Quality of Service Trade-offs between Central Data Centers and Nano Data Centers

Farzaneh Akhbar, Tolga Ovatman

2015

Abstract

Nano data centers are one of the latest trends in cloud computing aiming towards distributing the computing power of massive data centers among the clients in order to overcome setup and maintenance costs. The distribution process is done over the already present computing elements in client houses such as tv receivers, wireless modems, etc. In this paper we investigate the feasibility of using nano data centers instead of conventional data centers containing accumulated computing power. We try to draw the lines that may affect the decision of nano data center approach considering important parameters in cloud computing such as memory capacity, diversity of user traffic and computing costs. We also investigate the thresholds for these parameters to find out the conditions that make more sense to set up nano data centers as the best replacement of Central Data Centers. We use a CloudSim based simulator, namely CloudAnalyst, for Data Center performance experiments in java. Our results show that 1 gigabyte memory capacity can be seen as a threshold for response time improvement of nano data centers. For nano data centers with more memory capacity there will not be any improvement in response times that leverages the performance cost. We also combine the results of response time and performance cost to provide a similar threshold.

References

  1. Adami, D., Martini, B., Gharbaoui, M., Castoldi, P., Antichi, G., Giordano, S., 2013. Effective resource control strategies using OpenFlow in cloud data center. IM, page 568-574. IEEE.
  2. Buyya, R., Ranjan, R. and Calheiros, R.N., 2009. Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities. Proceedings of the 7th High Performance Computing and Simulation Conference HPCS2009, IEEE Computer Society.
  3. Haibo Mi, Huaimin Wang, Gang Yin, Yangfan Zhou, Dianxi Shi, Lin Yuan, 2010. Online Selfreconfiguration with Performance Guarantee for Energy-efficient Large-scale Cloud Computing Data Centers. IEEE SCC, page 514-521.
  4. Kliazovich, D., Bouvry, P., Audzevich, Y., Khan, S.U., 2010. GreenCloud: A Packet- Level Simulator of Energy-Aware Cloud Computing Data Center . GLOBECOM, page 1-5. IEEE.
  5. Laoutaris, N., Rodriguez, P., Massoulie, L., 2008. ECHOS: Edge Capacity Hosting Overlays of Nano Data Centers. Computer Communication Review 38(1):51-54.
  6. Moreno, I.S., Jie Xu, 2011. Customer-Aware Resource Overallocation to Improve Energy Efficiency in RealTime Cloud Computing Data Centers. SOCA, page 1- 8. IEEE.
  7. Ning Liu, Ziqian Dong, Rojas-Cessa, R., 2013. Task Scheduling and Server Provisioning for EnergyEfficient Cloud-Computing Data Centers. ICDCS Workshops, page 226-231. IEEE.
  8. Ousterhout, J., Agrawal, P., Erickson, D., Kozyrakis, C., Leverich, J., Mazières, D., Mitra, S., Narayanan, A., Parulkar, G., Rosenblum, M., Rumble, S.M., Stratmann, E., Stutsman R., 2011. The Case For RAMClouds. Commun. ACM 54(7):121-130.
  9. Papagianni, C., Leivadeas, A., Papavassiliou, S., Maglaris, V., Cervello-Pastor, C., Monje, A., 2013. On the Optimal Allocation of Virtual Resources in Cloud Computing Networks. IEEE Trans. Computers 62(6):1060-1071.
  10. Pepelnjak, I., 2014. Data Center Design Case Studies. In Space Publication. First edidtion.
  11. Qi Zhang, Lu Cheng, Boutaba, R., 2010. Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications In Journal of Internet Services and Applications. Vol. 1, No. 1. pp. 7-18.
  12. Sravan Kumar, R., Saxena, A. R., 2011. Data Integrity Proofs in Cloud Storage. COMSNETS, page 1-4. IEEE.
  13. Valancius, V., Laoutaris, N., Massoulié, L., Diot, C., Rodriguez, P., 2009. Greening the Internet with Nano Data Centers . CoNEXT. page 37-48. ACM.
  14. Wickremasinghe, B., Calheiros, R.N., Buyya, R., 2010. CloudAnalyst: A CloudSim-based Visual Modeller for Analysing Cloud Computing Environments and Applications. AINA, page 446-452. IEEE Computer.
  15. Zeng, Z., Veeravalli, B., 2012. Do More Replicas of Object Data Improve the Performance of Cloud Data Centers. UCC, page 39-46. IEEE.
Download


Paper Citation


in Harvard Style

Akhbar F. and Ovatman T. (2015). Quality of Service Trade-offs between Central Data Centers and Nano Data Centers . In Proceedings of the 5th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-104-5, pages 113-118. DOI: 10.5220/0005439101130118


in Bibtex Style

@conference{closer15,
author={Farzaneh Akhbar and Tolga Ovatman},
title={Quality of Service Trade-offs between Central Data Centers and Nano Data Centers},
booktitle={Proceedings of the 5th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2015},
pages={113-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005439101130118},
isbn={978-989-758-104-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - Quality of Service Trade-offs between Central Data Centers and Nano Data Centers
SN - 978-989-758-104-5
AU - Akhbar F.
AU - Ovatman T.
PY - 2015
SP - 113
EP - 118
DO - 10.5220/0005439101130118