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."

Unknown pattern extraction for statistical network protocol identification

The past decade has seen a lot of research on statistics-based network protocol identification using machine learning techniques. Prior studies have shown promising results in terms of high accuracy and fast classification speed. However, most works have embodied an implicit assumption that all protocols are known in advance and presented in the training data, which is unrealistic since real-world networks constantly witness emerging traffic patterns as well as unknown protocols in the wild. In this paper, we revisit the problem by proposing a learning scheme with unknown pattern extraction for statistical protocol identification. The scheme is designed with a more realistic setting, where the training dataset contains labeled samples from a limited number of protocols, and the goal is to tell these known protocols apart from each other and from potential unknown ones. Preliminary results derived from real-world traffic are presented to show the effectiveness of the scheme.

A general collaborative framework for modeling and perceiving distributed network behavior

Collaborative Anomaly Detection (CAD) is an emerging field of network security in both academia and industry. It has attracted a lot of attention, due to the limitations of traditional fortress-style defense modes. Even though a number of pioneer studies have been conducted in this area, few of them concern about the universality issue. This work focuses on two aspects of it. First, a unified collaborative detection framework is developed based on network virtualization technology. Its purpose is to provide a generic approach that can be applied to designing specific schemes for various application scenarios and objectives. Second, a general behavior perception model is proposed for the unified framework based on hidden Markov random field. Spatial Markovianity is introduced to model the spatial context of distributed network behavior and stochastic interaction among interconnected nodes. Algorithms are derived for parameter estimation, forward prediction, backward smooth, and the normality evaluation of both global network situation and local behavior. Numerical experiments using extensive simulations and several real datasets are presented to validate the proposed solution. Performance-related issues and comparison with related works are discussed.

GeoConnect : geographic and connectivity-aware routing protocol for wireless sensor network

Sensor nodes are closely tied with their geographic location and their connectivity. In recent years many routing protocols have been developed to provide efficient strategy. But most of them are either focus on the geographic proximity or on connectivity. However in sparse network, Geographic routing would fail at local dead ends where a node has no neighbour closer to destination. In contrast, connectivity-based routing may result in non-optimal path and overhead management. In this paper we designed a scalable and distributed routing protocol, GeoConnect, which considers geographic proximity and connectivity for choosing next hop. In GeoConnecl, we construct a new naming system that integrates geographic and connectivity information into a node identification. We use dissimilarity function to compute the dissimilarity and apply a distributed routing algorithm to route packets. The experimental results show that GeoConnect routing provides robust and better performance than sole geographic routing or connectivity routing.

An approach to filtering duplicate RFID data streams

In a system where distributed network of Radio Frequency Identification (RFID) readers are used to collaboratively collect data from tagged objects, a scheme that detects and eliminates redundant data streams is required. To address this problem, we propose an approach that is based on Bloom filter to detect duplicate readings and filter redundant RFID data streams. We have evaluated the performance of the proposed approach and compared it with existing approaches. The experimental results demonstrate that the proposed approach provides superior performance as compared to the baseline approaches.

Mark-aided distributed filtering by using neural network for DDoS defense

Currently Distributed Denial of Service (DDoS) attacks have been identified as one of the most serious problems on the Internet. The aim of DDoS attacks is to prevent legitimate users from accessing desired resources, such as network bandwidth. Hence the immediate task of DDoS defense is to provide as much resources as possible to legitimate users when there is an attack. Unfortunately most current defense approaches can not efficiently detect and filter out attack traffic. Our approach is to find the network anomalies by using neural network, deploy the system at distributed routers, identify the attack packets, and then filter them. The marks in the IP header that are generated by a group of IP traceback schemes, Deterministic Packet Marking (DPM)/Flexible Deterministic Packet Marking (FDPM), assist this process of identifying attack packets. The experimental results show that this approach can be used to defend against both intensive and subtle DDoS attacks, and can catch DDoS attacks’ characteristic of starting from multiple sources to a single victim. According to results, we find the marks in IP headers can enhance the sensitivity and accuracy of detection, thus improve the legitimate traffic throughput and reduce attack traffic throughput. Therefore, it can perform well in filtering DDoS attack traffic precisely and effectively.

