Guest Editorial Computational and Artificial Intelligence ... - IEEE Xplore

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 15, NO. 2, FEBRUARY 1997

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Guest Editorial Computational and Artificial Intelligence in High Speed Networks

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ECENTLY, there has been significant research, development, and trial activity in the area of high speed networking, particularly stimulated by the emergence of the ATM standard. Resource management has turned out to be a significant challenge for a number of reasons. For example: 1) the large increase in bandwidth means that traditional controls (which are limited by propagation latency) are now too slow to respond to network effects; 2) the intent that these services be able to carry multimedia traffic requires isochronous service guarantees; 3) new services (e.g., the World Wide Web) have overturned previous assumptions about how data networks are used. These resource management problems include flow and congestion control, admission control, traffic modeling and characterization, network management and fault diagnosis, interfaces to other networks (e.g., wireless), etc. The traditional techniques of traffic engineering, queueing analysis, decision theory, etc. have been supplemented with a large variety of novel techniques, including learning models, neural networks, artificial intelligence, dynamic systems, and fuzzy systems, in an attempt to cope with the new, different, and complex underlying phenomena. Some specific difficulties of traditional approaches have been the following: 1) The increasingly complex functionality of network components such as switches, multiplexers, and of modeling their dynamics. Moreover, both sources and network traffic is expected to become increasingly dynamic as new applications and services are developed in the future. Hence, there is a need for adaptive or even learning capabilities. 2) New services such as video-on-demand, multimedia conferences, distance learning, among others, are not well understood and available data is fragmentary. New techniques are needed to extract core behaviors and generalize when confronted with new traffic scenarios. 3) It has become increasingly important that the networks have no “down time”—hence, robustness, fault tolerance, and self-healing capabilities are prerequisites. 4) In some cases, it is required that the algorithms to manage high speed switching or transmission systems be able to make very fast decisions (on the order of micro-seconds or even less); hence, there is a need for a hardware design that incorporates a highly parallel and even analog architecture. Computational and artificial intelligence techniques are another class of information processing techniques with many adaptive, learning, inference, and universality features that can alleviate the shortcomings of conventional algorithmic approaches. Neural networks and fuzzy logic are examples Publisher Item Identifier S 0733-8716(97)00470-8.

of computational intelligence, whereas expert systems are examples of artificial intelligence. These techniques have been successfully applied in many engineering problems, spanning areas such as natural language processing (expert systems), pattern recognition (neural networks), and process control (neural networks and fuzzy logic). While these techniques are not a panacea (and it is very important to view them as supplementing proven traditional techniques), we are beginning to see them being successfully applied to high speed networks. The objective of this special issue of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS is to report some of the recent research in these areas. The 12 papers in this issue address applications in the following main areas: multimedia traffic prediction, multicast routing, capacity allocation, admission control, flow and congestion control, network design and management, wireless networks, and switching. The first paper by Yuang et al. shows how a back-propagation neural network (BPNN) can be used to provide intravideo synchronization by predicting some traffic characteristics over a fixed time period. The authors show that the playout quality can be improved using another neural network which determines an optimal window that can achieve maximum playout quality. The next paper, by Gelenbe et al. proposes a random neural network architecture to solve the multicast problem. The authors prove that the random neural network approach improves the quality of the Steiner trees compared to other heuristics, i.e., the minimum spanning tree, or the average distance one. The next two papers address capacity allocation problems. The paper by Farag´o et al. formulates the problem of optimizing the dimensions of virtual subnetworks over a physical ATM network as a convex programing one, which is solved using neural networks. This approach is also capable of solving convex programming tasks with time-varying penalty functions. In the second paper, Campbell et al. describe how neural network controllers can be used to allocate capacities in SDH networks. They examine both a feedforward–heuristic approach and feedforward–recurrent combination and compare them to a linear programming optimizer as a benchmark. They find that the neural network approach can deliver very high throughput and, in contrast to algorithmic approaches, can provide solutions in real time. Two papers address call admission control in ATM networks. In the first paper, Uehara and Hirota propose a fuzzy logic admission controller. They apply fuzzy inference to estimate the possibility distribution of the cell loss rate, which is then a basis for admission control decisions. They show

