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Reseach Article

The Effect of Cloud Workload Consolidation on Cloud Energy Consumption and Performance in Multi-Tenant Cloud Infrastructure

by Kenga Mosoti Derdus, Vincent Oteke Omwenga, Patrick Job Ogao
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 37
Year of Publication: 2019
Authors: Kenga Mosoti Derdus, Vincent Oteke Omwenga, Patrick Job Ogao
10.5120/ijca2019918353

Kenga Mosoti Derdus, Vincent Oteke Omwenga, Patrick Job Ogao . The Effect of Cloud Workload Consolidation on Cloud Energy Consumption and Performance in Multi-Tenant Cloud Infrastructure. International Journal of Computer Applications. 181, 37 ( Jan 2019), 47-53. DOI=10.5120/ijca2019918353

@article{ 10.5120/ijca2019918353,
author = { Kenga Mosoti Derdus, Vincent Oteke Omwenga, Patrick Job Ogao },
title = { The Effect of Cloud Workload Consolidation on Cloud Energy Consumption and Performance in Multi-Tenant Cloud Infrastructure },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2019 },
volume = { 181 },
number = { 37 },
month = { Jan },
year = { 2019 },
issn = { 0975-8887 },
pages = { 47-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number37/30278-2019918353/ },
doi = { 10.5120/ijca2019918353 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:27.751167+05:30
%A Kenga Mosoti Derdus
%A Vincent Oteke Omwenga
%A Patrick Job Ogao
%T The Effect of Cloud Workload Consolidation on Cloud Energy Consumption and Performance in Multi-Tenant Cloud Infrastructure
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 37
%P 47-53
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As energy consumption is becoming a problem in cloud data centers, cloud service providers have adopted different techniques to address this problem. One of the most attractive technique is virtual machine (VM) consolidation. Apart from reducing energy consumption in computing platforms, this technique has other advantages such as reduced infrastructure costs and ease of virtual machine management. However, VM consolidation, which does not recognize workload characteristics may, in the long run, increase energy consumption and lead energy wastage. This paper investigates the relationship between different VM workload types and server energy consumption in a multi-tenant datacenters. Experiments are conducted using well known CPU, I/O, memory and network intensive workload benchmark obtained from Phoronix Test Suite (PTS). Results obtained show that there is a noticeable difference in the amount of energy consumed when VMs run workloads, which dominate the various server physical resources. Secondly, consolidating homogeneous workloads is disastrous in terms of energy consumption and performance over heterogeneous workloads. The latter can further reduce energy consumption and achieve acceptable performance levels if an optimum workload mix is reached.

References
  1. X. Chen, L. Rupprecht, R. Osman, P. Pietzuch, F. Franciosi and W. Knottenbelt, "CloudScope: Diagnosing and Managing Performance Interference in Multi-tenant Clouds," in 2015 IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 2015.
  2. Industry Outlook, "Industry Outlook Data Center Energy Efficiency," 2014. [Online]. Available: http://www.datacenterjournal.com/industry-outlook-data-center-energy-efficiency/. [Accessed 10 October 2018].
  3. M. D. Kenga, V. Omwenga and P. Ogao, "Energy Consumption in Cloud Computing Environments," in Pan African Conference on Science, Computing and Telecommunications (PACT) 2017, Nairobi, 2017.
  4. J. Patel, V. Jindal, I.-L. Yen, F. Bastani, J. Xu and P. Garraghan, "Workload Estimation for Improving Resource Management Decisions in the Cloud," in 2015 IEEE Twelfth International Symposium on Autonomous Decentralized Systems, Taichung, Taiwan, 2015.
  5. M. G. Xavier, K. J. Matteussi, F. Lorenzo and C. A. F. D. Rose, "Understanding performance interference in multi-tenant cloud databases and web applications," in 2016 IEEE International Conference on Big Data , Washington, DC, USA, 2016.
  6. S. Shen, V. v. Beek and A. Iosup, "Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters," in 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Shenzhen, China, 2015.
  7. C.-Z. Mar, S. Lavinia, A.-C. Orgerie and P. Guillaume, "An experiment-driven energy consumption model for virtual machine management systems," 2016.
  8. S. Mohsen, S. Hadi and N. Mahsa, "Power-efficient distributed scheduling of virtual machines using workload-aware consolidation techniques," The Journal of Supercomputing , 2011.
  9. M. Kurpicz, A. Sobe and P. Felber, "Using power measurements as a basis for workload placement in heterogeneous multi-cloud environments," in Proceedings of the 2nd International Workshop on CrossCloud Systems, New York, NY, USA , 2014.
  10. F. P. Sareh, "Energy-Efficient Management of Resources in Enterprise and Container-based Clouds," The University of Melbourne , 2016.
  11. J. Smith and I. Sommerville, "Workload Classification & Software Energy Measurement for Efficient Scheduling on Private Cloud Platforms," in Conference’10 University of St Andrews, 2011.
  12. A. Al-Dulaimy, R. Zantout, W. Itani and A. Zekri, "Job Submission in the Cloud: Energy Aware Approaches," in Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, 2016.
  13. V. Vasudevan, D. Andersen, M. Kaminsky, L. Tan, J. Franklin and I. Moraru, "Energy-efficient cluster computing with FAWN: workloads and implications," in Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking, Passau, Germany, 2010.
  14. A. Martin and V. Marangozova‐Martin, "Automatic benchmark profiling through advanced workflow‐based trace analysis," Wiley Online Library, 2018.
  15. Phoronix Test Suite, "Phoronix Test Suite - Linux Testing and Benchmarking Platform, Automated Testing, Open-Source Benchmarking," Phoronix Media, 2018. [Online]. Available: https://www.phoronix-test-suite.com/. [Accessed 01 August 2018].
  16. Phoronix Test Suite , "Phoronix Test Suite Suites," Phoronix Media, 2018. [Online]. Available: https://openbenchmarking.org/suites/pts. [Accessed 05 August 2018].
  17. S. M. Ismael, Y. Renyu, X. Jie and W. Tianyu, "Improved Energy-Efficiency in Cloud Datacenters with Interference-Aware Virtual Machine Placement," in Autonomous Decentralized Systems (ISADS), 2013 IEEE Eleventh International Symposium, 2013.
  18. C. Ltd, "powerstat - a tool to measure power consumption," 2018. [Online]. Available: http://manpages.ubuntu.com/manpages/bionic/man8/powerstat.8.html. [Accessed 05 August 2018].
Index Terms

Computer Science
Information Sciences

Keywords

Cloud computing cloud workloads data center energy consumption cloud workload consolidation multi-tenant cloud IaaS