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Abstract: The data center network (DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.

云计算数据中心网络结构：回顾、分类与研究热点展望

研究目的：数据中心网络由大量服务器主机与数据交换设备经高速网络互连，是数据中心的重要组成部分。数据中心能够通过建立集中化的数据资源向终端用户按需提供信息与服务。近年来，基于云计算的服务大量增加，由此产生的数据中心内（间）大规模数据流量，使得数据中心网络规模不断扩大，而传统的数据中心网络结构随着云服务的用户增加，在带宽汇聚、扩展性、性价比等方面的表现不尽如人意。因此，迫切需要一个具备良好可扩展性、高性价比、高稳定性以及低能耗的新型数据中心网络结构。
文章内容：回顾了近年来数据中心网络结构的研究发现和相关技术，指出现有云计算数据中心网络结构的特点。将现有多种数据中心网络结构按照树形(Clos/tree-based)，负载均衡（valiant load balancing），递归（hierarchically recursive），光/无线（optical/wireless），以及随机连接（randomly connected）五个方面进行分类，详细介绍各个类别下的代表结构，并对这些网络结构进行横向比较，选取的指标包括带宽、容错、可扩展性、开销以及网络搭建大致费用。最后，从可扩展性、成本、稳定性、能效等方面，对未来面向云计算的数据中心网络结构的研究热点进行展望。
数据中心网络；云计算；结构；网络拓扑

[1]Abu-Libdeh, H., Costa, P., Rowstron, A., et al., 2010. Symbiotic routing in future data centers. ACM SIGCOMM Comput. Commun. Rev., 40(4):51-62.

[2]Al-Fares, M., Loukissas, A., Vahdat, A., 2008. A scalable, commodity data center network architecture. ACM SIGCOMM Comput. Commun. Rev., 38(4):63-74.

[3]Alon, N., Roichman, Y., 1994. Random Cayley graphs and expanders. Random Struct. Algor., 5(2):271-284.

[4]Armbrust, M., Fox, A., Griffith, R., et al., 2010. A view of cloud computing. Commun. ACM, 53(4):50-58.

[5]Barabási, A.L., Albert, R., 1999. Emergence of scaling in random networks. Science, 286(5439):509-512.

[6]Beimborn, D., Miletzki, T., Wenzel, S., 2011. Platform as a service (PaaS). Bus. Inform. Syst. Eng., 3(6):381-384.

[7]Beloglazov, A., Buyya, R., 2010. Energy efficient resource management in virtualized cloud data centers. Proc. 10th IEEE/ACM Int. Conf. on Cluster, Cloud and Grid Computing, p.826-831.

[8]Bhardwaj, S., Jain, L., Jain, S., 2010. Cloud computing: a study of infrastructure as a service (IaaS). Int. J. Eng. Inform. Technol., 2(1):60-63.

[9]Bilal, K., Khan, S.U., Kolodziej, J., et al., 2012. A comparative study of data center network architectures. 26th European Conf. on Modelling and Simulation, p.526-532.

[10]Bilal, K., Khan, S.U., Zhang, L., et al., 2013a. Quantitative comparisons of the state-of-the-art data center architectures. Concurr. Comput. Pract. Exp., 25(12):1771-1783.

[11]Bilal, K., Manzano, M., Khan, S.U., et al., 2013b. On the characterization of the structural robustness of data center networks. IEEE Trans. Cloud Comput., 1(1):64-77.

[12]Borthakur, D., 2007. The Hadoop Distributed File System: Architecture and Design. Available from http://svn.eu.apache.org [Accessed on Jan. 13, 2014].

[13]Boru, D., Kliazovich, D., Granelli, F., et al., 2013. Energy-efficient data replication in cloud computing datacenters. IEEE Globecom Int. Workshop on Cloud Computing Systems, Networks, and Applications, p.446-451.

[14]Buxmann, P., Hess, T., Lehmann, S., 2008. Software as a service. Wirtschaftsinformatik, 50(6):500-503.

[15]Buyya, R., Yeo, C.S., Venugopal, S., 2008. Market-oriented cloud computing: vision, hype, and reality for delivering IT services as computing utilities. 10th IEEE Int. Conf. on High Performance Computing and Communications, p.5-13.

[16]Chang, F., Dean, J., Ghemawat, S., et al., 2008. Bigtable: a distributed storage system for structured data. ACM Trans. Comput. Syst., 26(2):1-26.

