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Sustainable and Resilient Network Infrastructure Design for Cloud Data Centers

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Part of the Service Science: Research and Innovations in the Service Economy book series (SSRI)

Abstract

In this chapter we firstly review the state of the art of the data center networks (DCNs) topological structures and explain the challenges in this field, which motivates our work. Then we propose a method for evaluating the topological metrics related to network robustness and node centrality so as to identify the most critical nodes and links in DCNs, as well as measuring the overall DCN performance such as throughput, latency, packet drop ratio according to the various faults occurred in the network. Moreover, we have identified the energy consumption behaviours according to the change of DCN’s internal structure. Our simulation studies showed that the DCN topology and traffic load have significant impact on its overall energy consumption and also on other network-related performance aspects.

Keywords

Data centre networking QoS Topological connectivity Energy efficiency Service resiliency Sustainable network structure 

References

  1. Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. ACM Symposium conducted at the meeting of the ACM SIGCOMM Computer Communication Review (2008)Google Scholar
  2. Banerjee, A., Mukherjee, T., Varsamopoulos, G., Gupta, S.K.: Integrating cooling awareness with thermal aware workload placement for HPC data centers. Sustain. Comput. Inform. Sys. 1(2), 134–150 (2011)Google Scholar
  3. Beitelmal, A.H., Patel, C.D.: Thermo-fluids provisioning of a high performance high density data center. Distrib. Parallel Databases. 21(2–3), 227–238 (2007)CrossRefGoogle Scholar
  4. Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M.Q., Pentikousis, K.: Energy-efficient cloud computing. Comput. J. 53(7), 1045–1051 (2010)CrossRefGoogle Scholar
  5. Brocanelli, M., Zheng, W., Wang, X.: Reducing the expenses of geo-distributed data centers with portable containerized modules. Perform. Eval. 79, 104–119 (2014)CrossRefGoogle Scholar
  6. Choo, K.-K.R.: Mobile cloud storage users. IEEE Cloud Comput. 1(3), 20–23 (2014)CrossRefGoogle Scholar
  7. Deo, N.: Graph Theory with Applications to Engineering and Computer Science. Courier Dover Publications, New York (2016)zbMATHGoogle Scholar
  8. Elnozahy, E.M., Kistler, M., Rajamony, R.: Energy-efficient server clusters. Springer. Symposium conducted at the meeting of the International Workshop on Power-Aware Computer Systems (2002)Google Scholar
  9. Ge, C., Sun, Z., Wang, N.: A survey of power-saving techniques on data centers and content delivery networks. IEEE Commun. Surv. Tutorials. 15(3), 1334–1354 (2013)CrossRefGoogle Scholar
  10. Gorti, N.P.K.: Application aware performance, power consumption, and reliability tradeoff. Diss. Iowa State University, (2014)Google Scholar
  11. Google White Paper: Google’s green data centers: network POP case study. Google. https://static.googleusercontent.com/media/www.google.com/en//corporate/datacenter/dc-best-practicesgoogle. pdf, (2011)
  12. Guo, C., Lu, G., Li, D., Wu, H., Zhang, X., Shi, Y., Tian, C., Zhang, Y., Songwu, L.: BCube: a high performance, server-centric network architecture for modular data centers. ACM SIGCOMM Comput. Commun. Rev. 39(4), 63–74 (2009)CrossRefGoogle Scholar
  13. Heymann, S.: Gephi. In: Encyclopedia of Social Network Analysis and Mining, pp. 612–625. Springer, New York (2014)Google Scholar
  14. Horvath, T., Abdelzaher, T., Skadron, K., Liu, X.: Dynamic voltage scaling in multitier web servers with end-to-end delay control. IEEE Trans. Comput. 56(4), 444–458 (2007)CrossRefMathSciNetGoogle Scholar
  15. Howard, A.J., Holmes, J.: Addressing data center efficiency: lessons learned from process evaluations of utility energy efficiency programs. Energy Efficiency. 5(1), 137–148 (2012)CrossRefGoogle Scholar
  16. Kliazovich, D., Bouvry, P., Khan, S.U.: GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. J. Supercomput. 62(3), 1263–1283 (2012)CrossRefGoogle Scholar
  17. Koomey, J.G., Belady, C., Patterson, M., Santos, A., Lange, K.-D.: Assessing trends over time in performance, costs, and energy use for servers. Lawrence Berkeley National Laboratory, Stanford University, Microsoft Corporation, and Intel Corporation, Tech. Rep (2009)Google Scholar
  18. Malik, A.W., Bilal, K., Aziz, K., Kliazovich, D., Ghani, N., Khan, S. U., Buyya, R.: Cloudnetsim++: A toolkit for data center simulations in omnet++IEEE. Symposium conducted at the meeting of the 2014 11th Annual High Capacity Optical Networks and Emerging/Enabling Technologies (Photonics for energy) (2014)Google Scholar
  19. Mell, P., Grance, T.: The NIST definition of cloud computing. Commun. ACM. 53(6), 50 (2010)Google Scholar
  20. Networking, C.V.: Cisco global cloud index: Forecast and methodology, 2012–2017 (White paper): Cisco (2013)Google Scholar
  21. Sadashiv, N., Kumar, S.D.: Cluster, grid and cloud computing: A detailed comparison. Symposium conducted at the meeting of the Computer Science & Education (ICCSE), 2011 6th International Conference on IEEE (2011)Google Scholar
  22. Shang, L., Peh, L.-S., Jha, N.K.: Power-efficient interconnection networks: dynamic voltage scaling with links. IEEE Comput. Archit. Lett. 1(1), 6–6 (2002)CrossRefGoogle Scholar
  23. Steere, D.C., Goel, A., Gruenberg, J., McNamee, D., Pu, C., Walpole, J.: A feedback-driven proportion allocator for real-rate scheduling Symposium conducted at the meeting of the OSDI (1999)Google Scholar
  24. Viswanath, R., Wakharkar, V., Watwe, A., Lebonheur, V.: Thermal performance challenges from silicon to systems. Intel. Technol. J. (2000)Google Scholar
  25. Wang, L., Chen, D., Zhao, J., Tao, J.: Resource management of distributed virtual machines. Int. J. Ad Hoc Ubiquitous Comput. 10(2), 96–111 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Information Technology and Software EngineeringSchool of Engineering, Computer and Mathematical Sciences, Auckland University of TechnologyAucklandNew Zealand

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