Supporting ad hoc collaborations in peer-to-peer networks

Traditionally, applications and tools supporting collaborative computing have been designed only with personal computers in mind and support a limited range of computing and network platforms. These applications are therefore not well equipped to deal with network heterogeneity and, in particular, do not cope well with dynamic network topologies. Progress in this area must be made if we are to fulfil the needs of users and support the diversity, mobility, and portability that are likely to characterise group work in future. This paper describes a groupware platform called Coco that is designed to support collaboration in a heterogeneous network environment. The work demonstrates that progress in the p development of a generic supporting groupware is achievable, even in the context of heterogeneous and dynamic networks. The work demonstrates the progress made in the development of an underlying communications infrastructure, building on peer-to-peer concept and topologies to improve scalability and robustness.

A peer-to-peer approach to content dissemination and search in collaborative networks

There are three key driving forces behind the development of Internet Content Management Systems (CMS) - a desire to manage the explosion of content, a desire to provide structure and meaning to content in order to make it accessible, and a desire to work collaboratively to manipulate content in some meaningful way. Yet the traditional CMS has been unable to meet the latter of these requirements, often failing to provide sufficient tools for collaboration in a distributed context. Peer-to-Peer (P2P) systems are networks in which every node is an equal participant (whether transmitting data, exchanging content, or invoking services) and there is an absence of any centralised administrative or coordinating authorities. P2P systems are inherently more scalable than equivalent client-server implementations as they tend to use resources at the edge of the network much more effectively. This paper details the rationale and design of a P2P middleware for collaborative content management.

Virtual landmarking for locality aware peer IDs

In Peer-to-Peer (P2P) networks, it is often desirable to assign node IDs which preserve locality relationships in the underlying topology. Node locality can be embedded into node IDs by utilizing a one dimensional mapping by a Hilbert space filling curve on a vector of network distances from each node to a subset of reference landmark nodes within the network. However this approach is fundamentally limited because while robustness and accuracy might be expected to improve with the number of landmarks, the effectiveness of 1 dimensional Hilbert Curve mapping falls for the curse of dimensionality. This work proposes an approach to solve this issue using Landmark Multidimensional Scaling (LMDS) to reduce a large set of landmarks to a smaller set of virtual landmarks. This smaller set of landmarks has been postulated to represent the intrinsic dimensionality of the network space and therefore a space filling curve applied to these virtual landmarks is expected to produce a better mapping of the node ID space. The proposed approach, the Virtual Landmarks Hilbert Curve (VLHC), is particularly suitable for decentralised systems like P2P networks. In the experimental simulations the effectiveness of the methods is measured by means of the locality preservation derived from node IDs in terms of latency to nearest neighbours. A variety of realistic network topologies are simulated and this work provides strong evidence to suggest that VLHC performs better than either Hilbert Curves or LMDS use independently of each other.

Learning to lead for business growth – implications for supporting SMEs in Wales

The Welsh private and third sectors are heavily dependent on SMEs. Consequently the performance of SMEs is critical to the performance of the Welsh economy. Substantial public funds, particularly from European Structural Funds, have been allocated to support these since 2000. The majority of programmes thus funded have been led from within the Welsh Government. This paper reports interim evaluation findings from one intervention led by two Welsh higher education institutions (HEIs), namely the LEAD Wales programme. The programme is an extended intervention to support the leadership skills of owner-managers and incorporates a range of learning methods, including formal masterclasses, but emphasizes situated and experiential learning through action learning, coaching and peer-to-peer exchange exercises. The programme’s impact is assessed on the experiences of 325 participants, of whom 217 have completed the programme. The paper concludes that situated learning methods, through which participants are able to draw from shared history and experience over an extended period are critical to programme success. By contrast, short-term thematic teaching, based around more formal, hierarchical learning is less likely to yield significant and sustainable economic benefits. The implications of this for business support in Wales are discussed.

Farmer attitudes and livestock disease: exploring citizenship behaviour and peer monitoring across two BVD control schemes in the UK

The eradication of BVD in the UK is technically possible but appears to be socially untenable. The following study explored farmer attitudes to BVD control schemes in relation to advice networks and information sharing, shared aims and goals, motivation and benefits of membership, notions of BVD as a priority disease and attitudes toward regulation. Two concepts from the organisational management literature framed the study: citizenship behaviour where actions of individuals support the collective good (but are not explicitly recognised as such) and peer to peer monitoring (where individuals evaluate other’s behaviour). Farmers from two BVD control schemes in the UK participated in the study: Orkney Livestock Association BVD Eradication Scheme and Norfolk and Suffolk Cattle Breeders Association BVD Eradication Scheme. In total 162 farmers participated in the research (109 in-scheme and 53 out of scheme). The findings revealed that group helping and information sharing among scheme members was low with a positive BVD status subject to social censure. Peer monitoring in the form of gossip with regard to the animal health status of other farms was high. Interestingly, farmers across both schemes supported greater regulation with regard to animal health, largely due to the mistrust of fellow farmers following voluntary disease control measures. While group cohesiveness varied across the two schemes, without continued financial inducements, longer-term sustainability is questionable

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

Decentralized load balancing for highly irregular search problems

In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good calability and close-to linear speedup in a distributed network of workstations.

