Influence of the grade of service in the evaluation of the mean reserved bandwidth for elastic services

Postigo-Boix, Marcos; Melús-Moreno, José Luis

doi:10.1109/lcomm.2008.071659

Cited by 4 publications

(2 citation statements)

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“…The price of each class of these services is based on: the average rate of the accepted class of elastic reservations with guaranteed GoS and their mean reserved bandwidth per accepted request, , res i B . In [5] as a new component in the determination of the price of the elastic reservations and also carries out the analysis and calculation of the elasticity of the reservations that maximizes a chosen revenue function. Although many methods to price services have been proposed only a few are focused on elastic reservations but neither of them has jointly tackled the issues treated in this paper.…”

Section: Related Workmentioning

confidence: 99%

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Analysis of a Pricing Method for Elastic Services with Guaranteed GoS

Postigo-Boix¹,

Melús-Moreno²

2012

JSSM

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Service Providers (SPs), which offer services based on elastic reservations with a guaranteed Grade of Service (GoS), should know how to price these services and how to quantify the benefits in different scenarios. This paper analyzes a method for evaluating the price of a service based on elastic reservations with a guaranteed Grade of Service. The method works as follows: First, the SP determines the requirements of the service that wants to offer; Second, the SP evaluates the average rate of the accepted elastic reservations of the service with a guaranteed GoS; Third, the SP calculates the price that guarantees the GoS with an aggregate demand function that depends on a demand modulation factor of the elastic reservations that is the mean reserved bandwidth, B res ; and Finally, the SP obtains the optimum value of the elasticity of the reservations that gives the maximum revenue, and the required access bandwidth in this case. The paper not only applies the method to a class i of elastic reservations when a linear-based demand and a revenue function are selected, but it also analyzes the influence of each one of the considered parameters. This method could be extended to the case of multiple classes of independent and guaranteed elastic services, applying the method to each service with its estimated demand and revenue functions.

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Section: Related Workmentioning

confidence: 99%

“…The mean reserved bandwidth per accepted reservation request for class I [5], is expressed as follow:…”

Section: A3 Evaluation Of the Mean Reserved Bandwidth Per Accepted mentioning

confidence: 99%