Virtual network embedding with pre‐transformation and incentive convergence mechanism

Efficient and fair resource allocation for multitudinous virtual networks running cloud‐based applications is crucial to archive dynamic resources multi‐tenancy in cloud computing. In order to solve the problem, we propose a novel virtual network embedding (VNE) algorithm to increase revenue and utilization of substrate network as well as to improve acceptance fairness of virtual networks. First, we present a virtual topology pre‐transformation mechanism leveraging reusable technology to reduce topology difference and achieve acceptance fairness. Then, because of the Non‐deterministic polynomial‐time (NP)‐hard characteristics of VNE, we model the problem as an integer linear programming problem and solve the VNE problem with a discrete particle swarm optimization‐based algorithm. The operations and parameters of particles are well redefined according to the VNE context. Finally, an incentive convergence mechanism is proposed to reduce mapping complexity, which can be used to accelerate convergence and to save more bandwidth by exploiting individual candidate nodes' lists. Simulation results prove that our proposed method is superior to the existing similar algorithms in terms of physical resource utilization, acceptance fairness, revenue/cost ratio, and searching efficiency. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Raouf Boutaba,et al.  A survey of network virtualization , 2010, Comput. Networks.

[2]  Jian Wang,et al.  XenLoop: a transparent high performance inter-VM network loopback , 2008, HPDC '08.

[3]  Jie Wu,et al.  Virtual Network Embedding with Opportunistic Resource Sharing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[4]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[5]  Wang Wei-bo.  Experiment and Analysis of Parameters in Particle Swarm Optimization , 2008 .

[6]  Amin Vahdat,et al.  A scalable, commodity data center network architecture , 2008, SIGCOMM '08.

[7]  Jun Fang,et al.  VMCTune: A Load Balancing Scheme for Virtual Machine Cluster Using Dynamic Resource Allocation , 2010, 2010 Ninth International Conference on Grid and Cloud Computing.

[8]  Yong Zhu,et al.  Algorithms for Assigning Substrate Network Resources to Virtual Network Components , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[9]  Mario Vento,et al.  An Improved Algorithm for Matching Large Graphs , 2001 .

[10]  Jin-Soo Kim,et al.  Inter-domain socket communications supporting high performance and full binary compatibility on Xen , 2008, VEE '08.

[11]  Holger Karl,et al.  A virtual network mapping algorithm based on subgraph isomorphism detection , 2009, VISA '09.

[12]  Dongyu Qiu,et al.  Modeling of the resource allocation in cloud computing centers , 2015, Comput. Networks.

[13]  Xiang Cheng,et al.  Virtual network embedding through topology-aware node ranking , 2011, CCRV.

[14]  Alberto Abelló,et al.  Tuning small analytics on Big Data: Data partitioning and secondary indexes in the Hadoop ecosystem , 2015, Inf. Syst..

[15]  Xiang Cheng,et al.  A unified enhanced particle swarm optimization‐based virtual network embedding algorithm , 2013, Int. J. Commun. Syst..

[16]  Xu Huang,et al.  A novel method of virtual network embedding based on topology convergence-degree , 2013, 2013 IEEE International Conference on Communications Workshops (ICC).

[17]  Raouf Boutaba,et al.  ViNEYard: Virtual Network Embedding Algorithms With Coordinated Node and Link Mapping , 2012, IEEE/ACM Transactions on Networking.

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

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

[20]  Raouf Boutaba,et al.  Virtual Network Embedding with Coordinated Node and Link Mapping , 2009, IEEE INFOCOM 2009.

[21]  Hermann de Meer,et al.  Distributed and scalable embedding of virtual networks , 2015, J. Netw. Comput. Appl..

[22]  C. Marquezan,et al.  Distributed autonomic resource management for network virtualization , 2010, 2010 IEEE Network Operations and Management Symposium - NOMS 2010.

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

[24]  Deo Prakash Vidyarthi,et al.  An Optimal Virtual Network Mapping Model Based on Dynamic Threshold , 2015, Wirel. Pers. Commun..

[25]  Liu Qiang,et al.  Virtual Network Mapping Model and Optimization Algorithms , 2012 .

[26]  Kanisius Karyono,et al.  Computational load analysis of Dijkstra, A*, and Floyd-Warshall algorithms in mesh network , 2013, 2013 International Conference on Robotics, Biomimetics, Intelligent Computational Systems.

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

[28]  S.N. Singh,et al.  Fuzzy Adaptive Particle Swarm Optimization for Bidding Strategy in Uniform Price Spot Market , 2007, IEEE Transactions on Power Systems.