This work evaluates the performance of Peer-to-Peer storage systems in structured Peer-to-Peer (P2P) networks under the impacts of a continuous process of nodes joining and leaving the network (Churn). Based on the Distributed Hash Tables (DHT), the peer-to-peer systems provide the means to store data among a large and dynamic set of participating host nodes. We consider the fact that existing solutions do not tolerate a high Churn rate or are not really scalable in terms of number of stored data blocks. The considered performance metrics include number of data blocks lost, bandwidth consumption, latencies and distance of matched lookups. We have selected Pastry, Chord and Kademlia to evaluate the e ect of inopportune connections/disconnections in Peer-to-Peer storage systems, because these selected P2P networks possess distinctive characteristics. Chord is one of the rst structured P2P networks that implements Distributed Hash Tables (DHTs). Similar to Chord, Pastry is based on a ring structure, with the identi er space forming the ring. However, Pastry uses a di erent algorithm than Chord to select the overlay neighbors of a peer. Kademlia is a more recent structured P2P network, with the XOR mechanism for improving distance calculation. DHT deployments are characterized by Churn. But if the frequency of Churn is too high, data blocks can be lost and lookup mechanism begin to incur delays. In architectures that employ DHTs, the choice of algorithm for data replication and maintenance can have a signi cant impact on the performance and reliability. PAST is a persistent Peer-to-Peer storage utility, which replicates complete les on multiple nodes, and uses Pastry for message routing and content location. The hypothesis is that by enhancing the Churn tolerance through building a really e cient replication and maintenance mechanisms, it will: i) Operate better than a peer-to-peer storage system such as PAST especially in replica placement strategy with a fewer data transfers. ii) Resolve le lookups with a match that is closer to the source peer, thus con- serving bandwidth. Our research will involve a series of simulation studies using two network simulators OverSim and OMNeT++. The main results are: Our approach achieves a higher data availability in presence of Churn, than the original PAST replication strategy; For a Churn occuring every minute our strategy loses two times less blocks than PAST; Our replication strategy induces an average of twice less block transfers than PAST.

by Wong Po Choi. / Bibliography: leaves 47-50 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1985

by Cheung Kam Wah. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 62-63). / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- ISDN Architecture --- p.4 / Chapter 2.1 --- ISDN Definition --- p.4 / Chapter 2.2 --- Channel Structure --- p.6 / Chapter 2.3 --- Wiring Configuration of ISDN User-Network Interface --- p.6 / Chapter 2.4 --- ISDN Signalling Protocol at S/T Interface --- p.7 / Chapter Chapter 3. --- ISDN Signalling Protocol Enhancement --- p.8 / Chapter 3.1 --- ISDN Basic Rate Access Configuration --- p.8 / Chapter 3.2 --- Protocol Enhancement --- p.10 / Chapter 3.3 --- ISDN Layer 1 Signalling Protocol Enhancement --- p.11 / Chapter 3.4 --- ISDN Layer 2 Signalling Protocol Enhancement --- p.13 / Chapter 3.5 --- ISDN Layer 3 Signalling Protocol Enhancement --- p.15 / Chapter Chapter 4. --- Basic Call Control Procedure for Special Terminal Equipment --- p.18 / Chapter 4.1 --- Signalling Procedure for Call establishment and Termination --- p.18 / Chapter 4.2 --- Requirements on the Developed System --- p.27 / Chapter Chapter 5. --- Software Simulation --- p.29 / Chapter Chapter 6. --- System Performance Analysis --- p.33 / Chapter 6.1 --- Simple Measurement on the Execution Time of ISDN Call --- p.33 / Chapter 6.2 --- Call Handling Capacity Analysis --- p.36 / Chapter 6.3 --- Call Request Delay Analysis --- p.42 / Chapter Chapter 7. --- Conclusion --- p.59 / References --- p.62 / Appendix I: Software Simulation Program Flowchart --- p.64 / Appendix II: Software Simulation Program in Borland C++ Language --- p.86

An interactive visualization of self-organizing radio networks is developed. When the size and complexity of today’s radio networks grows, the need of automated network organizing methods increase to cut down on work, money and mistakes. The automation, however, leads the network operators to lose control over their own network and possible trust issues come along. Instead of giving back control to the operators, which would increase costs and work, Ericsson has suggested creating a visualization making clear that their self-organizing methods work as intended and letting the operator to efficiently explore their own network data. In this thesis project a visualization application is developed allowing the network operator to explore the settings and performance of their network organized by Ericsson’s automatic algorithm called Automatic Neighbor Relations (ANR). The user can interact with the visualization by picking, filtering, and more, to find potential patterns in the data, find bad data values, and see how settings affect the performance of the network. The visualization is built around a map where parameter and performance data is presented. Other visualization components come from the visualization framework GeoAnalytics Visualization (GAV), developed at Linköpings universitet, which also stands as a basis for the entire visualization.

