SWS Academic Research eLibraryEarth & Planetary Sciences

Scholarly record

POWER CONSUMPTION MINIMIZATION MODELS IN DATA CENTERS

Delia Mihaela Radulescu

First published: 2019-06-20https://doi.org/10.5593/sgem2019/2.1/s07.068View metrics

Abstract

In the last decades the energy consumption of data centers has increased exponentially. The high energy consumption is due to power consumption of the IT resources, to the cooling system and to the inefficient usage of the resources. From the total power consumption in a data center one half is consumed by active servers and another half is consumed by the cooling system, network of the data center etc. There are several techniques for increasing the efficient usage of the resources in a data center. One of these techniques is the virtualization technology. This enables each user to work with his own virtual machine that has its own operating system. The purpose of the present paper is to present an optimization model that tries to find the best allocation of virtual machines to servers in a data center that minimizes the overall power consumption. The model has two objective functions. The first objective function is the IT power consumption of the servers. The second objective function is connected with the power consumption of the cooling system. The aim of the model is the minimization of the two objective functions. Constraints are given on the resource availability of the server resources (CPU, memory, bandwidth etc). The decision variables of the model are represented by a matrix with binary entries that describes the allocation of virtual machines to servers and by a vector with binary entries that indicates the active servers. In the paper is used a new idea for cooling power minimization. In order to do this the variance of the consumed power of the servers is minimized. This ensures the minimization of cooling power. Starting from the above multi-objective model we formulate several single objective models.

Publication Impact Profile

Publication details

Title
POWER CONSUMPTION MINIMIZATION MODELS IN DATA CENTERS
Authors
Delia Mihaela Radulescu
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 19th International Multidisciplinary Scientific GeoConference SGEM2019, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2019
Pages
517-524
SWS Citekey
Radulescu20197517524
ISSN
1314-2704
ISBN
978-619-7408-79-9
Language
en
Publication type
Conference Paper
Keywords
References12
  1. Bharathi, P. D., Prakash, P.; Kiran, M. V. K., Virtual machine placement strategies in cloud computing, In: Power and Advanced Computing Technologies (i-PACT), 2017 Innovations in. IEEE, pp 1-7, 2017.

  2. Challita S., Paraiso F., Merle P., A study of virtual machine placement optimization in data centers. ScitePress. 7th International Conference on Cloud Computing and Services Science, CLOSER 2017, Porto, Portugal. pp. 343–350, 2017.

  3. Gohil B., Shah S., Golechha Y., Patel D., A comparative analysis of virtual machine placement techniques in the cloud environment. International Journal of Computer Applications, vol.156, issue 14, pp 12-18, 2016.

  4. Khan M. A., Paplinski A., Khan A. M., Murshed M., Buyya R., Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management: A review. In Sustainable Cloud and Energy Services, pp. 135-165, 2018. Springer, Cham.

  5. Mollamotalebi M., Hajireza S., Multi-objective dynamic management of virtual machines in cloud environments, Journal of Cloud Computing: Advances, Systems and Applications, vol. 6, issue 16, pp 1-13, 2017.

  6. Rădulescu D.M., Rădulescu C.Z., Lazaroiu G., Boncea R., Binary programming models for the server consolidation problem in data centers, Proc. 18 th International Multidisciplinary Scientific GeoConference SGEM 2018, Section Informatics, vol. 18, Issue 2.1, 201-208, 2018.

  7. Rădulescu D.M., Rădulescu C.Z., Lazaroiu G., New metrics for the measurement of energy efficiency in data centers, Proc. 18 th International Multidisciplinary Scientific GeoConference SGEM 2018, Section Informatics, vol. 18, Issue 2.1, 603-610, 2018.

  8. Setzer T., Wolke A., Virtual machine re-assignment considering migration overhead, in Network Operations and Management Symposium (NOMS), 2012, IEEE 2012, pp. 631-634, 2012.

  9. Speitkamp B., Bichler M., A mathematical programming approach for server consolidation problems in virtualized data centers, IEEE Trans. Serv. Comput., vol. 3, no. 4, , pp. 266–278, 2010.

  10. Usmani Z., Singh S., A Survey of Virtual Machine Placement Techniques in a Cloud Data Center, Procedia Computer Science, vol. 78, pp 491–498, 2016.

  11. Varasteh A., Goudarzi M,, Server Consolidation Techniques in Virtualized Data Centers: A Survey, IEEE Systems Journal, Vol. 11, Issue 2, pp 772 – 783, 2017.

  12. Ye X., Yin Y., Lan L., Energy-efficient many-objective virtual machine placement optimization in a cloud computing environment, IEEE Access, vol. 5, pp. 16006-16020, 2017.

View or Download full articleAccess options
Full paper accessChoose SWS login, librarian support, or instant article download.

SWS access login

Login as SWS Scientific Committee

Authors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.

For librarian assistance: [email protected]

Purchase Instant Access

48-hour online accessComing soon
Online-only accessComing soon
Download the full article in PDF formatEUR 35
  • Article can be downloaded after successful payment.
  • Article may be used according to SWS library access terms.
  • Article cannot be redistributed.
Get full paper

Back to publication list

Connected events

SGEM WORLD SCIENCE Conferences

The EPS Library connects published research with the scientific events where it was presented, reviewed and prepared for long-term scholarly discovery.

International Scientific GeoConference SGEM
SGEM GeoConference

International Scientific GeoConference SGEM

Explore the main SGEM WORLD SCIENCE forum for Earth and Planetary Sciences, where research becomes conference presentation, proceedings publication and long-term library visibility.

Visit sgem.org
SGEM Vienna Green
SGEM Vienna Green

Green Sciences and Sustainability

Explore SGEM Vienna Green — connecting sustainability, environmental science, climate research, green technologies and interdisciplinary solutions for the future.

Visit sgemviennagreen.org

International Indexing & Global Visibility of Our Scientific Publications

Scopus

Scopus Scientific Database

Compendex

Engineering abstracts
& indexing

Clarivate

Scientific Databases

EBSCO

EBSCO
Science Services Database

OpenAIRE

Linked research discovery
& metadata

ProQuest

Scientific Databases
Part of Clarivate

Crossref

We are a voting CrossRef member - the official DOI provider

ScienceOpen

Research discovery
& metadata

HAL

Multidisciplinary archive
& metadata

Petroleum Abstracts

Scientific Database
Part of Clarivate

Google Scholar

Google Scholar
Scholarly Search

RSCI

Russian Science Citation Index
Part of Web of Science

OpenAlex

Open research index
& metadata

Mendeley

Scientific Databases
Part of Elsevier

GeoRef

GeoRef — AGI
Geoscience Bibliographic Database