Demand management for telecommunications services

In this paper we develop a novel method of controlling the demand in a multi-class, QoS-enabled network, using pricing and resource allocation for income maximisation. We first present a solution to the problem of calculating the optimal prices and QoS for a single link using a limiting regime appro...

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Veröffentlicht in:Computer networks (Amsterdam, Netherlands : 1999) Jg. 51; H. 12; S. 3507 - 3524
Hauptverfasser: Zachariadis, Grigorios, Barria, Javier A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 22.08.2007
Elsevier Science
Elsevier Sequoia S.A
Schlagworte:
Algorithms
Applied sciences
Approximation
Asymptotic approximation
Exact sciences and technology
Grammatical aspect
Heuristic
Miscellaneous
Networks
Operation, maintenance, reliability
Prices
Pricing
Quality of service
Resource allocation
Revenue management
Studies
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Teleprocessing networks. Isdn
Utility
Utility
Revenue management
Asymptotic approximation
Pricing
Resource allocation
Quality of service
Iterative method
Computation time
Distributed algorithm
Heuristic method
Resource management
Service quality
Telecommunication services
ISSN:1389-1286, 1872-7069
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Beschreibung
Zusammenfassung:In this paper we develop a novel method of controlling the demand in a multi-class, QoS-enabled network, using pricing and resource allocation for income maximisation. We first present a solution to the problem of calculating the optimal prices and QoS for a single link using a limiting regime approximation, which reduces the associated computational burden. A heuristic algorithm is then proposed that improves the limiting regime solution, achieving better results for links with small capacity. We further extend this approach to a multi-link network, where a distributed iterative algorithm is developed based on the solution of the single link model. Results from small and medium size networks show that, even when the assumptions we used do not hold, our approach yields results very close to the optimal ones (0.17–2.95% difference), which are computed by exhaustively searching in the decision space. Moreover, the calculation time using the proposed approach is approximately 1.5 min for problems which took more than 240 min to solve using exhaustive search.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2007.02.005