Optimising Structured P2P Networks for Complex Queries

With network enabled consumer devices becoming increasingly popular, the number of connected devices and available services is growing considerably - with the number of connected devices es- timated to surpass 15 billion devices by 2015. In this increasingly large and dynamic environment it is important that users have a comprehensive, yet efficient, mechanism to discover services. Many existing wide-area service discovery mechanisms are centralised and do not scale to large numbers of users. Additionally, centralised services suffer from issues such as a single point of failure, high maintenance costs, and difficulty of management. As such, this Thesis seeks a Peer to Peer (P2P) approach. Distributed Hash Tables (DHTs) are well known for their high scalability, financially low barrier of entry, and ability to self manage. They can be used to provide not just a platform on which peers can offer and consume services, but also as a means for users to discover such services. Traditionally DHTs provide a distributed key-value store, with no search functionality. In recent years many P2P systems have been proposed providing support for a sub-set of complex query types, such as keyword search, range queries, and semantic search. This Thesis presents a novel algorithm for performing any type of complex query, from keyword search, to complex regular expressions, to full-text search, over any structured P2P overlay. This is achieved by efficiently broadcasting the search query, allowing each peer to process the query locally, and then efficiently routing responses back to the originating peer. Through experimentation, this technique is shown to be successful when the network is stable, however performance degrades under high levels of network churn. To address the issue of network churn, this Thesis proposes a number of enhancements which can be made to existing P2P overlays in order to improve the performance of both the existing DHT and the proposed algorithm. Through two case studies these enhancements are shown to improve not only the performance of the proposed algorithm under churn, but also the performance of traditional lookup operations in these networks

Statistical structures for internet-scale data management

Efficient query processing in traditional database management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributed systems, on the other hand, statistics management still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge. To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average-Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation of these tools for distributed statistical synopses, and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability

Parallelisation for data-intensive applications over peer-to-peer networks

In Data Intensive Computing, properties of the data that are the input for an application decide running performance in most cases. Those properties include the size of the data, the relationships inside data, and so forth. There is a class of data intensive applications (BLAST, SETI@home, Folding@Home and so on so forth) whose performances solely depend on the amount of input data. Another important characteristic of those applications is that the input data can be split into units and these units are not related to each other during the runs of the applications. This characteristic helps this class of data intensive applications to be parallelised in the way where the input data is split into units and application runs on different computer nodes for certain portion of the units. SETI@home and Folding@Home have been successfully parallelised over peer-to-peer networks. However, they suffer from the problems of single point of failure and poor scalability. In order to solve these problems, we choose BLAST as our example data intensive applications and parallelise BLAST over a fully distributed peer-to-peer network. BLAST is a popular bioinformatics toolset which can be used to compare two DNA sequences. The major usage of BLAST is searching a query of sequences inside a database for their similarities so as to identify whether they are new. When comparing single pair of sequences, BLAST is efficient. However, due to growing size of the databases, executing BLAST jobs locally produces prohibitively poor performance. Thus, methods for parallelising BLAST are sought. Traditional BLAST parallelisation approaches are all based on clusters. Clusters employ a number of computing nodes and high bandwidth interlinks between nodes. Cluster-based BLAST exhibits higher performance; nevertheless, clusters suffer from limited resources and scalability problems. Clusters are expensive, prohibitively so when the growth of the sequence database are taken into account. It involves high cost and complication when increasing the number of nodes to adapt to the growth of BLAST databases. Hence a Peer-to-Peer-based BLAST service is required. This thesis demonstrates our parallelisation of BLAST over Peer-to-Peer networks (termed ppBLAST), which utilises the free storage and computing resources in the Peer-to-Peer networks to complete BLAST jobs in parallel. In order to achieve the goal, we build three layers in ppBLAST each of which is responsible for particular functions. The bottom layer is a DHT infrastructure with the support of range queries. It provides efficient range-based lookup service and storage for BLAST tasks. The middle layer is the BitTorrent-based database distribution. The upper layer is the core of ppBLAST which schedules and dispatches task to peers. For each layer, we conduct comprehensive research and the achievements are presented in this thesis. For the DHT layer, we design and implement our DAST-DHT. We analyse balancing, maximum number of children and the accuracy of the range query. We also compare the DAST with other range query methodology and state that if the number of children is adjusted to more two, the performance of DAST overcomes others. For the BitTorrent-like database distribution layer, we investigate the relationship between the seeding strategies and the selfish leechers (freeriders and exploiters). We conclude that OSS works better than TSS in a normal situation

Wireless mobile ad-hoc sensor networks for very large scale cattle monitoring

This paper investigates the use of wireless mobile ad hoc sensor networks in the nationwide cattle monitoring systems. This problem is essential for monitoring general animal health and detecting outbreaks of animal diseases that can be a serious threat for the national cattle industry and human health. We begin by describing a number of related approaches for supporting animal monitoring applications and identify a comprehensive set of requirements that guides our approach. We then propose a novel infrastructure-less, self -organized peer to peer architecture that fulfills these requirements. The core of our work is the novel data storage and routing protocol for large scale, highly mobile ad hoc sensor networks that is based on the Distributed Hash Table (DHT) substrate that we optimize for disconnections. We show over a range of extensive simulations that by exploiting nodes’ mobility, packet overhearing and proactive caching we significantly improve availability of sensor data in these extreme conditions