[17]Chen, K., Singla, A., Singh, A., et al., 2012a. OSA: an optical switching architecture for data center networks with unprecedented flexibility. Proc. 9th USENIX Conf. on Networked Systems Design and Implementation.

[18]Chen, Y., Griffith, R., Liu, J., et al., 2009. Understanding TCP incast throughput collapse in datacenter networks. Proc. 1st ACM Workshop on Research on Enterprise Networking, p.73-82.

[19]Chen, Y., Alspaugh, S., Borthakur, D., et al., 2012. Energy efficiency for large-scale MapReduce workloads with significant interactive analysis. Proc. 7th ACM European Conf. on Computer Systems, p.43-56.

[20]Cisco Data Center, 2007. Infrastructure 2.5 Design Guide.

[21]Clos, C., 1953. A study of non-blocking switching networks. Bell Syst. Techn. J., 32(2):406-424.

[22]Cui, Y., Wang, H., Cheng, X., et al., 2011. Wireless data center networking. IEEE Wirel. Commun., 18(6):46-53.

[23]Dally, W.J., Towles, B., 2004. Principles and Practices of Interconnection Networks. Morgan Kaufmann, San Francisco, CA, USA.

[24]Dean, J., Ghemawat, S., 2008. MapReduce: simplified data processing on large clusters. Commun. ACM, 51(1):107-113.

[25]Ding, Z., Guo, D., Liu, X., et al., 2012. A MapReduce-supported network structure for data centers. Concurr. Comput. Pract. Exp., 24(12):1271-1295.

[26]Droms, R., 1997. Dynamic Host Configuration Protocol. RFC Editor, United States.

[27]Farrington, N., Porter, G., Radhakrishnan, S., et al., 2011. Helios: a hybrid electrical/optical switch architecture for modular data centers. ACM SIGCOMM Comput. Commun. Rev., 41(4):339-350.

[28]Formu, J., 2009. Cloud Cube Model: Selecting Cloud Formations for Secure Collaboration.

[29]Foster, I., Kesselman, C., Nick, J., et al., 2002. Grid services for distributed system integration. Computer, 35(6):37-46.

[30]Frécon, E., Stenius, M., 1998. Dive: a scaleable network architecture for distributed virtual environments. Distr. Syst. Eng., 5(3):91-100.

[31]Gantz, J., Reinsel, D., 2012. The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the Future.

[32]Ghemawat, S., Gobioff, H., Leung, S.T., 2003. The Google File System. ACM SIGOPS Oper. Syst. Rev., 37(5):29-43.

[33]Greenberg, A., Hamilton, J., Maltz, D.A., et al., 2008a. The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput. Commun. Rev., 39(1):68-73.

[34]Greenberg, A., Lahiri, P., Maltz, D., et al., 2008b. Towards a next generation data center architecture: scalability and commoditization. Proc. ACM Workshop on Programmable Routers for Extensible Services of Tomorrow, p.57-62.

[35]Greenberg, A., Hamilton, J.R., Jain, N., et al., 2009. Vl2: a scalable and flexible data center network. ACM SIGCOMM Comput. Commun. Rev., 39(4):51-62.

[36]Guo, C., Wu, H., Tan, K., et al., 2008. DCell: a scalable and fault-tolerant network structure for data centers. ACM SIGCOMM Comput. Commun. Rev., 38(4):75-86.

[37]Guo, C., Lu, G., Li, D., et al., 2009. BCube: a high performance, server-centric network architecture for modular data centers. ACM SIGCOMM Comput. Commun. Rev., 39(4):63-74.

[38]Gyarmati, L., Trinh, T., 2010. Scafida: a scale-free network inspired data center architecture. ACM SIGCOMM Comput. Commun. Rev., 40(5):4-12.

[39]Heller, B., Seetharaman, S., Mahadevan, P., et al., 2010. ElasticTree: saving energy in data center networks. Proc. 7th USENIX Conf. on Networked Systems Design and Implementation, p.19-21.

[40]Ikeda, T., Tsutsumi, O., 1995. Optical switching and image storage by means of azobenzene liquid-crystal films. Science, 268(5219):1873-1875.

[41]Isard, M., Budiu, M., Yu, Y., et al., 2007. Dryad: distributed data-parallel programs from sequential building blocks. ACM SIGOPS Operat. Syst. Rev., 41(3):59-72.

[42]Jericho Forum, 2009. Cloud Cube Model: Selecting Cloud Formations for Secure Collaboration.

