching scheme based on betweenness and replacement rate in content centric networking[J]. Journal of Electronics & Information Technology, 2014, 36(1): 1-7. doi: 10.3724/SP.J.1146.2013.00503.
[7]
HU Q, WU M, WANG D, et al. Lifetime-based greedy caching approach for content-centric networking[C]. 21st International Conference on Telecommunications, Lisbon, 2014: 426-430.
GE Guodong, GUO Yunfei, LIU Caixia, et al. A collaborative caching strategy for privacy protection in content centric networking[J]. Journal of Electronics & Information Technology, 2015, 37(5): 1220-1226. doi: 10.11999/ JEIT140874.
ZHU Yi, MI Zhengkun, WANGg Wennai, et al. A cache probability replacement policy based on content popularity in content centric networks[J]. Journal of Electronics & Information Technology, 2013, 35(6): 1305-1310. doi. 10.3724/SP.J1146.2012.01143.
[10]
MING Z X, XU M W, and WANG D. Age-based cooperative caching in information-centric networks[C]. IEEE INFOCOM Workshop on Emerging Design Choices in Name-oriented Networking, Orlando, USA, 2012: 268-273.
[11]
LEET D. On the existence of a spectrum of policies that subsumes the Least Recently Used (LRU) and Least Frequently Used (LFU) policies[C]. Proceedings of the 1999 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, Atlanta, 1999: 134-143.
[12]
CHE H, TUNG Y, and WANG Z. Hierarchical Web caching systems: Modeling, design and experimental results[J]. IEEE Selected Areas in Communications, 2012, 20(7): 1305-1314.
[13]
TRAVERSO S, AHMED M, GATETTO M, et al. Temporal locality in today's content caching: Why it matters and how to model it[J]. ACM SIGCOMM Computer Communications Review, 2013, 43(5): 5-12.
[14]
KARYPIS G, HAN E H, and KUMAR V. CHAMELEON: Hierarchical clustering algorithm using dynamic modeling[J]. IEEE Computer, 1999, 32(8): 68-75.
[15]
AFANASYEV A, MOISEENKO I, and ZHANG L. “ndnSIM: NDN Simulator for NS-3”[R]. University of California Technical Report, 2012.