Wireless mobile ad-hoc sensor networks for very large scale cattle monitoring

This paper investigates the use of wireless mobile ad hoc sensor networks in the nationwide cattle monitoring systems. This problem is essential for monitoring general animal health and detecting outbreaks of animal diseases that can be a serious threat for the national cattle industry and human health. We begin by describing a number of related approaches for supporting animal monitoring applications and identify a comprehensive set of requirements that guides our approach. We then propose a novel infrastructure-less, self -organized peer to peer architecture that fulfills these requirements. The core of our work is the novel data storage and routing protocol for large scale, highly mobile ad hoc sensor networks that is based on the Distributed Hash Table (DHT) substrate that we optimize for disconnections. We show over a range of extensive simulations that by exploiting nodes’ mobility, packet overhearing and proactive caching we significantly improve availability of sensor data in these extreme conditions

Designs and Analyses in Structured Peer-To-Peer Systems

Peer-to-Peer (P2P) computing is a recent hot topic in the areas of networking and distributed systems. Work on P2P computing was triggered by a number of ad-hoc systems that made the concept popular. Later, academic research efforts started to investigate P2P computing issues based on scientific principles. Some of that research produced a number of structured P2P systems that were collectively referred to by the term "Distributed Hash Tables" (DHTs). However, the research occurred in a diversified way leading to the appearance of similar concepts yet lacking a common perspective and not heavily analyzed. In this thesis we present a number of papers representing our research results in the area of structured P2P systems grouped as two sets labeled respectively "Designs" and "Analyses". The contribution of the first set of papers is as follows. First, we present the princi- ple of distributed k-ary search and argue that it serves as a framework for most of the recent P2P systems known as DHTs. That is, given this framework, understanding existing DHT systems is done simply by seeing how they are instances of that frame- work. We argue that by perceiving systems as instances of that framework, one can optimize some of them. We illustrate that by applying the framework to the Chord system, one of the most established DHT systems. Second, we show how the frame- work helps in the design of P2P algorithms by two examples: (a) The DKS(n; k; f) system which is a system designed from the beginning on the principles of distributed k-ary search. (b) Two broadcast algorithms that take advantage of the distributed k-ary search tree. The contribution of the second set of papers is as follows. We account for two approaches that we used to evaluate the performance of a particular class of DHTs, namely the one adopting periodic stabilization for topology maintenance. The first approach was of an intrinsic empirical nature. In this approach, we tried to perceive a DHT as a physical system and account for its properties in a size-independent manner. The second approach was of a more analytical nature. In this approach, we applied the technique of Master Equations, which is a widely used technique in the analysis of natural systems. The application of the technique lead to a highly accurate description of the behavior of structured overlays. Additionally, the thesis contains a primer on structured P2P systems that tries to capture the main ideas prevailing in the field

A Taxonomy of Self-configuring Service Discovery Systems

We analyze the fundamental concepts and issues in service discovery. This analysis places service discovery in the context of distributed systems by describing service discovery as a third generation naming system. We also describe the essential architectures and the functionalities in service discovery. We then proceed to show how service discovery fits into a system, by characterizing operational aspects. Subsequently, we describe how existing state of the art performs service discovery, in relation to the operational aspects and functionalities, and identify areas for improvement

A NOVEL LINEAR DIOPHANTINE EQUATION-BAESD LOW DIAMETER STRUCTURED PEER-TO-PEER NETWORK

This research focuses on introducing a novel concept to design a scalable, hierarchical interest-based overlay Peer-to-Peer (P2P) system. We have used Linear Diophantine Equation (LDE) as the mathematical base to realize the architecture. Note that all existing structured approaches use Distributed Hash Tables (DHT) and Secure Hash Algorithm (SHA) to realize their architectures. Use of LDE in designing P2P architecture is a completely new idea; it does not exist in the literature to the best of our knowledge. We have shown how the proposed LDE-based architecture outperforms some of the most well established existing architecture. We have proposed multiple effective data query algorithms considering different circumstances, and their time complexities are bounded by (2+ r/2) only; r is the number of distinct resources. Our alternative lookup scheme needs only constant number of overlay hops and constant number of message exchanges that can outperform DHT-based P2P systems. Moreover, in our architecture, peers are able to possess multiple distinct resources. A convincing solution to handle the problem of churn has been offered. We have shown that our presented approach performs lookup queries efficiently and consistently even in presence of churn. In addition, we have shown that our design is resilient to fault tolerance in the event of peers crashing and leaving. Furthermore, we have proposed two algorithms to response to one of the principal requests of P2P applications’ users, which is to preserve the anonymity and security of the resource requester and the responder while providing the same light-weighted data lookup