Massive data delivery in unstructured peer-to-peer networks with network coding

With more and more multimedia applications on the Internet, such as IPTV, bandwidth becomes a vital bottleneck for the booming of large scale Internet based multimedia applications. Network coding is recently proposed to take advantage to use network bandwidth efficiently. In this paper, we focus on massive multimedia data, e.g. IPTV programs, transportation in peer-to-peer networks with network coding. By through study of networking coding, we pointed out that the prerequisites of bandwidth saving of network coding are: 1) one information source with a number of concurrent receivers, or 2) information pieces cached at intermediate nodes. We further proof that network coding can not gain bandwidth saving at immediate connections to a receiver end; As a result, we propose a novel model for IPTV data transportation in unstructured peer-to-peer networks with network coding. Our preliminary simulations show that the proposed architecture works very well.

A performance model for analysis of heterogeneous multi-cluster systems

This paper addresses the problem of performance modeling for large-scale heterogeneous distributed systems with emphases on multi-cluster computing systems. Since the overall performance of distributed systems is often depends on the effectiveness of its communication network, the study of the interconnection networks for these systems is very important. Performance modeling is required to avoid poorly chosen components and architectures as well as discovering a serious shortfall during system testing just prior to deployment time. However, the multiplicity of components and associated complexity make performance analysis of distributed computing systems a challenging task. To this end, we present an analytical performance model for the interconnection networks of heterogeneous multi-cluster systems. The analysis is based on a parametric family of fat-trees, the m-port n-tree, and a deterministic routing algorithm, which is proposed in this paper. The model is validated through comprehensive simulation, which demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

Analytical interconnection networks model for multi-cluster computing systems

This paper addresses the problem of interconnection networks performance modeling of large-scale distributed systems with emphases on multi-cluster computing systems. The study of interconnection networks is important because the overall performance of a distributed system is often critically hinged on the effectiveness of its interconnection network. We present an analytical model that considers stochastic quantities as well as processor heterogeneity of the target system. The model is validated through comprehensive simulation, which demonstrates that the proposed model exhibits a good degree of accuracy for various system sizes and under different operating conditions.

Distributed network systems : from concepts to implementations

"This textbook covers both theoretical and practical aspects of distributed computing. It describes the client-server model for developing distributed network systems, the communication paradigms used in a distributed network system, and the principles of reliability and security in the design of distributed network systems." "This book is suitable for self-study or for use in classes. Most parts of the book have been used by the authors in their teaching of various topics including distributed systems, computer networks, and distributed database systems. This book can also serve as an invaluable guide for computing professionals in their work for the design and implementation of distributed network systems."

A rural model for delivering regional continuing nurse education by videoconference : a pilot project

Background : The South West Alliance of Rural Health (SWARH) is an alliance of 33 health care agencies scattered across South West Victoria over a total area of approximately 46 000 square kilometres. SWARH was established to develop and install information technology (IT) infrastructure, hardware and software for all acute public hospitals in the region. The Rural Intercampus Learning Environment Project (March 2003-March 2004) piloted the use of the SWARH regional area IT network to deliver a program of continuing education to Division 1 registered nurses, utilising the expertise of local health professionals. The study was funded by the Department of Human Services, Victoria.

Aims & rationale/Objectives : Establish partnerships for multi-site delivery of programs.
Develop existing educational programs and resources for video and intranet delivery (including IT training and change management).
Collaboratively deliver educational programs utilising SWARHnet to six rural sites.
Explore issues related to the use of the technology in continuing education for rural nurses and the implications for practice.

Methods : Key informant interviews, paper-based audits, and focus groups informed the development of the schedule of the program. Session participants completed a 2-page evaluation questionnaire.

Principal findings : Participants must own the process.
Videoconferencing should be considered an adjunct to traditional education programs.
Videoconferencing most suitable for short education sessions as opposed to full-day workshops.
IT problems are unpredictable and frustrating for all concerned.
IT awareness/training of staff is essential.

Implications : The project proposes a model for coordinating and delivering regional continuing education which has been shown to improve access to education programs across multiple sites. It provides a sustainable organisational framework for the program, which could be applied in continuing professional education programs of other rural health professions, such as dentistry, medicine, allied health and pharmacy.

Presentation type : Poster

Risk allocation in public-private partnership projects : an innovative model with an intelligent approach

Both the increasing private participation in public projects and the critical importance of appropriate risk allocation to the success of Public-private partnership (PPP) projects justify specific research on how to establish effective risk allocation strategies in PPP projects. Partner’s risk management capability is currently the main concern to risk allocation in PPP projects. Following the transaction cost economics, it is argued that factors such as partner’s commitment and risk management structure should be considered simultaneously in order to develop effective risk allocation strategies. Based on the holistic capability-commitment governance-driven view, this paper proposed a model for generating an optimal risk allocation strategy in PPP projects. The model is demonstrated and described. An artificial intelligent technique integrated with fuzzy logic for model testing and validation is then introduced and justified. The innovative model is expected to provide a logical and complete understanding of the risk allocation strategy selection process, and to provide stakeholders with a richer framework than previously existing ones to guide their decision-making on risk allocation strategies.

Chaos theory based detection against network mimicking DDoS attacks

DDoS attack traffic is difficult to differentiate from legitimate network traffic during transit from the attacker, or zombies, to the victim. In this paper, we use the theory of network self-similarity to differentiate DDoS flooding attack traffic from legitimate self-similar traffic in the network. We observed that DDoS traffic causes a strange attractor to develop in the pattern of network traffic. From this observation, we developed a neural network detector trained by our DDoS prediction algorithm. Our preliminary experiments and analysis indicate that our proposed chaotic model can accurately and effectively detect DDoS attack traffic. Our approach has the potential to not only detect attack traffic during transit, but to also filter it.

