A switchable tree structure as an interconnection network.

by Siu Man Tsang.Thesis (M.Ph.)--Chinese University of Hong Kong, 1987.Bibliography: leaves 120-121

Analyzing Traffic and Multicast Switch Issues in an ATM Network.

This dissertation attempts to solve two problems related to an ATM network. First, we consider packetized voice and video sources as the incoming traffic to an ATM multiplexer and propose modeling methods for both individual and aggregated traffic sources. These methods are, then, used to analyze performance parameters such as buffer occupancy, cell loss probability, and cell delay. Results, thus obtained, for different buffer sizes and number of voice and video sources are analyzed and compared with those generated from existing techniques. Second, we study the priority handling feature for time critical services in an ATM multicast switch. For this, we propose a non-blocking copy network and priority handling algorithms. We, then, analyze the copy network using an analytical method and simulation. The analysis utilizes both priority and non-priority cells for two different output reservation schemes. The performance parameters, based on cell delay, delay jitter, and cell loss probability, are studied for different buffer sizes and fan-outs under various input traffic loads. Our results show that the proposed copy network provides a better performance for the priority cells while the performance for the non-priority cells is slightly inferior in comparison with the scenario when the network does not consider priority handling. We also study the fault-tolerant behavior of the copy network, specially for the broadcast banyan network subsection, and present a routing scheme considering the non-blocking property under a specific pattern of connection assignments. A fault tolerant characteristic can be quantified using the full access probability. The computation of the full access probability for a general network is known to be NP-hard. We, therefore, provide a new bounding technique utilizing the concept of minimal cuts to compute full access probability of the copy network. Our study for the fault-tolerant multi-stage interconnection network having either an extra stage or chaining shows that the proposed technique provides tighter bounds as compared to those given by existing approaches. We also apply our bounding method to compute full access probability of the fault-tolerant copy network

Inference in distributed multiagent reasoning systems in cooperation with artificial neural networks

This research is motivated by the need to support inference in intelligent decision support systems offered by multi-agent, distributed intelligent systems involving uncertainty. Probabilistic reasoning with graphical models, known as Bayesian networks (BN) or belief networks, has become an active field of research and practice in artificial intelligence, operations research, and statistics in the last two decades. At present, a BN is used primarily as a stand-alone system. In case of a large problem scope, the large network slows down inference process and is difficult to review or revise. When the problem itself is distributed, domain knowledge and evidence has to be centralized and unified before a single BN can be created for the problem. Alternatively, separate BNs describing related subdomains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving, even if the interdependency relations are available. This issue has been investigated in several works, including most notably Multiply Sectioned BNs (MSBNs) by Xiang [Xiang93]. MSBNs provide a highly modular and efficient framework for uncertain reasoning in multi-agent distributed systems. Inspired by the success of BNs under the centralized and single-agent paradigm, a MSBN representation formalism under the distributed and multi-agent paradigm has been developed. This framework allows the distributed representation of uncertain knowledge on a large and complex environment to be embedded in multiple cooperative agents and effective, exact, and distributed probabilistic inference. What a Bayesian network is, how inference can be done in a Bayesian network under the single-agent paradigm, how multiple agents’ diverse knowledge on a complex environment can be structured as a set of coherent probabilistic graphical models, how these models can be transformed into graphical structures that support message passing, and how message passing can be performed to accomplish tasks in model compilation and distributed inference are covered in details in this thesis

Improving Scalability and Usability of Parallel Runtime Environments for High Availability and High Performance Systems

The number of processors embedded in high performance computing platforms is growing daily to solve larger and more complex problems. Hence, parallel runtime environments have to support and adapt to the underlying platforms that require scalability and fault management in more and more dynamic environments. This dissertation aims to analyze, understand and improve the state of the art mechanisms for managing highly dynamic, large scale applications. This dissertation demonstrates that the use of new scalable and fault-tolerant topologies, combined with rerouting techniques, builds parallel runtime environments, which are able to efficiently and reliably deliver sets of information to a large number of processes. Several important graph properties are provided to illustrate the theoretical capability of these topologies in terms of both scalability and fault-tolerance, such as reasonable degree, regular graph, low diameter, symmetric graph, low cost factor, low message traffic density, optimal connectivity, low fault-diameter and strongly resilient. The dissertation builds a communication framework based on these topologies to support parallel runtime environments. Such a framework can handle multiple types of messages, e.g., unicast, multicast, broadcast and all-gather. Additionally, the communication framework has been formally verified to work in both normal and failure circumstances without creating any of the common problems such as broadcast storm, deadlock and non-progress cycle

