Evaluation of Effect of Network Energy Consumption in Load Distribution across Data Centers

  • Harumasa Tada
  • Makoto Imase
  • Masayuki Murata
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 66)


Recently, the consumption of a considerable amount of energy by data centers has become a serious problem, and there are many researches aiming at the reduction of this energy consumption. However, previous researches intend to reduce only the energy consumed inside data centers. To the best of our knowledge, there are few researches on load distributuion that focus on the network energy consumption arising from the communication across data centers. In this study, we consider the energy consumption of the network as well as that of the data centers in the request distribution across geographically distributed data centers. By using various conditions, we calculate the overall energy consumption of two request distribution policies—one respects the network energy consumption, and the other does not. By comparing these two policies, we examine the condition under which the network energy consumption is worth considering.


Cloud Computing Electricity Cost Optimization Simulated Annealing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pinheiro, E., Bianchini, R., Carrera, E., Heath, T.: Load balancing and unbalancing for power and performance in cluster-based systems. In: Workshop on Compilers and Operating Systems for Low Power, vol. 180, pp. 182–195 (2001)Google Scholar
  2. 2.
    Elnozahy, E., Kistler, M., Rajamony, R.: Energy-Efficient Server Clusters. In: Falsafi, B., VijayKumar, T.N. (eds.) PACS 2002. LNCS, vol. 2325, pp. 179–196. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Sharma, V., Thomas, A., Abdelzaher, T., Skadron, K., Lu, Z.: Power-aware QoS management in web servers. In: Proceedings of the 24th IEEE International Real-Time Systems Symposium, p. 63 (2003)Google Scholar
  4. 4.
    Rusu, C., Xu, R., Melhem, R., Mosse, D.: Energy-efficient policies for request-driven soft real-time systems. In: Euromicro Conference on Real-Time Systems, ECRTS 2004 (2004)Google Scholar
  5. 5.
    Chen, Y., Das, A., Qin, W., Sivasubramaniam, A., Wang, Q., Gautam, N.: Managing server energy and operational costs in hosting centers. ACM SIGMETRICS Performance Evaluation Review 33(1), 303–314 (2005)CrossRefGoogle Scholar
  6. 6.
    Heath, T., Diniz, B., Carrera, E., et al.: Energy conservation in heterogeneous server clusters. In: Proceedings of the Tenth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, p. 195. ACM (2005)Google Scholar
  7. 7.
    Rusu, C., Ferreira, A., Scordino, C., Watson, A., Melhem, R., Mossé, D.: Energy-efficient real-time heterogeneous server clusters. In: Proceedings of RTAS, pp. 418–428 (2006)Google Scholar
  8. 8.
    Chen, G., He, W., Liu, J., Nath, S., Rigas, L., Xiao, L., Zhao, F.: Energy-aware server provisioning and load dispatching for connection-intensive internet services. In: Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, pp. 337–350. USENIX Association (2008)Google Scholar
  9. 9.
    Gurumurthi, S., Sivasubramaniam, A., Kandemir, M., Franke, H.: DRPM: dynamic speed control for power management in server class disks. In: Proceedings of the 30th Annual International Symposium on Computer Architecture, p. 181. ACM (2003)Google Scholar
  10. 10.
    Zhu, Q., David, F., Devaraj, C., Li, Z., Zhou, Y., Cao, P.: Reducing energy consumption of disk storage using power-aware cache management (2004)Google Scholar
  11. 11.
    Ganesh, L., Weatherspoon, H., Balakrishnan, M., Birman, K.: Optimizing Power Consumption in Large Scale Storage Systems. In: Proceedings of the 11th USENIX Workshop on Hot Topics in Operating Systems, HotOS 2007 (2007)Google Scholar
  12. 12.
    Li, X., Li, Z., Zhou, Y., Adve, S.: Performance directed energy management for main memory and disks. ACM Transactions on Storage (TOS) 1(3), 380 (2005)Google Scholar
  13. 13.
    Moore, J., Chase, J., Ranganathan, P., Sharma, R.: Making scheduling gcool h: Temperature-aware workload placement in data centers. In: Proceedings of the USENIX Annual Technical Conference, pp. 61–75 (2005)Google Scholar
  14. 14.
    Bash, C., Forman, G.: Cool job allocation: Measuring the power savings of placing jobs at cooling-efficient locations in the data center. In: 2007 USENIX Annual Technical Conference on Proceedings of the USENIX Annual Technical Conference, pp. 1–6. USENIX Association (2007)Google Scholar
  15. 15.
    Tang, Q., Gupta, S., Varsamopoulos, G.: Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: a cyber-physical approach. IEEE Transactions on Parallel and Distributed Systems, 1458–1472 (2008)Google Scholar
  16. 16.
    Ghemawat, S., Gobioff, H., Leung, S.: The Google file system. ACM SIGOPS Operating Systems Review 37(5), 43 (2003)CrossRefGoogle Scholar
  17. 17.
    Chang, F., Dean, J., Ghemawat, S., Hsieh, W., Wallach, D., Burrows, M., Chandra, T., Fikes, A., Gruber, R.: Bigtable: A distributed storage system for structured data. In: Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2006 (2006)Google Scholar
  18. 18.
    Dean, J., Ghemawat, S.: Map Reduce: Simplified data processing on large clusters. Communications of the ACM-Association for Computing Machinery-CACM 51(1), 107–114 (2008)CrossRefGoogle Scholar
  19. 19.
    Data Center Knowledge, Google’s Chiller-less Data Center (2009),
  20. 20.
    Information Week, Google In Oregon: Mother Nature Meets The Data Center (2007),
  21. 21.
    Hamilton, J.: Architecture for modular data centers, Arxiv preprint cs/0612110 (2006)Google Scholar
  22. 22.
    Le, K., Bianchini, R., Martonosi, M., Nguyen, T.: Cost-and Energy-Aware Load Distribution Across Data Centers. In: Workshop on Power Aware Computing and Systems, HotPower 2009 (2009)Google Scholar
  23. 23.
    Barroso, L., Hölzle, U.: The datacenter as a computer: An introduction to the design of warehouse-scale machines. Synthesis Lectures on Computer Architecture 4(1), 1–108 (2009)CrossRefGoogle Scholar
  24. 24.
    Mahadevan, P., Sharma, P., Banerjee, S., Ranganathan, P.: A Power Benchmarking Framework for Network Devices. In: Fratta, L., Schulzrinne, H., Takahashi, Y., Spaniol, O. (eds.) NETWORKING 2009. LNCS, vol. 5550, pp. 795–808. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  25. 25.
    Standard Performance Evaluation Corporation, SPECpower_ssj (2008),
  26. 26.
    Fei, A., Pei, G., Liu, R., Zhang, L.: Measurements on delay and hop-count of the internet. In: IEEE GLOBECOM 1998-Internet Mini-Conference (1998)Google Scholar
  27. 27.
    Principled Technologies, WebBench performance on quad-core and dual-core dual-processor servers (2006),
  28. 28.
  29. 29.
    Chabarek, J., Sommers, J., Barford, P., Estan, C., Tsiang, D., Wright, S.: Power awareness in network design and routing. In: IEEE INFOCOM (2008)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Harumasa Tada
    • 1
  • Makoto Imase
    • 2
  • Masayuki Murata
    • 2
  1. 1.Faculty of EducationKyoto University of EducationJapan
  2. 2.Graduate School of Information Science and TechnologyOsaka UniversityJapan

Personalised recommendations