Effectiveness of landmark analysis for establishing locality in p2p networks

Locality to other nodes on a peer-to-peer overlay network can be established by means of a set of landmarks shared among the participating nodes. Each node independently collects a set of latency measures to landmark nodes, which are used as a multi-dimensional feature vector. Each peer node uses the feature vector to generate a unique scalar index which is correlated to its topological locality. A popular dimensionality reduction technique is the space filling Hilbert’s curve, as it possesses good locality preserving properties. However, there exists little comparison between Hilbert’s curve and other techniques for dimensionality reduction. This work carries out a quantitative analysis of their properties. Linear and non-linear techniques for scaling the landmark vectors to a single dimension are investigated. Hilbert’s curve, Sammon’s mapping and Principal Component Analysis have been used to generate a 1d space with locality preserving properties. This work provides empirical evidence to support the use of Hilbert’s curve in the context of locality preservation when generating peer identifiers by means of landmark vector analysis. A comparative analysis is carried out with an artificial 2d network model and with a realistic network topology model with a typical power-law distribution of node connectivity in the Internet. Nearest neighbour analysis confirms Hilbert’s curve to be very effective in both artificial and realistic network topologies. Nevertheless, the results in the realistic network model show that there is scope for improvements and better techniques to preserve locality information are required.

A hierarchical distributed approach for mining molecular fragments

Recently, two approaches have been introduced that distribute the molecular fragment mining problem. The first approach applies a master/worker topology, the second approach, a completely distributed peer-to-peer system, solves the scalability problem due to the bottleneck at the master node. However, in many real world scenarios the participating computing nodes cannot communicate directly due to administrative policies such as security restrictions. Thus, potential computing power is not accessible to accelerate the mining run. To solve this shortcoming, this work introduces a hierarchical topology of computing resources, which distributes the management over several levels and adapts to the natural structure of those multi-domain architectures. The most important aspect is the load balancing scheme, which has been designed and optimized for the hierarchical structure. The approach allows dynamic aggregation of heterogenous computing resources and is applied to wide area network scenarios.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.

A platform for supporting micro-collaborations in a diverse device environment

Collaborative software is usually thought of as providing audio-video conferencing services, application/desktop sharing, and access to large content repositories. However mobile device usage is characterized by users carrying out short and intermittent tasks sometimes referred to as 'micro-tasking'. Micro-collaborations are not well supported by traditional groupware systems and the work in this paper seeks out to address this. Mico is a system that provides a set of application level peer-to-peer services for the ad-hoc formation and facilitation of collaborative groups across a diverse mobile device domain. The system builds on the Java ME bindings of the JXTA P2P protocols, and is designed with an approach to use the lowest common denominators that are required for collaboration between varying degrees of mobile device capability. To demonstrate how our platform facilitates application development, we built an exemplary set of demonstration applications and include code examples here to illustrate the ease and speed afforded when developing collaborative software with Mico.

The internet, information architecture and community memory

This article reviews current technological developments, particularly Peer-to-Peer technologies and Distributed Data Systems, and their value to community memory projects, particularly those concerned with the preservation of the cultural, literary and administrative data of cultures which have suffered genocide or are at risk of genocide. It draws attention to the comparatively good representation online of genocide denial groups and changes in the technological strategies of holocaust denial and other far-right groups. It draws on the author's work in providing IT support for a UK-based Non-Governmental Organization providing support for survivors of genocide in Rwanda.

Economic models and algorithms for distributed systems

Distributed computing paradigms for sharing resources such as Clouds, Grids, Peer-to-Peer systems, or voluntary computing are becoming increasingly popular. While there are some success stories such as PlanetLab, OneLab, BOINC, BitTorrent, and SETI@home, a widespread use of these technologies for business applications has not yet been achieved. In a business environment, mechanisms are needed to provide incentives to potential users for participating in such networks. These mechanisms may range from simple non-monetary access rights, monetary payments to specific policies for sharing. Although a few models for a framework have been discussed (in the general area of a "Grid Economy"), none of these models has yet been realised in practice. This book attempts to fill this gap by discussing the reasons for such limited take-up and exploring incentive mechanisms for resource sharing in distributed systems. The purpose of this book is to identify research challenges in successfully using and deploying resource sharing strategies in open-source and commercial distributed systems.