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 15, NO. 2, FEBRUARY 1997

that their approach avoids estimating excessively large values of the cell loss rate, which subsequently achieves a high multiplexing gain. Another approach to admission control using neural networks is proposed by Youssef et al.; it is based upon a utility BPNN that computes the required bandwidth for each call from real-time measurements of the traffic via its count process. They have proved that their approach is more accurate than other conventional ones and that it achieves better statistical multiplexing gain. Two papers deal with the problem of flow and congestion control. The first one, by Liu and Douligeris, shows how a BPNN can be used to regulate a sources’ traffic based upon appropriate congestion criteria. They prove that their approach achieves better performance, in terms of cell loss rate and transmission delays, than static feedback control algorithms. The second paper, by Pitsillides et al., proposes a fuzzy logic rate-based controller for the available bit rate traffic. They compare their approach to the enhanced proportional rate control algorithm and prove that it provides better throughput and lower latency. Network design using a neural networks’ knowledge acquisition approach is presented in the paper by Fahmy et al. They use neural networks and machine learning to enhance the learning capabilities of an expert system network design tool and show that this approach improves the performance of the system. Applications in wireless networks are provided in two papers. First, Smith and Palaniswami propose two neural network approaches to solve the problem of static channel assignment which has been formulated as a generalized quadratic assignment problem. One neural network is an improved

Hopfield neural network, whereas the second is a modified approach of the Kohonen self-organization network. They show that the adaptive features of their approach can be used to solve dynamic assignment problems as well. The second paper, by Wang and Ansari, shows that under a TDMA protocol, the problem of scheduling collision-free transmissions is NPcomplete, and propose a mean field annealing algorithm to solve it. Comparisons with other scheduling algorithms prove that this approach can find near-optimal solutions. Finally, Park and Lee propose a neural network controller for cell scheduling in order to enhance the throughput of a nonblocking switch with input queueing. They show that this approach provides less cell less rate and buffer sizes compared to sequential input window algorithms or output queueing. The Guest Editors hope that this issue will convey to the reader the potential benefits of applying novel techniques to high speed networks and will motivate researchers and engineers to use the broadest set of tools available. The Editors acknowledge W. Tranter, Editor-in-Chief of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, for his continuous support and guidance of this issue. We are also grateful for S. McDonald and the Editorial Staff of the JOURNAL. IBRAHIM W. HABIB, Guest Editor ROBERT J. T. MORRIS, Guest Editor HIROSHI SAITO, Guest Editor BJORN PEHRSON, Guest Editor W. TRANTER, JSAC Board Representative

Ibrahim W. Habib (S’84–M’88–SM’94) received the B.Sc. degree from Ain Shams University, Cairo, Egypt, in 1981, the M.Sc. degree from the Polytechnic University of New York, in 1984, and the Ph.D. degree from the City University of New York, in 1991, all in electrical engineering. From 1981 to 1983, and from 1984 to 1988, he was a Senior Computer Networks Engineer working on several IBM–SNA networking projects in Egypt and Saudi Arabia. Since 1991, he has been with the Electrical Engineering Department of the City College of New York, where he is currently an Assistant Professor. His research interests are in the areas of flow and congestion control of ATM networks, modeling and simulation of multimedia systems, wireless ATM networks, and the applications of neural networks and fuzzy logic in high speed networks; he has published extensively in those areas. He a Technical Editor of the IEEE COMMUNICATIONS MAGAZINE. He has served as a Guest Editor of the IEEE COMMUNICATIONS MAGAZINE October 1995 issue on “Neurocomputing in High Speed Networks,” and is currently a Guest Editor of the “Bandwidth Allocation and Control in High Speed Networks” issue of the IEEE COMMUNICATIONS MAGAZINE, to appear in May 1997. He has organized and chaired several technical sessions in different IEEE conferences. Dr. Habib had served as a member of the technical program committee of ICC’96 and INFOCOM’94 conferences.

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Robert J. T. Morris (S’77–M’78) received the BScBE degree in electrical engineering and computer science from the University of New South Wales, Australia, and the Ph.D. degree in computer science from the University of California at Los Angeles. From 1991 to 1996, he managed the Storage Systems and Software and Digital Libraries of the IBM Almaden Research Center. Prior to that, he worked on a variety of network and computer system technologies at AT&T Bell Laboratories. He is currently the Director of Personal Systems and Advanced Systems Technology at the IBM T. J. Watson Research Center, Yorktown Heights, NY.

Hiroshi Saito (M’88–SM’95) received the B.E. degree in mathematical engineering, the M.E. degree in control engineering, and the Dr. Eng. degree in teletraffic engineering, all from the University of Tokyo, Japan, in 1981, 1983, and 1992, respectively. He joined the NTT Multimedia Networks Laboratories, in 1983, where he is currently working on teletraffic issues in ATM networks as a Dinstinguished Technical Member. Dr. Saito received the Young Engineer Award of the Institute of Electronics, Information and Communication Engineers (IEICE) in 1990 and the Telecommunication Advancement Institute Award in 1995. He is a member of the IEICE and the Operations Research Society of Japan

Bjorn Pehrson, photograph and biography not available at the time of publication.