[43]Kandula, S., Padhye, J., Bahl, P., 2009. Flyways to De-congest Data Center Networks.

[44]Katayama, Y., Takano, K., Kohda, Y., et al., 2011. Wireless data center networking with steered-beam mm wave links. IEEE Wireless Communications and Networking Conf., p.2179-2184.

[45]Lee, Y.C., Zomaya, A.Y., 2012. Energy efficient utilization of resources in cloud computing systems. J. Supercomput., 60(2):268-280.

[46]Li, D., Guo, C., Wu, H., et al., 2009. Ficonn: using backup port for server interconnection in data centers. IEEE INFOCOM, p.2276-2285.

[47]Li, W., Svard, P., 2010. REST-based SOA application in the cloud: a text correction service case study. World Congress on Services, p.84-90.

[48]Lian, F.L., Moyne, J., Tilbury, D., 2002. Network design consideration for distributed control systems. IEEE Trans. Contr. Syst. Technol., 10(2):297-307.

[49]Manzano, M., Bilal, K., Calle, E., et al., 2013. On the connectivity of data center networks. IEEE Commun. Lett., 17(11):2172-2175.

[50]Niranjan Mysore, R., Pamboris, A., Farrington, N., et al., 2009. Portland: a scalable fault-tolerant layer 2 data center network fabric. ACM SIGCOMM Comput. Commun. Rev., 39(4):39-50.

[51]Popa, L., Ratnasamy, S., Iannaccone, G., et al., 2010. A cost comparison of datacenter network architectures. Proc. 6th Int. Conf. Co-NEXT, Article 16.

[52]Ranachandran, K., 2008. 60 GHz Data-Center Networking: Wireless=>Worryless. Technical Report, NEC Laboratories America, Inc.

[53]Redkar, T., Guidici, T., 2011. Windows Azure Platform. Apress.

[54]Rimal, B., Choi, E., Lumb, I., 2009. A taxonomy and survey of cloud computing systems. 5th Int. Joint Conf. on INC, IMS and IDC, p.44-51.

[55]Shin, J.Y., Sirer, E.G., Weatherspoon, H., et al., 2012. On the feasibility of completely wireless datacenters. Proc. 8th ACM/IEEE Symp. on Architectures for Networking and Communications Systems, p.3-14.

[56]Singh, A., Korupolu, M., Mohapatra, D., 2008. Server-storage virtualization: integration and load balancing in data centers. Proc. ACM/IEEE Conf. on Supercomputing, p.53.

[57]Singla, A., Hong, C.Y., Popa, L., et al., 2012. Jellyfish: networking data centers randomly. Proc. 9th USENIX Conf. on Networked Systems Design and Implementation, p.17.

[58]Tarantino, A., 2012. Point-of-view paper: high tech’s innovative approach to sustainability. Int. J. Innov. Sci., 4(1):37-40.

[59]Tennenhouse, D., Wetherall, D., 2002. Towards an active network architecture. Proc. DARPA Active Networks Conf. and Exposition, p.2-15.

[60]Tschudi, W., Xu, T., Sartor, D., et al., 2004. Energy Efficient Data Centers. Lawrence Berkeley National Laboratory.

[61]Tziritas, N., Xu, C.Z., Loukopoulos, T., et al., 2013. Application-aware workload consolidation to minimize both energy consumption and network load in cloud environments. 42nd IEEE Int. Conf. on Parallel Processing, p.449-457.

[62]USEPA, 2012. 2012 Annual Report—US Environmental Protection Agency.

[63]Vahdat, A., Al-Fares, M., Farrington, N., et al., 2010. Scale-out networking in the data center. IEEE Micro, 30(4):29-41.

[64]Valiant, L.G., 1990. A bridging model for parallel computation. Commun. ACM, 33(8):103-111.

[65]Wang, G., Andersen, D.G., Kaminsky, M., et al., 2010. C-through: part-time optics in data centers. ACM SIGCOMM Comput. Commun. Rev., 40(4):327-338.

[66]Wu, H., Lu, G., Li, D., et al., 2009. MDCube: a high performance network structure for modular data center interconnection. Proc. 5th Int. Conf. on Emerging Networking Experiments and Technologies, p.25-36.

[67]Wu, K., Xiao, J., Ni, L.M., 2012. Rethinking the architecture design of data center networks. Front. Comput. Sci., 6(5):596-603.

[68]Zahariev, A., 2009. Google APP Engine. Helsinki University of Technology, Helsinki, Finland.