A maximally robustness embedding algorithm in virtual data centers with multi-attribute node ranking based on TOPSIS

The virtualization of the data center network is one of the technologies that enable the performance guarantee and more flexibility and improve the utilization of infrastructure resources in cloud computing. One of the key issues in the management of virtual data center (VDC) is VDC embedding, which...

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Published in:	The Journal of supercomputing Vol. 75; no. 12; pp. 8059 - 8093
Main Authors:	Shooshtarian, L., Safaei, F.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.12.2019 Springer Nature B.V
Subjects:	Algorithms Classification Cloud computing Compilers Computer centers Computer Science Data centers Embedding Genetic algorithms Infrastructure Interpreters Mapping Multiple objective analysis Nodes Optimization Processor Architectures Programming Languages Ranking Robustness (mathematics) Routers Shortest-path problems Sorting algorithms Substrates Switches Switching theory Topology Virtual networks Network robustness NSGAII Virtual data center network (VDC) Data center network virtualization TOPSIS Virtual network embedding algorithm (VNE) Optimization
ISSN:	0920-8542, 1573-0484
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Abstract	The virtualization of the data center network is one of the technologies that enable the performance guarantee and more flexibility and improve the utilization of infrastructure resources in cloud computing. One of the key issues in the management of virtual data center (VDC) is VDC embedding, which deals with the efficient mapping of required virtual network resources from the shared resources of the infrastructure provider (InP). In this paper, we propose a new VDC embedding algorithm that is different from previous works in many aspects. First, the provision of robustness for data center infrastructure is one of the critical requirements of cloud technology; however, this challenge has not been considered in the related literature. In order to analyze and evaluate the robustness of the infrastructure network, the classical and spectral graph robustness metrics are employed. Second, in order to avoid imbalance mapping and increase the efficiency of infrastructure resources, besides the resource dynamic capacity, four node attributes are exploited to compute the nodes mapping potential. The TOPSIS technique for nodes ranking has been used to increase the compatibility with the ideal solution. Third, unlike previous works in which the mapping phases of nodes and links are getting used to being separated, in the proposed algorithm, the virtual network is mapped to a physical network in a single step. Fourth, we also consider resources for network nodes (switches or routers). For these purposes, a multi-objective mathematical optimization problem is extracted with two goals of maximizing infrastructure network robustness and minimizing the long-term average cost-to-revenue ratio mapping for InPs. Finally, a new single-stage (non-dominated sorting-based genetic algorithm) NSGAII-based online VDCE algorithm is presented, where node mapping is TOP-MANR based and edge mapping is based on the shortest path. The fat-tree topology is considered for the substrate and virtual networks, and these two networks are modeled as a weighted undirected graph.
AbstractList	The virtualization of the data center network is one of the technologies that enable the performance guarantee and more flexibility and improve the utilization of infrastructure resources in cloud computing. One of the key issues in the management of virtual data center (VDC) is VDC embedding, which deals with the efficient mapping of required virtual network resources from the shared resources of the infrastructure provider (InP). In this paper, we propose a new VDC embedding algorithm that is different from previous works in many aspects. First, the provision of robustness for data center infrastructure is one of the critical requirements of cloud technology; however, this challenge has not been considered in the related literature. In order to analyze and evaluate the robustness of the infrastructure network, the classical and spectral graph robustness metrics are employed. Second, in order to avoid imbalance mapping and increase the efficiency of infrastructure resources, besides the resource dynamic capacity, four node attributes are exploited to compute the nodes mapping potential. The TOPSIS technique for nodes ranking has been used to increase the compatibility with the ideal solution. Third, unlike previous works in which the mapping phases of nodes and links are getting used to being separated, in the proposed algorithm, the virtual network is mapped to a physical network in a single step. Fourth, we also consider resources for network nodes (switches or routers). For these purposes, a multi-objective mathematical optimization problem is extracted with two goals of maximizing infrastructure network robustness and minimizing the long-term average cost-to-revenue ratio mapping for InPs. Finally, a new single-stage (non-dominated sorting-based genetic algorithm) NSGAII-based online VDCE algorithm is presented, where node mapping is TOP-MANR based and edge mapping is based on the shortest path. The fat-tree topology is considered for the substrate and virtual networks, and these two networks are modeled as a weighted undirected graph.
Author	Shooshtarian, L. Safaei, F.
