Energy Cost-Effectiveness of Cloud Service Datacenters

  • Cheng-Jen Tang
  • Miau-Ru Dai
Part of the Communications in Computer and Information Science book series (CCIS, volume 223)


Cloud computing is a computation intensive service that clusters distributed computers providing applications as services and on-demand resources over Internet. Theoretically, such consolidated resource enhances the energy efficiency of both clients and servers. In reality, cloud computing is a panacea for enhancing energy efficiency under some certain conditions. For a user of cloud services, the computing resources are located at remote machines. Pioneers in exploring cloud computing, such as Google, AmazonWeb, Microsoft Azure, Yahoo, and IBM all use web pages as service interface via HTTP protocol. Through appropriated designs, sorting, one of the most frequently used algorithms, required by a web page can be executed and succeed by either clients or servers. As the model proposed in this paper, such client-server balanced computing allocation suggests a more energy-efficient and cost-effective web service.


Energy Efficiency Cloud Computing Datacenter 


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  1. 1.
    Forrester research - marketing and strategy data (2008),
  2. 2.
    Internet world stats - world internet users and population stats (2010),
  3. 3.
    Nuclear energy institute - u.s. nuclear power plants (2011),
  4. 4.
    Ayala, J.L., Veidenbaum, A., Lpez-Vallejo, M.: Power-aware compilation for register file energy reduction. International Journal of Parallel Programming 31, 451–467 (2003),, doi:10.1023/B:IJPP.0000004510.66751.2eCrossRefGoogle Scholar
  5. 5.
    Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. IEEE Computer 40(12), 33–37 (2007), CrossRefGoogle Scholar
  6. 6.
    Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M., Pentikousis, K.: Energy-efficient cloud computing. The Computer Journal 53(7), 1045 (2010)CrossRefGoogle Scholar
  7. 7.
    Bianchini, R., Rajamony, R.: Power and energy management for server systems. Computer 37(11), 68–76 (2004)CrossRefGoogle Scholar
  8. 8.
    Bunse, C., Höpfner, H., Roychoudhury, S., Mansour, E.: Choosing the best sorting algorithm for optimal energy consumption? In: Proceedings of the International Conference on Software and Data Technologies (ICSOFT), pp. 199–206 (2009)Google Scholar
  9. 9.
    Elnozahy, E., Kistler, M., Rajamony, R.: Energy-efficient server clusters. Power-Aware Computer Systems, 179–197 (2003)Google Scholar
  10. 10.
    Hamilton, J.: Cooperative expendable micro-slice servers (CEMS): low cost, low power servers for internet-scale services. In: Conference on Innovative Data Systems Research (CIDR 2009), Citeseer (January 2009)Google Scholar
  11. 11.
    Knuth, D.E.: The Art of Computer Programming, Sorting and Searching, 2nd edn., vol. 3. Addison-Wesley, Reading (1998)zbMATHGoogle Scholar
  12. 12.
    Rusu, C., Ferreira, A., Scordino, C., Watson, A.: Energy-efficient real-time heterogeneous server clusters. In: Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium, pp. 418–428. IEEE, Los Alamitos (2006)Google Scholar
  13. 13.
    Schmidt, D., Wehn, N.: Dram power management and energy consumption: a critical assessment. In: Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes, SBCCI 2009, pp. 32:1–32:5. ACM, New York (2009), Google Scholar
  14. 14.
    Siegmund, N., Rosenmüller, M., Apel, S.: Automating energy optimization with features. In: Proceedings of the 2nd International Workshop on Feature-Oriented Software Development, FOSD 2010, pp. 2–9. ACM, New York (2010), Google Scholar
  15. 15.
    Skiena, S.S.: The Algorithm Design Manual, 2nd edn. Springer, Heidelberg (2008)CrossRefzbMATHGoogle Scholar
  16. 16.
    Zedlewski, J., Sobti, S., Garg, N., Zheng, F., Krishnamurthy, A., Wang, R.: Modeling hard-disk power consumption. In: Proceedings of the 2nd USENIX Conference on File and Storage Technologies, pp. 217–230. USENIX Association (2003)Google Scholar
  17. 17.
    Zhong, S., Shen, Y., Hao, F.: Tuning compiler optimization options via simulated annealing. In: Second International Conference on Future Information Technology and Management Engineering, FITME 2009, pp. 305–308 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cheng-Jen Tang
    • 1
  • Miau-Ru Dai
    • 1
  1. 1.Graduate Institute of Communication EngineeringTatung UniversityTaipeiTaiwan

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