Pricing for Mobile Virtual Network Operators: The contribution of u-map

Meidanis, Charalampos; Stiakogiannakis, Ioannis; Papadopouli, Maria

doi:10.1109/dyspan.2014.6817788

Cited by 6 publications

(2 citation statements)

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“…In a fully virtualized network environment, usage charges of individual network devices can be done with acceptable accuracy . The pricing of virtual network is an independent problem by itself i0.1em0.1em0.1emfalse(∈scriptSfalse) as shown:

P R_{i} = t_{d u r}^{i} ((), \sum_{n \in false{M E M, S C false}} ((), R e v_{n}^{i} - C o s t_{n}^{i}) + \sum_{e \in false{B W false}} ((), R e v_{e}^{i} - C o s t_{e}^{i})) .

R e v_{n}^{i}

and

R e v_{e}^{i}

are the revenue associated with node resources and link resource, respectively, of VDCN i for unit time.…”

Section: Resource‐aware Vdcne Approachmentioning

confidence: 99%

Resource availability–aware adaptive provisioning of virtual data center networks

Gilesh¹,

Kumar

Jacob

2019

Int J Network Mgmt

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Optimal provisioning of virtual networks with varying topologies on their physical counterpart is a major challenge in cloud data centers. Embedding the virtual network on the shared physical infrastructure poses a multitude of challenges to the infrastructure provider. This paper proposes a hybrid approach which adapts to the availability and fragmentation level of residual resources for near-optimal mapping of multiple virtual data center networks on a substrate data center network. The proposed technique uses coordinated static and dynamic embedding phases, adaptively, to maximize the density of embedding which increases the revenue of the cloud service provider. Also, it judiciously modifies the existing mappings for efficient utilization of resources. Empirical results show that the proposed hybrid approach can attain ∼10% higher acceptance ratio compared with the existing exact and heuristic techniques in the literature. With the proposed mixed integer programming model for static embedding, the average path length is reduced by 20% compared with the existing techniques.Int J Network Mgmt. 2019;29:e2066.wileyonlinelibrary.com/journal/nem

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P R_{i} = t_{d u r}^{i} ((), \sum_{n \in false{M E M, S C false}} ((), R e v_{n}^{i} - C o s t_{n}^{i}) + \sum_{e \in false{B W false}} ((), R e v_{e}^{i} - C o s t_{e}^{i})) .

R e v_{n}^{i}

and

R e v_{e}^{i}

are the revenue associated with node resources and link resource, respectively, of VDCN i for unit time.…”

Section: Resource‐aware Vdcne Approachmentioning

confidence: 99%

Resource availability–aware adaptive provisioning of virtual data center networks

Gilesh¹,

Kumar

Jacob

2019

Int J Network Mgmt

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“…Typically, operators collect such information from market surveys, while CoRLAB employed u-map to obtain this information. The analysis shows that in the absence of capacity constraints, the optimal pricing strategies for both operators depend only on the distribution of users' willingness to pay [16]. The impact of the availability of information on the design of tariff plans was then analyzed via simulations.…”

Section: Pricing For Mobile Virtual Network Operatorsmentioning

confidence: 99%

On user-centric tools for QoE-based recommendation and real-time analysis of large-scale markets

et al. 2014

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This article focuses on mechanisms that empower users with quality of experience recommendations and smart real-time analytics. It presents a user-centric recommendation system (called u-map) that enables users to collect network measurements and subjective opinion scores about the performance of various services. It also cross-correlates measurements obtained by u-map to provide geo-statistics, user profiles, and quality of experience prediction models for different services. The article also presents CoRLAB, a modular multi-layer framework for modeling and assessing various markets, services, and their evolution under a diverse set of customer populations and conditions. Umap feeds CoRLAB with user measurements and feedback in (semi) real-time. The article discusses how u-map and CoRLAB have been used to analyze telecommunication markets and services. It highlights the main research results, challenges, and potential research directions.

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