Network Virtualization: Technologies, Perspectives, and Frontiers

Wang, Anjing; Iyer, M.; Dutta, Rudra; Rouskas, George N.; Baldin, Ilya

doi:10.1109/jlt.2012.2213796

Cited by 123 publications

(72 citation statements)

References 43 publications

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“…Nevertheless, when we think of network virtualization, we tend to more generally associate this concept with Computer Science. In [13], a thorough definition of this type of virtualization is provided: "Network virtualization is any form of partitioning or combining a set of network resources, and presenting (abstracting) it to users such that each user, through its set of partitioned or combined resources has a unique, separate view of the network. Resources can be fundamental (nodes, links) or derived (topologies), and can be virtualized recursively.…”

Section: Wireless Network Virtualizationmentioning

confidence: 99%

“…Within these networks, different components can be virtualized, and according to them, again, virtualization can take different shapes and nuances. As stated in [13], a given type of virtual network could become a different type of virtual network once we change the perspective. For instance, within the domain of wireless networks virtualization, we could explore infrastructure virtualization, air-interface virtualization or the virtualization of additional network components.…”

Section: Wireless Network Virtualizationmentioning

confidence: 99%

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Trading Wireless Capacity Through Spectrum Virtualization Using LTE-A

2014

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Markets for spectrum were first proposed by Ronald Coase [1] as a way to efficiently allocate this resource. It took another forty years for primary markets to be developed (in the form of spectrum auctions) as the mechanism for assigning spectrum licenses to users. It is not a secret that secondary markets would be necessary to fully realize the benefits of economic allocation of spectrum. But this is easier said than done, since spectrum is a complex, multi-dimensional product with relatively few buyers and sellers (at least for commercial mobile services), so liquid secondary markets have not emerged, even though spectrum trading through brokers is commonplace.In this paper, we find that liquidity for spectrum markets can be improved over "naked" spectrum markets [2, 3] when a standardized commodity can be traded that uses the principles of spectrum virtualization [4]. We utilize the Physical Resource Blocks (PRBs) of LTE-Advanced as the traded commodity and modify the SPEC-TRAD model developed in [5] accordingly. Though much remains to be done, we find that this is a promising approach to finally realizing liquid secondary markets in radio spectrum.

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Section: Wireless Network Virtualizationmentioning

confidence: 99%

Section: Wireless Network Virtualizationmentioning

confidence: 99%

Trading Wireless Capacity Through Spectrum Virtualization Using LTE-A

2014

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“…It is a three steps process that deals with the efficient mapping of the virtual network and the optimal constrained allocation of the shared physical resources in the substrate network [5]. The first step is the resource discovery which is usually performed by the InP to share the current state of the monitored physical network with multiple SPs.…”

Section: Related Workmentioning

confidence: 99%

Dynamic node allocation in network virtualization

Seddiki

Song

Frikha

2013

2013 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Abstract-Network virtualization is a new technology that provides a transparent abstraction of the networking resources. The most important challenge in network virtualization is the allocation of the physical substrate network among the pool of the active virtual networks (VNs). Our work in progress aims at allocating the node resources in a virtualized networking infrastructure. We believe that this allocation should be dynamic to lead to higher performance and better utilization of the physical resources. In this paper, we propose two models for dynamic node allocation for multiple VNs. The first model uses game theory and market-based approach in order to better allocate the physical node. The second one proposes a dynamic weighted round robin (WRR) approach where each VN receives a fraction of the physical node according to an estimation of its current number of waiting packets and its weight. Both models use a distributed approach to minimize the packet delays inside the physical router and to fairly allocate the nodes between different VN.

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“…Wireless access network virtualisation and cloudification of Telecom equipment and services proposed by (Wang et al, 2013), requires careful placement of compute capacity as not to introduce significant propagation delay. The placement of the compute nodes has to reflect the demand for Telecom-and cloudservices in the geographic area which the RAN covers.…”

Section: Introductionmentioning

confidence: 99%

Telco Clouds - Modelling and Simulation

Krzywda

Tärneberg²,

Östberg

et al. 2015

Proceedings of the 5th International Conference on Cloud Computing and Services Science

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Abstract:In this paper, we propose a telco cloud meta-model that can be used to simulate different infrastructure configurations and explore their consequences on the system performance and costs. To achieve this, we analyse current telecommunication and data centre infrastructure paradigms, describe the architecture of the telco cloud and detail the benefits of merging both infrastructures in a unified system. Next, we detail the dynamics of the telco cloud and identify the components that are the most relevant from the perspective of modelling performance and cost. A number of well established simulation technologies exist for most of the telco cloud components, we thus proceed with surveying existing models in an attempt to construct a suitable composite meta-model. Finally, we present a showcase scenario to demonstrate the scope of our telco cloud simulator.

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Network Virtualization: Technologies, Perspectives, and Frontiers

Cited by 123 publications

References 43 publications

Trading Wireless Capacity Through Spectrum Virtualization Using LTE-A

Trading Wireless Capacity Through Spectrum Virtualization Using LTE-A

Dynamic node allocation in network virtualization

Telco Clouds - Modelling and Simulation

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