Optimal provisioning for virtual network request in cloud-based data centers

Authors:
Gang Sun;Hongfang Yu;Vishal Anand;Lemin Li;Hao Di
Affiliations:
School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China 611731;School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China 611731;Department of Computer Science, State University of New York, College at Brockport, Brockport, USA 14420;School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China 611731;School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China 611731
Venue:
Photonic Network Communications
Year:
2012

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Abstract

Today applications and services are migrating to a cloud-computing-based paradigm in which the users access the applications and services hosted in data centers, by using thin-clients on the user terminal device. These applications/services are typically hosted and run on virtual machines in interconnected data centers. Different applications from the same user may need to access and change shared data or information. Thus, we may abstract the applications from same user as a virtual network (VN). For better performance and efficiency, it is critical that the VN request be accommodated with optimal provisioning under the current resource state of data centers. In this paper, for addressing the issue of how to design an optimal provisioning scheme for the VN request such that the total revenue of is maximized, we first develop a framework for the optimal provisioning of VN request by using mixed integer programming. Since the optimal provisioning problem is NP-hard, we also propose a genetic algorithm---based heuristic algorithm for addressing the problem of optimal provisioning for VN with unsplittable flow and optimal provisioning for VN with splittable flow problems. We demonstrate the effectiveness of our approach in improving the total revenue by conducting extensive simulations on different networks.