针对包含服务器、中继以及用户的分层网络，研究如何利用缓存降低传输延迟的问题。通过结合传统缓存和网络编码技术，提出新型的中心化和去中心化编码缓存方案。其中，中心化的方案根据中继、用户的数量以及缓存大小，对文件布置和发送策略进行优化设计，在满足用户文件请求的同时，实现数据的高效传输；去中心化的方案以牺牲少量性能为代价，支持用户数量变化和网络环境切换，拥有更高的灵活性。两种方案均充分利用中继的缓存资源，实现服务器和中继的并行传输，并根据用户的文件请求进行编码后发送，获得传统缓存方案所不具有的多播增益。仿真结果表明，本文的方案能够满足用户任意的文件请求，在不增加缓存大小的情况下，明显降低系统的传输延迟。
For a hierarchical network consisting of a server, multiple relays and multiple users, this paper studies on how to utilize cache at user and relay nodes to reduce the transmission delay. We propose novel coded caching schemes for the centralized and decentralized settings respectively. Our centralized scheme achieves better performance but requiring a fixing number of users, and our decentralized scheme supports flexible network change with only small loss of performance. Both schemes combine the traditional caching technology with network coding, and exploit the relays' cache resource to assist the transmission between the server and users. Moreover, our schemes allow parallel transmission between the server and relay, and achieve multicast gain by using coding during the delivery phase. The simulation results show that compared to the previous scheme, our schemes can greatly reduce the transmission delay without increasing the caching size.
[1] Cisco. Cisco visual networking index: global mobile data traffic forecast update, 2015-2020 white paper[R/OL]. 2016,Document ID 958959758.(2017-05-26) [2020-04-02]. http://www.cisco.com/c/en/us/solutions/service-provide/visual-networking-index-vni/index.html.
[2] Maddah-Ali M A, Niesen U. Fundamental limits of caching[J]. IEEE Transactions on Information Theory, 2014, 60(5): 2856-2867. DOI: 10.1109/TIT.2014.2306938.
[3] Maddah-Ali M A, Niesen U. Decentralized coded caching attains order-optimal memory-rate tradeoff[J]. IEEE/ACM Transactions on Networking, 2015, 23(4): 1029-1040. DOI:10.1109/TNET.2014.2317316.
[4] Karamchandani N, Niesen U, Maddah-Ali M A, et al. Hierarchical coded caching[J]. IEEE Transactions on Information Theory, 2016, 62(6): 3212-3229. DOI:10.1109/TIT.2016.2557804.
[5] Niesen U, Maddah-Ali M A. Coded caching with nonuniform demands[J]. IEEE Transactions on Information Theory, 2017, 63(2): 1146-1158.DOI:10.1109/TIT.2016.2639522.
[6] Shariatpanahi S P, Motahari S A, Khalaj B H. Multi-server coded caching[J]. IEEE Transactions on Information Theory, 2016, 62(12): 7253-7271.DOI:10.1109/TIT.2016.2614722.
[7] Li S Z, Maddah-Ali M A, Yu Q, et al. A fundamental tradeoff between computation and communication in distributed computing[J]. IEEE Transactions on Information Theory, 2018, 64(1): 109-128.DOI:10.1109/TIT.2017.2756959.
[8] Yan Q F, Cheng M Q, Tang X H, et al. On the placement delivery array design for centralized coded caching scheme[J]. IEEE Transactions on Information Theory, 2017, 63(9): 5821-5833.DOI:10.1109/TIT.2017.2725272.
[9] Shangguan C, Zhang Y W, Ge G N. Centralized coded caching schemes: a hypergraph theoretical approach[J]. IEEE Transactions on Information Theory, 2018, 64(8): 5755-5766.DOI:10.1109/TIT.2018.2847679.
[10] Yan Q F, Tang X H, Chen Q C, et al. Placement delivery array design through strong edge coloring of bipartite graphs[J]. IEEE Communications Letters, 2018, 22(2): 236-239.DOI:10.1109/LCOMM.2017.2765629.
[11] Saberali S A, Lampe L, Blake I F. Decentralized coded caching without file splitting[J]. IEEE Transactions on Wireless Communications, 2019, 18(2): 1289-1303.DOI:10.1109/TWC.2019.2891618.
[12] Ravindrakumar V, Panda P, Karamchandani N, et al. Private coded caching[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(3): 685-694.DOI:10.1109/TIFS.2017.2765503.
[13] Kamel S, Sarkiss M, Wigger M, et al. Secrecy capacitymemory tradeoff of erasure broadcast channels[J]. IEEE Transactions on Information Theory, 2019, 65(8): 5094-5124.DOI:10.1109/TIT.2019.2902578.
[14] Tandon R. The capacity of cache aided private information retrieval[C]//2017 55th Annual Allerton Conference on Communication, Control, and Computing. October 3-6, 2017, Monticello, IL, USA. IEEE, 2017: 1078-1082.DOI:10.1109/ALLERTON.2017.8262857.
