A VLSI fuzzy expert system for real-time traffic control in ATM networks

Ascia, Giuseppe; Catania, Vincenzo; Ficili, G.; Palazzo, Sergio; Panno, Daniela

doi:10.1109/91.554444

Cited by 41 publications

(14 citation statements)

References 26 publications

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“…The reasons could be the lack of comprehensive performance comparisons between the best traditional techniques and the ones involving Soft Computing applications. The comparisons performed in the research studies usually have been undertaken in simplified networking scenarios, and testing on real hardware has not been undertaken yet except for some partial implementations such as in (Ascia et al 1997). Before the applications of Soft Computing techniques to high speed communication networks become accepted, it will be necessary to place a greater emphasis on rigorous demonstration of the advantages to be gained.…”

Section: Conclusion and Recommendationsmentioning

confidence: 98%

“…For example, well studied mechanisms such as leaky bucket and window mechanisms cannot achieve the ideal UPC characteristics but only provide a trade-off between the above requirements. Catania et al (1995Catania et al ( , 1996a and Ascia et al (1997) have proposed a UPC mechanism based on fuzzy logic control which displays characteristics close to ideal UPC, and have also implemented the algorithm as a VLSI chip. The mechanism they propose ensures that a bursty source conforms to its agreed average cell rate.…”

Section: Traffic Policing Usage Parameter Controlmentioning

confidence: 99%

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A roadmap of fuzzy logic in ATM, mobile and active networks: challenges of the new century

Vasilakos

2003

Soft Computing - A Fusion of Foundations, Methodologies and App

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ATM networking technology was conceived 20 years ago, and installations will reach their peak the next 10 years. Active networks will be the technology which will follow the ATM. In this article, we address the most important issues regarding recent advances and future perspectives in ATM, including IP/ATM integration, Active Networks, MobileActive Networks and the impact of fuzzy technology in solving the important problems of the above future networking technologies. In the new century it is imperative that we shift from a technology to an application (needs of consumers) focus, where ubiquitous and invisible (context-aware) computing will be a reality.

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Section: Conclusion and Recommendationsmentioning

confidence: 98%

Section: Traffic Policing Usage Parameter Controlmentioning

confidence: 99%

A roadmap of fuzzy logic in ATM, mobile and active networks: challenges of the new century

Vasilakos

2003

Soft Computing - A Fusion of Foundations, Methodologies and App

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“…Its fuzzy logic model consisted in three network parameters: (1) congestion, (2) quality and (3) network capacity. Ascia et al (1997) Cheng et al (1999) had also used this approach, but in the context of high-speed multimedia networks. Although there are different proposed unconventional CACs, the embodied knowledge in conventional methods is difficult to be incorporated into ANN or fuzzy logic design.…”

Section: Unconventional Admission Controlsmentioning

confidence: 99%

“…Several works with fuzzy logic are also presented in the literature. Ascia et al (1997) present a fuzzy logic-based CAC for ATM networks. Fuzzy logic-based CAC was designed to know which decisions must be taken in case of traffic load level variations.…”

Section: Unconventional Admission Controlsmentioning

confidence: 99%

Conventional and Unconventional Cellular Admission Control Mechanisms

Tostes¹,

Duarte-Figueiredo²,

Zárate³

2011

Recent Developments in Mobile Communications - A Multidisciplinary Approach

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“…The central engineering goal is to specify methods for the design of useful, intelligent artifacts. There are some concepts of CI like: fuzzy sets and systems, neural networks, neurofuzzy networks and systems, genetic algorithms, evolutionary algorithms, and etc.Due to highly nonlinear behavior of telecommunication systems and uncertainty in the parameters, using the CI and Artificial Intelligence (AI) techniques in these systems has been widely increased in recent years [11][12][13][14][15][16][17]. CI and AI techniques can be used for the design and management of communication networks.…”

mentioning

confidence: 99%

Application of Computational Intelligence (Fuzzy Logic, Neural Networks and Evolutionary Programming) to Active Networking Technology

Galily

Roudsari

Sadri

2005

Fuzzy Systems and Knowledge Discovery

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Abstract. Computational intelligent techniques, e.g., neural networks, fuzzy systems, neuro-fuzzy systems, and evolutionary algorithms have been successfully applied for many engineering problems. These methods have been used for solving control problems in packet switching network architectures. The introduction of active networking adds a high degree of flexibility in customizing the network infrastructure and introduces new functionality. Therefore, there is a clear need for investigating both the applicability of computational intelligence techniques in this new networking environment, as well as the provisions of active networking technology that computational intelligence techniques can exploit for improved operation. We report on the characteristics of these technologies, their synergy and on outline recent efforts in the design of a computational intelligence toolkit and its application to routing on a novel active networking environment. IntroductionThe events in the area of computer networks during the last few years reveal a significant trend toward open architecture nodes, the behavior of which can easily be controlled. This trend has been identified by several developments [1,2] such as:• Emerging technologies and applications that demand advanced computations and perform complex operations • Sophisticated protocols that demand access to network Resources • Research toward open architecture nodes Active Networks (AN), a technology that allows flexible and programmable open nodes, has proven to be a promising candidate to satisfy these needs. AN is a relatively new concept, emerged from the broad DARPA community in 1994-95 [1,3,4]. In AN, programs can be "injected" into devices, making them active in the sense that their behavior and the way they handle data can be dynamically controlled and customized. Active devices no longer simply forward packets from point to point; instead, data is manipulated by the programs installed in the active nodes (devices). Packets may be classified and served on a per-application or per-user basis. Complex tasks and computations may be performed on the packets according to the content of [5][6][7][8][9][10]. CI is the study of the design of intelligent agents. An agent is something that acts in an environment-it does something. Agents include worms, dogs, thermostats, airplanes, humans, organizations, and society. An intelligent agent is a system that acts intelligently: What it does is appropriate for its circumstances and its goal, it is flexible to changing environments and changing goals, it learns from experience, and it makes appropriate choices given perceptual limitations and finite computation. The central scientific goal of computational intelligence is to understand the principles that make intelligent behavior possible, in natural or artificial systems. The main hypothesis is that reasoning is computation. The central engineering goal is to specify methods for the design of useful, intelligent artifacts. There are some concepts of CI like: fuzzy sets and ...

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A VLSI fuzzy expert system for real-time traffic control in ATM networks

Cited by 41 publications

References 26 publications

A roadmap of fuzzy logic in ATM, mobile and active networks: challenges of the new century

A roadmap of fuzzy logic in ATM, mobile and active networks: challenges of the new century

Conventional and Unconventional Cellular Admission Control Mechanisms

Application of Computational Intelligence (Fuzzy Logic, Neural Networks and Evolutionary Programming) to Active Networking Technology

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