Multi-cluster computing interconnection network performance modeling and analysis

The overall performance of a distributed system often depends on the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems–which is the focus of this paper–is very important. In particular, we address the problem of fat-tree based interconnection networks performance modeling for multi-user heterogeneous multi-cluster computing systems. To this end, we present an analytical model and validate the model through comprehensive simulation. The results of the simulation demonstrate that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions. On the basis of the validated model, we propose an adaptive assignment function based on the existing heterogeneity of the system to minimize multi-user environment overhead.

Factors affecting SMEs' willingness to share knowledge online : a path model

This paper presents the findings of a study of SME owner-managers that examined their willingness to share information online with other members of a local business network. The main variables associated with willingness to share knowledge online were found to be willingness to share information in conventional modes and the intensity with which they used the internet for business activities. A number of other variables were found to be indirectly or unrelated to willingness to share knowledge online. A significant locality effect was also identified which suggests that the social context of the network to which the business belonged influences willingness to share knowledge online. Our work supports previous research which concludes that online knowledge sharing initiatives should enhance relationships within the business network itself as well as the technical aspects of the networking platform and the technical competence of potential users.

Beliefs, values, and action : the judgment model of cognitive distortions in sexual offenders

In this paper, we present the Judgment Model of Cognitive Distortions (JMCD), a new model of cognitive distortions that spans multiple levels of analysis and contains different types of judgments. This model proposes that cognitive distortions tend to cluster together in what we have termed Thematic Networks (TN): judgments about beliefs, values, and actions. We argue that the three sets of judgments cover all types of cognitive distortions apparent in sexual offenders including those revolving around content (i.e., asserting characteristics to people, the offender, the world) and cognitive operations (i.e., denial, minimization, rationalizations). Following a description of the JMCD, we demonstrate how it can account for the cognitive schemata identified in sexual offenders by researchers and clinicians. The paper concludes with a brief discussion of the clinical and research implications of the JMCD.

GOM : new genetic optimizing model for broadcasting tree in MANET

Data broadcasting in a mobile ad-hoc network (MANET) is the main method of information dissemination in many applications, in particular for sending critical information to all hosts. Finding an optimal broadcast tree in such networks is a challenging task due to the broadcast storm problem. The aim of this work is to propose a new genetic model using a fitness function with the primary goal of finding an optimal broadcast tree. Our new method, called Genetic Optimisation Model (GOM) alleviates the broadcast storm problem to a great extent as the experimental simulations result in efficient broadcast tree with minimal flood and minimal hops. The result of this model also shows that it has the ability to give different optimal solutions according to the nature of the network.

Applying genetic algorithm for optimizing broadcasting process in ad-hoc network

Optimizing broadcasting process in mobile ad hoc network (MANET) is considered as a main challenge due to many problems, such as Broadcast Storm problem and high complexity in finding the optimal tree resulting in an NP-hard problem. Straight forward techniques like simple flooding give rise to Broadcast Storm problem with a high probability. In this work, genetic algorithm (GA) that searches over a population that represents a distinguishable ‘structure’ is adopted innovatively to suit MANETs. The novelty of the GA technique adopted here to provide the means to tackle this MANET problem lies mainly on the proposed method of searching for a structure of a suitable spanning tree that can be optimized, in order to meet the performance indices related to the broadcasting problem. In other words, the proposed genetic model (GM) evolves with the structure of random trees (individuals) ‘genetically’ generated using rules that are devised specifically to capture MANET behaviour in order to arrive at a minimal spanning tree that satisfies certain fitness function. Also, the model has the ability to give different solutions depending on the main factors specified such as, ‘time’ (or speed) in certain situations and ‘reachability’ in certain others.

Secure connectivity model in wireless sensor networks (WSN) using first order Reed-Muller codes

In this paper, we suggest the idea of separately treating the connectivity and communication model of a Wireless Sensor Network (WSN). We then propose a novel connectivity model for a WSN using first order Reed-Muller Codes. While the model has a hierarchical structure, we have shown that it works equally well for a Distributed WSN. Though one can use any communication model, we prefer to use the communication model suggested by Ruj and Roy [1] for all computations and results in our work. Two suitable secure (symmetric) cryptosystems can then be applied for the two different models, connectivity and communication respectively. By doing so we have shown how resiliency and scalability are appreciably improved as compared to Ruj and Roy [1].

Towards a model to explain knowledge sharing in complex organizational environments

Effective knowledge sharing underpins the day-to-day work activities in knowledge-intensive organizational environments. This paper integrates key concepts from the literature towards a model to explain effective knowledge sharing in such environments. It is proposed that the effectiveness of knowledge sharing is determined by the maturity of informal and formal social networks and a shared information and knowledge-based artefact network (AN) in a particular work context. It is further proposed that facilitating mechanisms within the social and ANs, and mechanisms that link these networks, affect the overall efficiency of knowledge sharing in complex environments. Three case studies are used to illustrate the model, highlighting typical knowledge-sharing problems that result when certain model elements are absent or insufficient in a particular environment. The model is discussed in terms of diagnosing knowledge-sharing problems, organizational knowledge strategy, and the role of information and communication technology in knowledge sharing.