Advertisement

Server Utilization-Based Smart Temperature Monitoring System for Cloud Data Center

  • Sudipta SahanaEmail author
  • Rajesh Bose
  • Debabrata Sarddar
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 11)

Abstract

The rise in demand for cloud computing services has thrown sharply into focus the subject of energy efficiency and cooling methods. The words “green” and “computing” can often translate into commercial and production successes, vendors and consumers alike are keen to optimize the services offered through cloud data centers as much as possible. While various existing methods help in bringing down rising temperatures of servers operating in cloud data center infrastructure, most authors would agree that pushing in cold air requires energy to be fed to cooling equipment and the associated infrastructure. Based upon existing research conducted, we approached the problem in a new light—concentrating on server utilization to regulate the temperature. We introduce Mean Utilization Factor concept that allows detecting and regulating the amount of cool air that is to be channeled in and around the servers within a cloud data center to bring down the operating temperature.

Keywords

Temperature monitoring Power consumption Server utilization Mean utilization factor Cloud data center 

References

  1. 1.
    Self, S.J., Reddy, B.V., Rosen, M.A.: Review of underground coal gasification technologies and carbon capture. Int. J. Energy Environ. Eng. 3, 16 (2012). doi: 10.1186/2251-6832-3-16
  2. 2.
    Basmadjian, R., De Meer, H.,  Lent, R., Giuliani, G.: Cloud computing and its interest in saving energy: the use case of a private cloud. J. Cloud Comput. Adv. Syst. Appl. 1, 5 (2012). doi: 10.1186/2192-113X-1-5
  3. 3.
    Hamilton, J.: Cooperative Expendable Micro-Slice Servers (CEMS): low cost, low power servers for internet-scale services. In: 4th biennial conference on innovative data systems research (CIDR), Asilomar, California, USA, 4–7 Jan 2009Google Scholar
  4. 4.
  5. 5.
    Wang, T., Qin, B., Su, Z., Xia, Y., Hamdi, M., Foufou, S., Hamila, R.: Towards bandwidth guaranteed energy efficient data center networking. J. Cloud Comput. 4, 9 (2015). doi: 10.1186/s13677-015-0035-7 CrossRefGoogle Scholar
  6. 6.
    Wang, X., Wang, X., Xing, G., Chen, J., Lin, C.-X., Chen, Y.: Towards optimal sensor placement for hot server detection in data centers. In: ICDCS ‘11 Proceedings of the 2011 31st International Conference on Distributed Computing Systems, pp. 899–908. ISBN: 978-0-7695-4364-2. doi: 10.1109/ICDCS.2011.20
  7. 7.
    Itou, A., Nakanishi, T., Mizuguchi, T., Yoshida, M., Saburi, T.: High performance parallel computing for Computational Fluid Dynamics (CFD). In: Komatsu Technical Report, vol. 51 No. 156 (2005)Google Scholar
  8. 8.
    Chan, H., Connell, J., Isci, C., Kephart, J.O., Lenchner, J., Mansle, C., McIntosh, S.: A robot as mobile sensor and agent in data center energy management. In: Proceedings of the 8th International Conference on Autonomic Computing, ICAC 2011, Karlsruhe, Germany, 14–18 June 2011. doi: 10.1145/1998582.1998610
  9. 9.
    Choi, J., Kim, Y., Sivasubramaniam, A., Srebric, J., Wang, Q., Lee, J.: Modeling and managing thermal profiles of rack-mounted servers with thermostat. IEEE Int. Symp. High-Perform. Comput. Archit. HPCA 205–215 (2007). doi: 10.1109/HPCA.2007.346198
  10. 10.
    Brandt, J., Gentile, A., Mayo, J., Pebay, P., Roe, D., Thompson, D., Wong, M.: Resource monitoring and management with OVIS to enable HPC in cloud computing environments. In: IPDPS, 2009, International, Parallel and Distributed Processing Symposium, pp. 1–8 (2009). doi: 10.1109/IPDPS.2009.5161234
  11. 11.
    Katsaros, G., Subirats, J., Oriol Fitó, J., Guitart, J., Gilet, P., Espling, D.: A service framework for energy-aware monitoring and VM management in clouds. Future Gener. Comput. Syst. 29(8), 2077–2091 (2013)Google Scholar
  12. 12.
    Bose, R., Sahana, S., Sarddar, D.: An adaptive cloud service observation using billboard manager cloud monitoring tool. Int. J. Softw. Eng. Appl. 9(7), 159–170 (2015). ISSN: 1738-9984. doi: 10.14257/ijseia.2015.9.7.17
  13. 13.
    Bose, R., Sahana, S., Sarddar, D.: An energy efficient dynamic schedule based server load balancing approach for cloud data center. Int. J. Future Gener. Commun. Netw. 8(3), 123–136 (2015). ISSN: 2233-7857. doi: 10.14257/ijfgcn.2015.8.3.12
  14. 14.
    Chaudhry, M.T., Ling, T.C., Manzoor, A., Hussain, S.A., Kim, J.: Smart temperature monitoring for data center energy efficiency. ACM Comput. Surv. (CSUR) 47(3), Article No. 39 (2015). doi: 10.1145/2678278

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Sudipta Sahana
    • 1
    Email author
  • Rajesh Bose
    • 2
  • Debabrata Sarddar
    • 2
  1. 1.Department of Computer Science and EngineeringJIS College of EngineeringKalyani, NadiaIndia
  2. 2.Department of Computer Science and EngineeringUniversity of KalyaniKalyani, NadiaIndia

Personalised recommendations