CFP last date
22 April 2024
Reseach Article

Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing

by Deep Mann, Inderveer Chana
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 10
Year of Publication: 2012
Authors: Deep Mann, Inderveer Chana
10.5120/7806-0939

Deep Mann, Inderveer Chana . Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing. International Journal of Computer Applications. 50, 10 ( July 2012), 13-17. DOI=10.5120/7806-0939

@article{ 10.5120/7806-0939,
author = { Deep Mann, Inderveer Chana },
title = { Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 10 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number10/7806-0939/ },
doi = { 10.5120/7806-0939 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:47:55.202078+05:30
%A Deep Mann
%A Inderveer Chana
%T Heterogeneous Workload Consolidation for Efficient Management of Data Centers in Cloud Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 10
%P 13-17
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud computing is a recent innovation, which provides various services on a usage based payment model. The rapid expansion in data centers has triggered the dramatic increase in energy used, operational cost and its effect on the environment in terms of carbon footprints. To reduce power consumption, it is necessary to consolidate the hosting workloads. In this paper, we present a Single Threshold technique for efficient consolidation of heterogeneous workloads. Our technique focuses on the energy consumption of the data center due to the heterogeneity of the workloads and also gives information about the SLA violations. The experimental results demonstrate that our technique is efficient for the data centers to consolidate the heterogeneous workloads.

References
  1. Goiri, I. , Fito, J. , Julia, F. , Nou, R. , Berral, J. L. , and Guitart, J. 2010. Multifaceted resource management for dealing with heterogeneous workloads in virtualized data centers. In 11th ACM/IEEE international conference on grid computing (Grid 2010). pp. 25–32. Brussels. Belgium.
  2. Buyya, R. , Yeo, C. , and Venugopal, S. 2008. Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities. In Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications (HPCC-OB, IEEE CS Press. Los Alamitos, CA. USA).
  3. Foster, I. , Zhao, Y. , Raicu, I. , and Lu, S. 2008. Cloud Computing and Grid Computing 360-Degree Compared in Grid Computing Environments Workshop. 200B. GCE'OB. pp. 1-10.
  4. Koomey, J. 2007. Estimating total power consumption by servers in the US and the world. Final report. vol. 15.
  5. Berl, A. , Gelenbe, E. , Di Girolamo M. , Giuliani, G. , Meer, H. D. , Dang, M. Q. , and Pentikousis, K. 2009. Energy-Efficient Cloud Computing. The Computer Journal. vol. 53. No. 7. pp. 1045-1051.
  6. Lefevre, L. , and Orgerie, A. C. 2010. Designing and evaluating an energy efficient Cloud. The Journal of Supercomputing. Springer. vol. 51. No. 3. pp. 352-373.
  7. Lee, Y. C. , and Zomaya, A. Y. 2010. Energy efficient utilization of resources in cloud computing systems. Journal of Supercomputing. pp. 1-13.
  8. Zhan, J. , Wang, L. , Tu, B. , Li, Y. , Wang, P. , Zhou W. , and Meng, D. 2008. Phoenix Cloud: Consolidating Different Computing Loads on Shared Cluster System for Large Organization. Proceeding Workshop Cloud Computing and Its Application (CCA '08).
  9. Tian, C. , Zhou, H. , He, Y. , and Zha, L. 2009. A dynamic MapReduce scheduler for heterogeneous workloads. In Proceedings of the 2009 Eighth International Conference on Grid and Cooperative Computing. pp. 218-224. IEEE Computer Society.
  10. Nou, R. , Julia?, F. , Guitart, J. , and Torres. J. 2007. Dynamic resource provisioning for self-adaptive heterogeneous workloads in smp hosting platforms. International Conference on E-business (2nd) ICE-B. Barcelona. Spain.
  11. Steinder, M. , Whalley, I. , Carrera, D. , Gaweda, I. , and Chess, D. M. 2007. Server virtualization in autonomic management of heterogeneous workloads. In Integrated Network Management. pp. 139-148.
  12. Carrera, D. , Steinder, M. , Whalley, I. , Torres J. , and Ayguade, E. 2008. Managing SLAs of heterogeneous workloads using dynamic application placement. HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing. New York. USA.
  13. Calheiros, R. N. , Ranjan, R. , Beloglazov, A. , Rose, C. A. F. D. , and Buyya, R. 2010. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and Experience. Wiley Press. New York. USA.
  14. Beloglazov, A. , Abawajy J. , and Buyya, R. 2011. Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing. Future Generation Computer Systems. ISSN: 0167-739X. Elsevier Science. Amsterdam. The Netherlands. (In press, accepted on April 28).
Index Terms

Computer Science
Information Sciences

Keywords

Cloud Computing Energy Efficiency and Heterogeneous Workload Consolidation