A parallel download protocol for internet-based distributed systems

Parallel transfer of large amounts of data is attractive as (i) the data can be received in less time and (ii) is more resilient to connection failures. This is often used in e-Science under the field of data sharing. Data from one research project should be easily assessable to others regardless of the size of the data thus parallelism is used to help share the data quickly. However, there are many tasks to be carried out to make it possible: replica discovery and load detection just to name two. A common problem with most solutions is they overlook some tasks such as load detection. PHTTP is an innovate protocol which handles the tasks behind parallel transfer while presenting itself as a normal sequential transfer.

Distributed protocol for communications among underwater vehicles

Underwater surveying by swarms of autonomous underwater vehicles presents problems in communication among the robots. These problems involve the bandwidth, power consumption, timing, processing power, and other issues. This paper presents a novel approach to communicate and coordinate effectively among underwater vehicles to accomplish this task successfully. The proposed approach solves issues by reducing the number of hops to conserve power, while reducing computation time and bandwidth, effectively utilizing resources to reduce the load on each node. Finally, the simulation results are presented, in order to prove that the proposed approach improves efficiency and effectiveness in communicating among underwater vehicles.

Network management in distributed systems

Applications of localized surface plasmon resonance (LSPR) such as surface enhanced Raman scattering (SERS) devices, biosensors, and nano-optics are growing. Investigating and understanding of the parameters that affect the LSPR spectrum is important for the design and fabrication of LSPR devices. This paper studies different parameters, including geometrical structures and light attributes, which affect the LSPR spectrum properties such as plasmon wavelength and enhancement factor. The paper also proposes a number of rules that should be considered in the design and fabrication of LSPR devices.

Distributed audio network for speech enhancement in challenging noise backgrounds

This paper presents a new approach to enhance speech based on a distributed microphone network. Each microphone is used to simultaneously classify the input into either one of the noise types or as speech. For enhancing the speech signal a modified spectral subtraction approach is used that utilise the sound information of the entire network to update the noise model even during speech. This improves the reduction of the ambient noise, especially for non-stationary noise types such as street or beach noise. Experiments demonstrate the effectiveness of the proposed system.

Effect of speculative prefetching on network load in distributed systems

Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper on the other hand investigates the performance of speculative prefetching. When prefetching is performed speculatively, there is bound to be an increase in the network load. Furthermore, the prefetched items must compete for space with existing cache occupants. These two factors-increased load and eviction of potentially useful cache entries-are considered in the analysis. We obtain the following conclusion: to maximise the improvement in access time, prefetch exclusively all items with access probabilities exceeding a certain threshold.

CodePipe : an opportunistic feeding and routing protocol for reliable multicast with pipelined network coding

Multicast is an important mechanism in modern wireless networks and has attracted significant efforts to improve its performance with different metrics including throughput, delay, energy efficiency, etc. Traditionally, an ideal loss-free channel model is widely used to facilitate routing protocol design. However, the quality of wireless links would be affected or even jeopardized by many factors like collisions, fading or the noise of environment. In this paper, we propose a reliable multicast protocol, called CodePipe, with advanced performance in terms of energy-efficiency, throughput and fairness in lossy wireless networks. Built upon opportunistic routing and random linear network coding, CodePipe not only simplifies transmission coordination between nodes, but also improves the multicast throughput significantly by exploiting both intra-batch and inter-batch coding opportunities. In particular, four key techniques, namely, LP-based opportunistic routing structure, opportunistic feeding, fast batch moving and inter-batch coding, are proposed to offer substantial improvement in throughput, energy-efficiency and fairness. We evaluate CodePipe on ns2 simulator by comparing with other two state-of-art multicast protocols, MORE and Pacifier. Simulation results show that CodePipe significantly outperforms both of them.

Network and traffic engineering in emerging distributed computing applications

This book focuses on network management and traffic engineering for Internet and distributed computing technologies, as well as present emerging technology trends and advanced platform