A prediction-based dynamic resource management approach for network virtualization

In network virtualization environment, multiple virtual networks share the same resource of a physical network. Since the physical resources of a substrate network is limited, it is necessary to improve the utilization of physical resources. Considering the resource requirement of a virtual network may change over its lifetime, we propose a prediction-based resource management mechanism. To increase the utilization of the substrate network, we can adjust the resource allocated to the virtual network based on the result of prediction. Additionally, in order to avoid the result of prediction deviates from the real requirement, we compare our prediction result with the collection of the resource utilization at real time to ensure the correctness of our result. The simulation results show that our approach can increase the utilization of the physical resource and improve the virtual network acceptance ratio while ensuring the requirement of the virtual networks.

[1]  Lemin Li,et al.  A cost efficient framework and algorithm for embedding dynamic virtual network requests , 2013, Future Gener. Comput. Syst..

[2]  Xavier Hesselbach,et al.  Virtual Network Embedding: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[3]  Chunguang Zhou,et al.  Particle swarm optimization for traveling salesman problem , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[4]  Andreas Timm-Giel,et al.  Optimal mapping of virtual networks considering reactive reconfiguration , 2012, 2012 IEEE 1st International Conference on Cloud Networking (CLOUDNET).

[5]  David Dietrich,et al.  Multi-domain virtual network embedding with limited information disclosure , 2013, 2013 IFIP Networking Conference.

[6]  Raouf Boutaba,et al.  SVNE: Survivable Virtual Network Embedding Algorithms for Network Virtualization , 2013, IEEE Transactions on Network and Service Management.

[7]  R. Meyer,et al.  The Fundamental Theorem of Exponential Smoothing , 1961 .

[8]  C. Holt Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  Gwendal Simon,et al.  VDC Planner: Dynamic migration-aware Virtual Data Center embedding for clouds , 2013, 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013).

[11]  Djamal Zeghlache,et al.  Virtual network provisioning across multiple substrate networks , 2011, Comput. Networks.

[12]  Xiang Cheng,et al.  Virtual network embedding through topology awareness and optimization , 2012, Comput. Networks.

[13]  Jonathan S. Turner,et al.  Diversifying the Internet , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[14]  Minlan Yu,et al.  Rethinking virtual network embedding: substrate support for path splitting and migration , 2008, CCRV.

[15]  Raouf Boutaba,et al.  Network virtualization: state of the art and research challenges , 2009, IEEE Communications Magazine.

[16]  Scott Shenker,et al.  Overcoming the Internet impasse through virtualization , 2005, Computer.