SUTIL – Network selection based on utility function and integer linear programming

Pìrmez, Luci; Carvalho, Jaime C.; Delicato, Flávia C.; Protti, Fábio; Carmo, Luiz Fernando Rust da Costa; Pires, Paulo F.; Pirmez, Marcos

doi:10.1016/j.comnet.2010.03.007

Cited by 21 publications

(19 citation statements)

References 9 publications

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“…According to the RS model defined in [10], the RS for a specific user is defined as the hypothetic service representing all services transmitted by the user i. More specifically, an RS is represented by a set of representative requirements (RRs), in which each RR is calculated in order to guarantee the requirements of all services in a user.…”

Section: B Representative Service Modelmentioning

confidence: 99%

“…A bandwidth allocation scheme for maximizing the network user's satisfaction degree in multiservice wireless networks is proposed in [9]. And based on the utility function and integer linear programming, a network selection algorithm is proposed in [10]. The objectives of this algorithm are not only to maximize the user satisfaction with guaranteeing QoS, but also to minimize the energy consumption with connecting to a target network when multiple services are transmitted by one user simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Multi-User Multi-Service Rate Allocation Scheme Using Nash Bargaining Solution

Wang

Chen

Xie

2011

2011 IEEE Vehicular Technology Conference (VTC Fall)

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How to allocate rate to users with multiple services efficiently and fairly is an important issue in the wireless network.To ensure the quality of service required by multiple services of a user, efficient resource management becomes paramount. On the other hand, the fairness between users is also necessary. In this paper, we propose a bandwidth allocation scheme named as Efficiency and Inter-user Fairness (EIF). In the EIF scheme, the well-known game theoretic concept of Nash bargaining solution is deployed to allocate the total network bandwidth to users fairly and dispatch rate from user to services efficiently by maximizing the user's utility. We also propose a distributed algorithm for implementing the convex optimization problem in the EIF scheme. Simulation results show that the EIF scheme outperforms the compared schemes in that it considers both the system efficiency and the user's fairness, while other schemes only maximizes the total utility of all of the services or considers the user's fairness.

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Section: B Representative Service Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-User Multi-Service Rate Allocation Scheme Using Nash Bargaining Solution

Wang

Chen

Xie

2011

2011 IEEE Vehicular Technology Conference (VTC Fall)

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“…SUTIL [16] is a distributed network selection solution, based on utility function and integer linear programming, taking into account bandwidth, packet loss, delay and energy information. Unfortunately, the need to establish base requirements for every service used in the network and the assumption that the total network bandwidth is enough to sustain all traffic are unrealistic.…”

Section: Related Workmentioning

confidence: 99%

Towards utility-based resource management in heterogeneous wireless networks

Tsompanidis

Zahran

Sreenan

2012

Proceedings of the Seventh ACM International Workshop on Mobility in the Evolving Internet Architecture

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This work considers the network selection and the bandwidth assignment problems in the context of heterogeneous wireless networks operated by a single service provider. The current commercial and research practices on resource management are presented, and a novel utility-based approach supporting multiple client classes is introduced. A bandwidth sharing policy and a "controlled unfairness" scheme is achieved by combining distinct priority classes with logarithmic utility functions that variably grade the bandwidth allocated to clients.To demonstrate the possibilities of this approach an optimisation problem is formulated and its solution utilityoptimally allocates bandwidth and distributes clients to base stations, modelling a network-side resource management system. The centralised operation allows for network-wide provision of comparable service levels to clients of the same class. The optimal solution is compared to several heuristic methods. Simulations showcase the behaviour of the system, which successfully differentiates clients and utilises all available resources.

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“…Other works, such us [4] [5] or [6], also use optimization techniques but neither of them consider the price in their selection schemes.…”

Section: Introductionmentioning

confidence: 97%

Providing differentiated QoS with dynamic pricing policies for wireless access networks

Choque

Díez

Agüero

et al. 2013

2013 IFIP Wireless Days (WD)

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Mobile operators might need to reconsider their current policies, including the ones used for pricing. Although flat rate is at the time of writing the most prominent pricing method, it is believed to be inappropriate for forthcoming wireless communication scenarios. In this paper we propose the use of a dynamic scheme, in which base stations (BSs) modify their price according to the current carried load. By using different types of users, we assess the suitability of this scheme to provide tailored service levels. We use two complementary approaches: a distributed scheme, based on information available to a single user, is analyzed by means of an event-driven simulator that is flexible enough to settle the required level of heterogeneity and complexity on the network deployments. Besides, to assess the goodness of the obtained performance, we pose a binary linear programming problem, so as to acquire the best possible performance. In this latter case, we will also discuss the relevance of the corresponding objective function, since it might lead to quite different results.

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SUTIL – Network selection based on utility function and integer linear programming

Cited by 21 publications

References 9 publications

Multi-User Multi-Service Rate Allocation Scheme Using Nash Bargaining Solution

Multi-User Multi-Service Rate Allocation Scheme Using Nash Bargaining Solution

Towards utility-based resource management in heterogeneous wireless networks

Providing differentiated QoS with dynamic pricing policies for wireless access networks

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