By Topic

Toward a Genetic Algorithm Based Flexible Approach for the Management of Virtualized Application Environments in Cloud Platforms

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)
Abdul-Rahman, O. ; Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan ; Munetomo, M. ; Akama, K.

Resource management in cloud platforms becomes an increasingly complex and daunting task surrounded by various challenges of stringent QoS requirements, service availability guaranteeing and escalating overhead of the infrastructure that resulted from operation costs and ecological impact. On the other hand, virtualization adds a greater flexibility to the resource managers in addressing such challenges. However, at the same time, it imposes a further challenge of added management complexity. Recently, we have proposed a resource management model for cloud platforms, which utilizes a new resource mapping formulation and relays on a hybrid virtualization framework in an attempt to realize a resource manager that intelligently adapts the available cloud resources to satisfy the conflicting objectives of the running applications and underlying infrastructures' requirements. Moreover, we have proposed state of the art Binary-Real coded Genetic Algorithm (BRGA), which has been applied successfully to a wide spectrum of global and constrained optimization problems from the known benchmark suites. In this paper, we aim to proceed by proposing a mathematical model and a modified version of BRGA to validate our model. In addition, we aim to evaluate the feasibility, effectiveness and scalability of our approach through simulation experiments.

Published in:

Computer Communications and Networks (ICCCN), 2012 21st International Conference on

Date of Conference:

July 30 2012-Aug. 2 2012