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Journal of Computer Science and Technology

, Volume 33, Issue 2, pp 400–416 | Cite as

BCDC: A High-Performance, Server-Centric Data Center Network

  • Xi Wang
  • Jian-Xi Fan
  • Cheng-Kuan Lin
  • Jing-Ya Zhou
  • Zhao Liu
Regular Paper
First Online:
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Abstract

The capability of the data center network largely decides the performance of cloud computing. However, the number of servers in the data center network becomes increasingly huge, because of the continuous growth of the application requirements. The performance improvement of cloud computing faces great challenges of how to connect a large number of servers in building a data center network with promising performance. Traditional tree-based data center networks have issues of bandwidth bottleneck, failure of single switch, etc. Recently proposed data center networks such as DCell, FiConn, and BCube, have larger bandwidth and better fault-tolerance with respect to traditional tree-based data center networks. Nonetheless, for DCell and FiConn, the fault-tolerant length of path between servers increases in case of failure of switches; BCube requires higher performance in switches when its scale is enlarged. Based on the above considerations, we propose a new server-centric data center network, called BCDC, based on crossed cube with excellent performance. Then, we study the connectivity of BCDC networks. Furthermore, we propose communication algorithms and fault-tolerant routing algorithm of BCDC networks. Moreover, we analyze the performance and time complexities of the proposed algorithms in BCDC networks. Our research will provide the basis for design and implementation of a new family of data center networks.

Keywords

data center network interconnection network crossed cube server-centric fault-tolerant 
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Notes

Acknowledgment

We thank the anonymous reviewers and editors for their valuable suggestions that help to improve the presentation of the paper.

