信息中心网络缓存节点位置选择算法
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摘要
信息中心网络(Information-Centric Networking, ICN)"泛在缓存"的特性,引发数据副本率过高、缓存空间不能充分利用等问题。为了解决上述问题,分别从用户、服务提供商和网络运营商的角度出发,以最小化网络流量与网络费用开销为优化目标建立两个单目标优化模型,并将二者结合为帕累托模型;基于帕累托求解方法中数学规划法的思想,详细描述缓存节点位置选择算法。仿真结果表明:在流量性价比方面,所提缓存节点选择算法优于ICN的默认缓存机制;在网络费用开销方面,所提算法更适用于只有少数内容较为流行的网络中,而在所有内容都流行的情况下,ICN中默认的"遍地缓存"机制更为适宜。
The cache ubiquity feature in ICN(information-centric networking) causes many problems such as higher data duplication rate, underutilization of cache space, etc. To solve such problems, the benefits of multiple network roles were considered. From the views of user-service provider and network operator, two single-objective optimization models were established respectively, aiming at minimizing the network traffic and the network expense, which were merged into the Pareto model. The proposed cache location selected algorithm was described based on the mathematical programming method of Pareto. Simulation results show that the proposed algorithm outperforms the default cache mechanism of ICN in terms of traffic cost-effective. In terms of network expense, the proposed algorithm is more applicable to ICN when there are a few popular contents. However, when all contents are popular, the default mechanism of "cache everywhere" in ICN is more applicable.
The cache ubiquity feature in ICN(information-centric networking) causes many problems such as higher data duplication rate, underutilization of cache space, etc. To solve such problems, the benefits of multiple network roles were considered. From the views of user-service provider and network operator, two single-objective optimization models were established respectively, aiming at minimizing the network traffic and the network expense, which were merged into the Pareto model. The proposed cache location selected algorithm was described based on the mathematical programming method of Pareto. Simulation results show that the proposed algorithm outperforms the default cache mechanism of ICN in terms of traffic cost-effective. In terms of network expense, the proposed algorithm is more applicable to ICN when there are a few popular contents. However, when all contents are popular, the default mechanism of "cache everywhere" in ICN is more applicable.
引文
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[2] Zhang L X, Afanasyev A, Burke J, et al. Named data networking[J]. ACM SIGCOMM Computer Communication Review, 2014, 44(3): 66-73.
[3] Jacobson V, Smetters D K, Thornton J D, et al. Networking named content[C]//Proceedings of ACM Conference on Emerging Networking Experiments and Technology, 2009: 117-124.
[4] Ioannou A, Weber S. A survey of caching policies and forwarding mechanisms in information-centric networking[J]. IEEE Communications Surveys & Tutorials, 2016, 18(4): 2847-2886.
[5] Zhang G Q, Li Y, Lin T. Caching in information centric networking: a survey[J]. Computer Networks, 2013, 57(16): 3128-3141.
[6] Chai W K, He D L, Psaras I, et al. Cache “less for more” in information-centric networks(extended version)[J]. Computer Communications, 2013, 36(7): 758-770.
[7] Wang S, Bi J, Wu J P, et al. CPHR: in-network caching for information-centric networking with partitioning and hash-routing[J]. IEEE/ACM Transactions on Networking, 2016, 24(5): 2742-2755.
[8] Feng B H, Zhou H C, Zhang H K, et al. A popularity-based cache consistency mechanism for Information-Centric Networking[C]//Proceedings of IEEE Global Communications Conference, 2016.
[9] Nguyen D, Sugiyama K, Tagami A. Congestion price for cache management in information-centric networking[C]//Proceedings of IEEE Conference on Computer Communications Workshops, 2015: 287-292.
[10] Hajimirsadeghi M, Mandayam N B, Reznik A. Joint caching and pricing strategies for popular content in information centric networks[J]. IEEE Journal on Selected Areas in Communications, 2017, 35(3): 654-667.
[11] Zhang M, Luo H B, Zhang H K. A survey of caching mechanisms in information-centric networking[J]. IEEE Communications Surveys & Tutorials, 2015, 17(3): 1473-1499.
[12] Wang Y G, Li Z Y, Tyson G, et al. Optimal cache allocation for content-centric networking[C]//Proceedings of IEEE International Conference on Network Protocols, 2013.
[13] Rossi D, Rossini G. On sizing CCN content stores by exploiting topological information[C]//Proceedings of IEEE Conference on Computer Communications Workshops, 2012: 280-285.
[14] Mangili M, Martignon F, Capone A. Optimal design of information centric networks[J]. Computer Networks, 2015, 91(C): 638-653.
[15] Krishnan P, Raz D, Shavitt Y. The cache location problem[J]. IEEE/ACM Transactions on Networking, 2000, 8(5): 568-582.
[16] Wang X W, Wang X Y, Che H, et al. An intelligent economic approach for dynamic resource allocation in cloud services[J]. IEEE Transactions on Cloud Computing, 2015, 3(3): 275-289.
[17] 孙佳佳, 王兴伟, 高程希, 等. 云环境下基于神经网络和群搜索优化的资源分配机制[J]. 软件学报, 2014, 25(8): 1858-1873.SUN Jiajia, WANG Xingwei, GAO Chengxi, et al. Resource allocation scheme based on neural network and group search optimization in cloud environment[J]. Journal of Software, 2014, 25(8): 1858-1873. (in Chinese)
[18] Burkard R E, Krarup J. A linear algorithm for the pos/neg-weighted 1-median problem on a cactus[J]. Computing, 2010, 60(3): 193-215.
[19] 林闯, 陈莹, 黄霁崴, 等. 服务计算中服务质量的多目标优化模型与求解研究[J]. 计算机学报, 2015, 38(10): 1907-1923.LIN Chuang, CHEN Ying, HUANG Jiwei, et al. A survey on models and solutions of multi-objective optimization for QoS in services computing[J]. Chinese Journal of Computers, 2015, 38(10): 1907-1923. (in Chinese)
[20] Floyd R W. Algorithm 97: shortest path[J]. Communications of the ACM, 1962, 5(6): 345.
[21] Lü J H, Wang X W, Huang M. Ant colony optimization-inspired ICN routing with content concentration and similarity relation[J]. IEEE Communications Letters, 2016, 21(6): 1313-1316.
[22] Lü J H, Wang X W, Ren K X, et al. ACO-inspired Information-Centric Networking routing mechanism[J]. Computer Networks, 2017, 126: 200-217.
[23] Bellman R E. Dynamic programming[M]. USA:Princeton University Press, 1957.
[24] Zipf G K. Relative frequency as a determinant of phonetic change[J]. Harvard studies in classical philology, 1929, 40: 1-95.
[25] Amazon. Amazon CloudFront pricing [EB/OL]. [2017-11-10]. https://aws.amazon.com/cn/cloudfront/pricing.
[26] Perino D, Varvello M. A reality check for content centric networking[C]//Proceedings of ACM SIGCOMM Workshop on Information-centric Networking, 2011: 44-49.