Time constraint agents? coordination and learning in cooperative multi-agent system

Ph.DDOCTOR OF PHILOSOPH

Proceedings of the 26th International Symposium on Theoretical Aspects of Computer Science (STACS'09)

The Symposium on Theoretical Aspects of Computer Science (STACS) is held alternately in France and in Germany. The conference of February 26-28, 2009, held in Freiburg, is the 26th in this series. Previous meetings took place in Paris (1984), Saarbr¨ucken (1985), Orsay (1986), Passau (1987), Bordeaux (1988), Paderborn (1989), Rouen (1990), Hamburg (1991), Cachan (1992), W¨urzburg (1993), Caen (1994), M¨unchen (1995), Grenoble (1996), L¨ubeck (1997), Paris (1998), Trier (1999), Lille (2000), Dresden (2001), Antibes (2002), Berlin (2003), Montpellier (2004), Stuttgart (2005), Marseille (2006), Aachen (2007), and Bordeaux (2008). ..

Doctor of Philosophy

dissertationNetwork emulation has become an indispensable tool for the conduct of research in networking and distributed systems. It offers more realism than simulation and more control and repeatability than experimentation on a live network. However, emulation testbeds face a number of challenges, most prominently realism and scale. Because emulation allows the creation of arbitrary networks exhibiting a wide range of conditions, there is no guarantee that emulated topologies reflect real networks; the burden of selecting parameters to create a realistic environment is on the experimenter. While there are a number of techniques for measuring the end-to-end properties of real networks, directly importing such properties into an emulation has been a challenge. Similarly, while there exist numerous models for creating realistic network topologies, the lack of addresses on these generated topologies has been a barrier to using them in emulators. Once an experimenter obtains a suitable topology, that topology must be mapped onto the physical resources of the testbed so that it can be instantiated. A number of restrictions make this an interesting problem: testbeds typically have heterogeneous hardware, scarce resources which must be conserved, and bottlenecks that must not be overused. User requests for particular types of nodes or links must also be met. In light of these constraints, the network testbed mapping problem is NP-hard. Though the complexity of the problem increases rapidly with the size of the experimenter's topology and the size of the physical network, the runtime of the mapper must not; long mapping times can hinder the usability of the testbed. This dissertation makes three contributions towards improving realism and scale in emulation testbeds. First, it meets the need for realistic network conditions by creating Flexlab, a hybrid environment that couples an emulation testbed with a live-network testbed, inheriting strengths from each. Second, it attends to the need for realistic topologies by presenting a set of algorithms for automatically annotating generated topologies with realistic IP addresses. Third, it presents a mapper, assign, that is capable of assigning experimenters' requested topologies to testbeds' physical resources in a manner that scales well enough to handle large environments

A new feature selection and feature contrasting approach based on quality metric: application to efficient classification of complex textual data

International audienceFeature maximization is a cluster quality metric which favors clusters with maximum feature representation as regard to their associated data. This metric has already been successfully exploited, altogether, for defining unbiased clustering quality indexes, for efficient cluster labeling, as well as for substituting to distance in the clustering process, like in the IGNGF incremental clustering method. In this paper we go one step further showing that a straightforward adaptation of such metric can provide a highly efficient feature selection and feature contrasting model in the context of supervised classification. We more especially show that this technique can enhance the performance of classification methods whilst very significantly outperforming (+80%) the state-of-the art variable selection techniques in the case of the classification of unbalanced, highly multidimensional and noisy textual data, with a high degree of similarity between the classes. Our experimental dataset is a reference dataset of 7000 publications related to patents classes issued from a reference classification in the domain of pharmacology

Tree Machines: Architectures and Algorithms A Survey Paper

Recent advances in very large scale integrated (VLSI) circuit technology have lead to a surge in research aimed at finding new computer organizations that support a great deal of concurrency computer organizations based on tree structures appear well-suited to several kinds of parallel computations. In this paper we will discuss the performance of tree machines as well as Issues related to their implementation in VLSI. Examples of tree machines are presented, with an emphasis on the way the processing elements communicate in the machine. A taxonomy of tree algorithms based on a taxonomy of parallel algorithms proposed by Kung in 1979 is Introduced. Examples of tree algorithms are also given