This dissertation attempts to answer some of the vital questions involved in the genetic regulatory networks: inference, optimization and robustness of the mathe- matical models. Network inference constitutes one of the central goals of genomic signal processing. When inferring rule-based Boolean models of genetic regulations, the same values of predictor genes can correspond to di®erent values of the target gene because of inconsistencies in the data set. To resolve this issue, a consistency-based inference method is developed to model a probabilistic genetic regulatory network, which consists of a family of Boolean networks, each governed by a set of regulatory functions. The existence of alternative function outputs can be interpreted as the result of random switches between the constituent networks. This model focuses on the global behavior of genetic networks and re°ects the biological determinism and stochasticity. When inferring a network from microarray data, it is often the case that the sample size is not su±ciently large to infer the network fully, such that it is neces- sary to perform model selection through an optimization procedure. To this end, the network connectivity and the physical realization of the regulatory rules should be taken into consideration. Two algorithms are developed for the purpose. One algo- rithm ¯nds the minimal realization of the network constrained by the connectivity, and the other algorithm is mathematically proven to provide the minimally connected network constrained by the minimal realization. Genetic regulatory networks are subject to modeling uncertainties and perturba- tions, which brings the issue of robustness. From the perspective of network stability, robustness is desirable; however, from the perspective of intervention to exert in- °uence on network behavior, it is undesirable. A theory is developed to study the impact of function perturbations in Boolean networks: It ¯nds the exact number of a®ected state transitions and attractors, and predicts the new state transitions and robust/fragile attractors given a speci¯c perturbation. Based on the theory, one algorithm is proposed to structurally alter the network to achieve a more favorable steady-state distribution, and the other is designed to identify function perturbations that have caused changes in the network behavior, respectively.

This dissertation focuses on a comparative study of small-world characteristics in geographical, epidemic, and virtual spaces. Small-world network is the major component of the “new science of networks” that emerged recently in research related to complex networks. It has shown a great potential to model the complex networks encountered in geographical studies. This dissertation, in an attempt to understand the emergence of small-world phenomenon in spatial networks, has investigated the smallworld properties in aforementioned three spaces. Specifically, this dissertation has studied roadway transportation networks at national, metropolitan, and intra-city scales via network autocorrelation methods to investigate the distance effect on the emergence of small-world properties. This dissertation also investigated the effect of small-world network properties on the epidemic diffusion and different control strategies through agent-based simulation on social networks. The ASLevel Internet in the contiguous U.S. has been studied in its relation between local and global connections, and its correspondence with small-world characteristics. Through theoretical simulations and empirical studies on spatial networks, this dissertation has contributed to network science with a new method – network autocorrelation, and better understanding from the perspective of the relation between local and global connections and the distance effect in networks. A small-world phenomenon results from the interplay between the dynamics occurring on networks and the structure of networks; when the influencing distance of the dynamics reaches to the threshold of the network, the network will logically emerge as a small-world network. With the aid of numerical simulation a small-world network has a large number of local connections and a small number of global links. It is also found that the epidemics will take shorter time period to reach largest size on a small-world network and only particular control strategy, such as targeted control strategy, will be effective on smallworld networks. This dissertation bridges the gap between new science of networks and the network study in geography. It potentially contributes to GIScience with new modeling strategy for representing, analyzing, and modeling complexity in hazards prevention, landscape ecology, and sustainability science from a network-centric perspective.

<p>An interactive visualization of self-organizing radio networks is developed. When the size and complexity of today’s radio networks grows, the need of automated network organizing methods increase to cut down on work, money and mistakes. The automation, however, leads the network operators to lose control over their own network and possible trust issues come along. Instead of giving back control to the operators, which would increase costs and work, Ericsson has suggested creating a visualization making clear that their self-organizing methods work as intended and letting the operator to efficiently explore their own network data.</p><p>In this thesis project a visualization application is developed allowing the network operator to explore the settings and performance of their network organized by Ericsson’s automatic algorithm called Automatic Neighbor Relations (ANR). The user can interact with the visualization by picking, filtering, and more, to find potential patterns in the data, find bad data values, and see how settings affect the performance of the network.</p><p>The visualization is built around a map where parameter and performance data is presented. Other visualization components come from the visualization framework GeoAnalytics Visualization (GAV), developed at Linköpings universitet, which also stands as a basis for the entire visualization.</p>

Includes bibliographical references (p. 45-47).

Thesis (M. Sc. (Computer Science))--Rhodes University, 2003.

Thesis (M.Sc.)--York University, 2006. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves81-86). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://proquest.umi.com/pqdweb?index=4&did=1240708341&SrchMode=1&sid=2&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1194977884&clientId=5220