Supplementary material

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References

  1. 1.
    Harris D. Ballmer’s millionserver claim doesn’t seem so crazy. https://gigaom.com/2013/07/17/ballmers-million-server-claim-doesnt-seem-so-crazy/#comments, July 2013.
  2. 2.
    Dignan L. AWS financials on deck: The road to 3 million servers in operation. http://www.zdnet.com/article/aws-financials-on-deck-the-road-to-3-million-servers-in-operation/, April 2015.
  3. 3.
    Al-Fares M, Loukissas A, Vahdat A. A scalable, commodity data center network architecture. In Proc. the ACM SIGCOMM Conf. Data Communication, August 2008, pp.63-74.Google Scholar
  4. 4.
    Guo C X, Wu H T, Tan K, Shi L, Zhang Y G, Lu S W. DCell: A scalable and fault-tolerant network structure for data centers. In Proc. the ACM SIGCOMM Conf. Data Communication, August 2008, pp.75-86.Google Scholar
  5. 5.
    Li D, Guo C X, Wu H T, Tan K, Zhang Y G, Lu S W. Fi-Conn: Using backup port for server interconnection in data centers. In Proc. IEEE INFOCOM, April 2009, pp.2276-2285.Google Scholar
  6. 6.
    Guo C X, Lu G H, Li D, Wu H T, Zhang X, Shi Y F, Tian C, Zhang Y G, Lu S W. BCube: A high performance, server-centric network architecture for modular data centers. In Proc. the ACM SIGCOMM Conf. Data Communication, August 2009, pp.63-74.Google Scholar
  7. 7.
    Greenberg A, Hamilton J R, Jain N, Kandula S, Kim C, Lahiri P, Maltz D A, Patel P, Sengupta S. VL2: A scalable and flexible data center network. In Proc. the ACM SIGCOMM Conf. Data Communication, August 2009, pp.51-62.Google Scholar
  8. 8.
    Abu-Libdeh H, Costa P, Rowstron A, O’Shea G, Donnelly A. Symbiotic routing in future data centers. In Proc. ACM SIGCOMM, Aug.30-Sept.3, 2010, pp.51-62.Google Scholar
  9. 9.
    Yu Y, Qian C. Space shuffle: A scalable, flexible, and high-performance data center network. IEEE Trans. Parallel and Distributed Systems, 2016, 27(11): 3351-3365.CrossRefGoogle Scholar
  10. 10.
    Zheng K, Wang L, Yang B H, Sun Y, Uhlig S. LazyCtrl: A scalable hybrid network control plane design for cloud data centers. IEEE Trans. Parallel and Distributed Systems, 2017, 28(1): 115-127.CrossRefGoogle Scholar
  11. 11.
    Bhuyan L N, Agrawal D P. Generalized hypercube and hyperbus structures for a computer network. IEEE Trans. Computers, 1984, C-33(4): 323-333.CrossRefzbMATHGoogle Scholar
  12. 12.
    Leiserson C E. Fat-trees: Universal networks for hardware-efficient supercomputing. IEEE Trans. Computers, 1985, 34(10): 892-901.CrossRefGoogle Scholar
  13. 13.
    Dally W J. Performance analysis of k-ary n-cube interconnection networks. IEEE Trans. Computers, 1990, 39(6): 775-785.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Xiang D, Zhang Y L, Pan Y. Practical deadlock-free fault-tolerant routing in meshes based on the planar network fault model. IEEE Trans. Computers, 2009, 58(5): 620-633.MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Xiang D. Deadlock-free adaptive routing in meshes with fault-tolerance ability based on channel overlapping. IEEE Trans. Dependable and Secure Computing, 2011, 8(1): 74-88.CrossRefGoogle Scholar
  16. 16.
    Lin D, Liu Y, Hamdi M, Muppala J. FlatNet: Towards a flatter data center network. In Proc. IEEE Global Communications Conf., December 2012, pp.2499-2504.Google Scholar
  17. 17.
    Wang T, Su Z Y, Xia Y, Qin B, Hamdi M. NovaCube: A low latency Torus-based network architecture for data centers. In Proc. IEEE Global Communications Conf., December 2014, pp.2252-2257.Google Scholar
  18. 18.
    Wang T, Su Z Y, Xia Y, Liu Y, Muppala J, Hamdi M. SprintNet: A high performance servercentric network architecture for data centers. In Proc. IEEE Int. Conf. Communications, June 2014, pp.4005-4010.Google Scholar
  19. 19.
    Wang T, Su Z Y, Xia Y, Muppala J, Hamdi M. Designing efficient high performance server-centric data center network architecture. Computer Networks, 2015, 79: 283-296.CrossRefGoogle Scholar
  20. 20.
    Wang T, Su Z Y, Xia Y, Hamdi M. CLOT: A cost-effective low-latency overlaid Torus-based network architecture for data centers. In Proc. IEEE Int. Conf. Communications, June 2015, pp.5479-5484.Google Scholar
  21. 21.
    Li D W, Wu J, Liu Z Y, Zhang F. Towards the tradeoffs in designing data center network architectures. IEEE Trans. Parallel and Distributed Systems, 2017, 28(1): 260-273.Google Scholar
  22. 22.
    Efe K. A variation on the hypercube with lower diameter. IEEE Trans. Computers, 1991, 40(11): 1312-1316.CrossRefGoogle Scholar
  23. 23.
    Cull P, Larson S M. The Möbius cubes. IEEE Trans. Computers, 1995, 44(5): 647-659.Google Scholar
  24. 24.
    Abraham S, Padmanabhan K. The twisted cube topology for multiprocessors: A study in network asymmetry. Journal of Parallel and Distributed Computing, 1991, 13(1): 104-110.CrossRefGoogle Scholar
  25. 25.
    Fan J X, He L Q. BC interconnection networks and their properties. Chinese Journal of Computers, 2003, 26(1): 84-90. (in Chinese)MathSciNetGoogle Scholar
  26. 26.
    Wang D J. Hamiltonian embedding in crossed cubes with failed links. IEEE Trans. Parallel and Distributed Systems, 2012, 23(11): 2117-2124.CrossRefGoogle Scholar
  27. 27.
    Kulasinghe P, Bettayeb S. Embedding binary trees into crossed cubes. IEEE Trans. Computers, 1995, 44(7): 923-929.CrossRefzbMATHGoogle Scholar
  28. 28.
    Fan J, Lin X, Jia X. Optimal path embedding in crossed cubes. IEEE Trans. Parallel and Distributed Systems, 2005, 16(12): 1190-1200.CrossRefGoogle Scholar
  29. 29.
    Efe K. The crossed cube architecture for parallel computation. IEEE Trans. Parallel and Distributed Systems, 1992, 3(5): 513-524.CrossRefGoogle Scholar
  30. 30.
    Chang C P, Sung T Y, Hsu L H. Edge congestion and topological properties of crossed cubes. IEEE Trans. Parallel and Distributed Systems, 2000, 11(1): 64-80.CrossRefGoogle Scholar
  31. 31.
    Efe K, Blackwell P K, Slough W, Shiau T. Topological properties of the crossed cube architecture. Parallel Computing, 1994, 20(12): 1763-1775.Google Scholar
  32. 32.
    Kulasinghe P D. Connectivity of the crossed cube. Information Processing Letters, 1997, 61(4): 221-226.MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Fan J X, Jia X H. Edge-pancyclicity and path-embeddability of bijective connection graphs. Information Sciences, 2008, 178(2): 340-351.MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Yang X F, Dong Q, Tang Y Y. Embedding meshes/tori in faulty crossed cubes. Information Processing Letters, 2010, 110(14/15): 559-564.MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Zhou S M. The conditional diagnosability of crossed cubes under the comparison model. International Journal of Computer Mathematics, 2010, 87(15): 3387-3396.MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Dong Q, Zhou J L, Fu Y, Yang X F. Embedding a mesh of trees in the crossed cube. Information Processing Letters, 2012, 112(14/15): 599-603.MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Cheng B L, Fan J X, Jia X H, Zhang S K. Independent spanning trees in crossed cubes. Information Sciences, 2013, 233: 276-289.MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Cheng B L, Fan J X, Jia X H, Wang J. Dimension-adjacent trees and parallel construction of independent spanning trees on crossed cubes. Journal of Parallel and Distributed Computing, 2013, 73(5): 641-652.CrossRefzbMATHGoogle Scholar
  39. 39.
    Chen H C, Kung T L, Hsu L Y. 2-disjoint-path-coverable panconnectedness of crossed cubes. The Journal of Supercomputing, 2015, 71(7): 2767-2782.CrossRefGoogle Scholar
  40. 40.
    Chen H C, Zou Y H, Wang Y L, Pai K J. A note on path embedding in crossed cubes with faulty vertices. Information Processing Letters, 2017, 121: 34-38.MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Cheng B L, Wang D J, Fan J X. Constructing completely independent spanning trees in crossed cubes. Discrete Applied Mathematics, 2017, 219: 100-109.MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Diestel R. Graph Theory (4th edition). Springer, 2010.Google Scholar
  43. 43.
    Ghemawat S, Gobioff H, Leung S T. The Google file system. In Proc. the 19th ACM Symp. Operating Systems Principles, October 2003, pp.29-43.Google Scholar
  44. 44.
    Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 2008, 51(1): 107-113.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xi Wang
    • 1
    • 2
  • Jian-Xi Fan
    • 1
  • Cheng-Kuan Lin
    • 1
  • Jing-Ya Zhou
    • 1
  • Zhao Liu
    • 1
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.School of Software and Services OutsourcingSuzhou Institute of Industrial TechnologySuzhouChina

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