By Topic

Affinity-aware Virtual Cluster Optimization for MapReduce Applications

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

7 Author(s)
Cairong Yan ; Sch. of Comput. Sci. & Technol., Donghua Univ., Shanghai, China ; Ming Zhu ; Xin Yang ; Ze Yu
more authors

Infrastructure-as-a-Service clouds are becoming ubiquitous for provisioning virtual machines on demand. Cloud service providers expect to use least resources to deliver best services. As users frequently request virtual machines to build virtual clusters and run MapReduce-like jobs for big data processing, cloud service providers intend to place virtual machines closely to minimize network latency and subsequently reduce data movement cost. In this paper we focus on the virtual machine placement issue for provisioning virtual clusters with minimum network latency in clouds. We define distance as the latency between virtual machines and use it to measure the affinity of virtual clusters. Such metric of distance indicates the considerations of virtual machine placement and topology of physical nodes in clouds. Then we formulate our problem as the classical shortest distance problem and solve it by modeling to integer programming problem. A greedy virtual machine placement algorithm is designed to get a compact virtual cluster. Furthermore, an improved heuristic algorithm is also presented for achieving a global resource optimization. The simulation results verify our algorithms and the experiment results validate the improvement achieved by our approaches.

Published in:

Cluster Computing (CLUSTER), 2012 IEEE International Conference on

Date of Conference:

24-28 Sept. 2012