By Topic

Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Mark, C.C.T. ; Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore ; Niyato, D. ; Tham Chen-Khong

Cloud computing allows the users to efficiently and dynamically provision computing resources to meet their IT needs. Most cloud providers offer two types of payment plans to the user, i.e., reservation and on-demand. The reservation plan is typically cheaper than the on-demand plan but reservation plan has to be provisioned in advance. Reserving the resources would be straightforward if the actual computing demand (e.g., job processing) is known in advance. However, in reality, the actual computing demand can be observed only at the point of actual usage. Therefore, it is difficult to reserve the correct amount of resources during the reservation to meet the computing demands of the users. In this paper, we propose an evolutionary optimal virtual machine placement (EOVMP) algorithm with a demand forecaster. First, a demand forecaster predicts the computing demand. Then, EOVMP uses this predicted demand to allocate the virtual machines using reservation and on-demand plans for job processing. The performance of the proposed schemes is evaluated by simulations and numerical studies. The evaluation result shows that the EOVMP algorithm can provide the solution close to the optimal solution of stochastic integer programming (SIP) and the prediction of the demand forecaster is of reasonable accuracy.

Published in:

Advanced Information Networking and Applications (AINA), 2011 IEEE International Conference on

Date of Conference:

22-25 March 2011