How to Maximize User Satisfaction Degree in Multi-service IP Networks

Nguyen, Huy A.; Nguyên, Tam; Choi, Deokjai

doi:10.1109/aciids.2009.16

Cited by 11 publications

(11 citation statements)

References 18 publications

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“…According to [9], we assume in this work that any service supported belongs to three categories, the elastic service, the hard real-time service, and the real-time service.The elastic service includes file transfer, e-mail and web browsing. For this service, r min is small since it can tolerate relatively large delay.…”

Section: A Three Categories Of Service and Their Utility Functionmentioning

confidence: 99%

“…However, these bandwidth allocation schemes only consider a single service such as video stream or the abstract service with same type for one user, and do not address how to allocate bandwidth for a user with multiple services. 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].…”

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: A Three Categories Of Service and Their Utility Functionmentioning

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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“…Few prior researchers have investigated discrete priority classes, which we leverage in our approach, within the context of NUM. The authors of [41] propose assigning more bandwidth to users (i.e. via weighting their requests higher) based on their requested priority levels.…”

Section: Background On Iot Data Exchangementioning

confidence: 99%

FireDeX

Benson

Bouloukakis

Grant

et al. 2018

Proceedings of the 19th International Middleware Conference

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Real-time event detection and targeted decision making for emerging mission-critical applications, e.g. smart fire fighting, requires systems that extract and process relevant data from connected IoT devices in the environment. In this paper, we propose FireDeX, a cross-layer middleware that facilitates timely and effective exchange of data for coordinating emergency response activities. FireDeX adopts a publish-subscribe data exchange paradigm with brokers at the network edge to manage prioritized delivery of mission-critical data from IoT sources to relevant subscribers. It incorporates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics (e.g. delays, success rates). We design an extensible queueing theoretic model that abstracts these cross-layer interactions as a network of queues, thereby making it amenable for rapid analysis. We propose novel algorithms that utilize results of this analysis to tune data exchange configurations (event priorities and dropping policies) while meeting situational awareness requirements and resource constraints. FireDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate its performance through simulated experiments in a smart building fire response scenario. Our results demonstrate significant improvement to mission-critical data delivery under a variety of conditions. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.

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“…Some of these areas are QoE and traffic‐aware utility functions : Current wireless networks provide various kinds of service in addition to voice and data traffic, for instance, online conference, online shopping, and multimedia download. These services and applications can be covered by the following categories of elastic application, hard real‐time application, and real‐time application from the user satisfaction perspective . That is why the QoE is considered as a more advanced QoS representation; thus, more attention is required on utility function design for QoE .…”

Section: Open Research Topicsmentioning

confidence: 99%

Utility function design for strategic radio resource management games: An overview, taxonomy, and research challenges

Yang

Ejaz

et al. 2016

Trans. Emerging Tel. Tech.

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Radio resource management is important for wireless communication networks. Game theory has been extensively used to model, analyze, and design interactive behaviors and the strategic decision‐making for radio resource management. It is known that utility function is one of the critical elements in a game, which characterizes the preferred relationship of the rational players and is a function of the action of players and their opponents. We first overview the basics of game theory and utility functions. We then present a taxonomy of utility functions with respect to different types of players, the nature of actions, and preferences in terms of the fairness, quality of service, and quality of experience. We provide some insights based on the taxonomy of utility functions, which provides the readers with a comprehensive view. Following that, we also discuss other types of traffic‐aware utility functions with different fairness and the potential and super modular game‐theoretic utility functions. In addition, we summarize the desired properties and observations for the design of suitable utility functions. Finally, we investigate impacts of the pricing in utility functions. This article ends with the conclusions and a promising view on open problems and challenges with possible future research directions.

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How to Maximize User Satisfaction Degree in Multi-service IP Networks

Cited by 11 publications

References 18 publications

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

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

FireDeX

Utility function design for strategic radio resource management games: An overview, taxonomy, and research challenges

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