Author_xml	– sequence: 1 givenname: L. surname: Shooshtarian fullname: Shooshtarian, L. organization: Faculty of Computer Science and Engineering, Shahid Beheshti University G.C – sequence: 2 givenname: F. surname: Safaei fullname: Safaei, F. email: f_safaei@sbu.ac.ir organization: Faculty of Computer Science and Engineering, Shahid Beheshti University G.C
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Keywords	Network robustness NSGAII Virtual data center network (VDC) Data center network virtualization TOPSIS Virtual network embedding algorithm (VNE) Optimization
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References_xml	– reference: Zhang Q, Zhani MF, Jabri M, Boutaba R (2014) Venice: reliable virtual data center embedding in clouds. In: IEEE INFOCOM 2014—IEEE Conference on Computer Communications, Toronto, ON, pp 289–297 – reference: Bodík P, Menache I, Chowdhury M, Mani P, Maltz DA, Stoica I (2012) Surviving failures in bandwidth-constrained datacenters. In: Proceedings of Conference on Applications, Technologies, Architecture, and Protocols for Computer Communication, pp 431–442 – reference: Wang T, Qin B, Hamdi M (2015) An efficient framework for online virtual network embedding in virtualized cloud data centers. In: 2015 IEEE 4th International Conference on Cloud Networking (CloudNet), Niagara Falls, ON, pp 159–164 – reference: KarBWuEH-KLinY-DEnergy cost optimization in dynamic placement of virtualized network function chainsIEEE Trans Netw Serv Manag201815137238610.1109/TNSM.2017.2782370 – reference: FiedlerMAlgebraic connectivity of graphsCzechoslov Math J19732322983053180070265.05119 – reference: Vishwanath KV, Nagappan N (2010) Characterizing cloud computing hardware reliability. In: Proceedings of ACM Symposium on Cloud Computing (SoCC) – reference: Mi X, Chang X, Liu J, Sun L, Xing B (2012) Embedding virtual infrastructure based on genetic algorithm. In: 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies, Beijing, pp 239–244 – reference: Bigdeli A, Tizghadam A, Garcia AL (2009) Comparison of network criticality, algebraic connectivity, and other graphmetrics. In: Proceedings of the 1st Annual Workshop on Simplifying Complex Network for Practitioners – reference: CorreaESFletscherLABoteroJFVirtual data center embedding: a surveyIEEE Latin Am Trans20151351661167010.1109/TLA.2015.7112029 – reference: Tizghadam A, Leon-Garcia A (2008) On robust traffic engineering in core networks. In: IEEE GLOBECOM, December 2008 – reference: GreenbergAThe cost of a cloud: research problems in data center networksACM SIGCOMM Comput Commun Rev20093916879121149710.1145/1496091.1496103 – reference: Herker S, Khan A, An X (2013) Survey on survivable virtual network embedding problem and solutions. In: The Ninth International Conference on Networking and Services, pp 99–104 – reference: Velocity and the bottom line. http://radar.oreilly.com/2009/07/velocity-making-your-site-fast.html. Accessed 17 Aug 2017 – reference: ZhengXTianJXiaoXA heuristic survivable virtual network mapping algorithmSoft Comput20182351453146310.1007/s00500-018-3152-7 – reference: AmokraneAZhaniMFLangarRBoutabaRPujolleGGreenhead: virtual data center embedding across distributed infrastructuresIEEE Trans Cloud Comput201311364910.1109/TCC.2013.5 – reference: Rost M, Schmid S (2018) NP-completeness and inapproximability of the virtual network embedding problem and its variants. arXiv:1801.03162 [CoRR] – reference: KatohNIbarakiTMineHAn efficient algorithm for k-shortest simple-pathsNetworks19821241142768654210.1002/net.3230120406 – reference: DebKPratapAAgarwalSMeyarivanTA fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Trans Evol Comput20026218219710.1109/4235.996017 – reference: WeiPengSunDengfengWeighted Algebraic Connectivity: An Application to Airport Transportation NetworkIFAC Proceedings Volumes2011441138641386910.3182/20110828-6-IT-1002.00486 – reference: Tavakoli-SomehSRezvaniMHMulti-objective virtual network function placement using NSGA-II meta-heuristic approachJ Supercomput201910.1007/s11227-019-02849-y – reference: Jamakovic A, Uhlig S (2007) Influence of the network structure on robustness. In: 2007 15th IEEE International Conference on Networks, Adelaide, SA, pp 278–283 – reference: Zhani MF, Zhang Q, Simona G, Boutaba R (2013) VDC planner: dynamic migration-aware virtual data center embedding for clouds. In: 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), Ghent, pp 18–25 – reference: BhamareDSamakaMErbadAJainRGuptaLChanHAOptimal virtual network function placement in multi-cloud service function chaining architectureComput Commun201710211610.1016/j.comcom.2017.02.011 – reference: BorgattiSPCentrality and network flowSoc Netw2005271557195185810.1016/j.socnet.2004.11.008 – reference: Gilesh MP, Kumar SDM, Jacob L, Bellur U (2017) Towards a complete virtual data center embedding algorithm using hybrid strategy. 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Snippet	The virtualization of the data center network is one of the technologies that enable the performance guarantee and more flexibility and improve the utilization...
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SubjectTerms	Algorithms Classification Cloud computing Compilers Computer centers Computer Science Data centers Embedding Genetic algorithms Infrastructure Interpreters Mapping Multiple objective analysis Nodes Optimization Processor Architectures Programming Languages Ranking Robustness (mathematics) Routers Shortest-path problems Sorting algorithms Substrates Switches Switching theory Topology Virtual networks
Title	A maximally robustness embedding algorithm in virtual data centers with multi-attribute node ranking based on TOPSIS
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