Pricing Utility-Based Virtual Networks

Samaan, Nancy

doi:10.1109/tnsm.2013.043013.120304

Cited by 12 publications

(5 citation statements)

References 48 publications

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“…In addition, the endpoints of service requests in network slicing are bound to access points or forwarding nodes, while there might be no such requirement for VNE requests. Besides, most work on VNE is focused on maximizing the acceptance ratio of VNE requests with static resource allocation schemes, with little attention dedicated to the dynamic scaling of virtual networks [9], [33]. Therefore, it is interesting to investigate the difference between VNE and network slicing, in order to design new solutions for network slicing.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the operator can control the resource allocation to achieve its objective, e.g., minimizing the operational cost or maximizing profit. References [19] and [34]- [36] are focused on maximizing network utility of elastic traffic, and [33] aims to maximize the social welfare of the network, i.e., the user utility minus the resource cost. The network utility related methods provide the insight of exploiting economic mechanism to facilitate resource allocation to individual users/services, so as to achieve fairness and avoid congestion, etc.…”

Section: Related Workmentioning

confidence: 99%

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Optimizing Network Slice Dimensioning via Resource Pricing

et al. 2019

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Network slicing has been viewed as a key enabler for the next-generation software-defined and cloud-based network (e.g., 5G and beyond) to accommodate diverse services in a flexible and costefficient fashion. Network slicing allows a network slice provider (NSP) to operate on a common network infrastructure to create customized isolated logical networks (i.e., network slices) for network slice customers (NSCs), (i.e., service providers). NSP and NSCs are independent operators who pursue profit maximization, while in the literature, only network cost optimization is intensively investigated in terms of service function chain embedding, i.e., virtual network function (VNF) placement and flow routing. Therefore, slices should be dimensioned (i.e., resources allocated to slices) according to the resource availability and the economic mechanism in the network, so as to optimize the resource utilization and improve the profit of NSP/NSCs. In this paper, we study elastic slice dimensioning with resource pricing as a Stackelberg pricing game, in which the NSP sells slices by pricing resources and NSCs adjust their slice's resource demand on VNF capacity and bandwidth, while both are trying to maximize their profit. Then, we formulate optimization problems for the pricing game and find that a closed form solution of the optimal price cannot be obtained for a non-trivial network. Hence, we propose a resource pricing algorithm that aims to maximize the NSP's profit and the network's social welfare. Compared with existing usage-based pricing method and two heuristic methods, our proposed pricing algorithm for slice dimensioning strikes a trade-off between maximizing NSP's profit and other metrics, including the resource utilization. Hence, it will helpfully exploiting the benefits of network slicing.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimizing Network Slice Dimensioning via Resource Pricing

et al. 2019

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“…If there is more than one request, the ties are broken based on the IaaS provider's profit from embedding each request. 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 …”

Section: Resource‐aware Vdcne Approachmentioning

confidence: 99%

“…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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“…Papagianni et al [19] tackled the problem of providing a unified resource allocation framework for networked clouds with the goal of minimizing the cost of resource mapping procedures. Ghazar and Samaan [20] proposed a pricing mechanism for virtual network services to regulate the demand for their shared substrate network resources. Guazzone et al [21] proposed a framework for dynamic management of computing resources in order to achieve suitable QoS levels and to reduce the amount of energy consumption for providing services.…”

Section: Related Workmentioning

confidence: 99%

An Online Mechanism for Resource Allocation and Pricing in Clouds

Mashayekhy

Nejad

Grosu

et al. 2016

IEEE Trans. Comput.

132

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Cloud providers provision their various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances which are then allocated to the users. The users are charged based on a pay-as-you-go model, and their payments should be determined by considering both their incentives and the incentives of the cloud providers. Auction markets can capture such incentives, where users name their own prices for their requested VMs. We design an auction-based online mechanism for VM provisioning, allocation, and pricing in clouds that consider several types of resources. Our proposed online mechanism makes no assumptions about future demand of VMs, which is the case in real cloud settings. The proposed online mechanism is invoked as soon as a user places a request or some of the allocated resources are released and become available. The mechanism allocates VM instances to selected users for the period they are requested for, and ensures that the users will continue using their VM instances for the entire requested period. In addition, the mechanism determines the payment the users have to pay for using the allocated resources. We prove that the mechanism is incentive-compatible, that is, it gives incentives to the users to reveal their actual requests. We investigate the performance of our proposed mechanism through extensive experiments.

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Pricing Utility-Based Virtual Networks

Cited by 12 publications

References 48 publications

Optimizing Network Slice Dimensioning via Resource Pricing

Optimizing Network Slice Dimensioning via Resource Pricing

Resource availability–aware adaptive provisioning of virtual data center networks

An Online Mechanism for Resource Allocation and Pricing in Clouds

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