Emergence: a paradigm for robust and scalable distributed applications

Natural distributed systems are adaptive, scalable and fault-tolerant. Emergence science describes how higher-level self-regulatory behaviour arises in natural systems from many participants following simple rulesets. Emergence advocates simple communication models, autonomy and independence, enhancing robustness and self-stabilization. High-quality distributed applications such as autonomic systems must satisfy the appropriate nonfunctional requirements which include scalability, efficiency, robustness, low-latency and stability. However the traditional design of distributed applications, especially in terms of the communication strategies employed, can introduce compromises between these characteristics. This paper discusses ways in which emergence science can be applied to distributed computing, avoiding some of the compromises associated with traditionally-designed applications. To demonstrate the effectiveness of this paradigm, an emergent election algorithm is described and its performance evaluated. The design incorporates nondeterministic behaviour. The resulting algorithm has very low communication complexity, and is simultaneously very stable, scalable and robust.

Engineering emergence for cluster configuration

Distributed applications are being deployed on ever-increasing scale and with ever-increasing functionality. Due to the accompanying increase in behavioural complexity, self-management abilities, such as self-healing, have become core requirements. A key challenge is the smooth embedding of such functionality into our systems. Natural distributed systems such as ant colonies have evolved highly efficient behaviour. These emergent systems achieve high scalability through the use of low complexity communication strategies and are highly robust through large-scale replication of simple, anonymous entities. Ways to engineer this fundamentally non-deterministic behaviour for use in distributed applications are being explored. An emergent, dynamic, cluster management scheme, which forms part of a hierarchical resource management architecture, is presented. Natural biological systems, which embed self-healing behaviour at several levels, have influenced the architecture. The resulting system is a simple, lightweight and highly robust platform on which cluster-based autonomic applications can be deployed.

Scalable and efficient graph colouring in 3 dimensions using emergence engineering principles

This paper describes ways in which emergence engineering principles can be applied to the development of distributed applications. A distributed solution to the graph-colouring problem is used as a vehicle to illustrate some novel techniques. Each node acts autonomously to colour itself based only on its local view of its neighbourhood, and following a simple set of carefully tuned rules. Randomness breaks symmetry and thus enhances stability. The algorithm has been developed to enable self-configuration in wireless sensor networks, and to reflect real-world configurations the algorithm operates with 3 dimensional topologies (reflecting the propagation of radio waves and the placement of sensors in buildings, bridge structures etc.). The algorithm’s performance is evaluated and results presented. It is shown to be simultaneously highly stable and scalable whilst achieving low convergence times. The use of eavesdropping gives rise to low interaction complexity and high efficiency in terms of the communication overheads.

Urban labour, voice and legitimacy: economic development and the emergence of community unionism

Community unionism has emerged in the past decade as a growing strand of industrial relations research and is influencing trade union strategies for renewal. This article seeks to further develop the concept, while exploring the potential roles for unions in communities subject to projects of urban regeneration.

Journal citation among heterodox economists 1995-2007: Dynamics of community emergence

Purpose – This study aims to investigate the pattern among 17 heterodox economic journals over a prolonged period to provide evidence about the social dynamics among the group of researchers who publish in them and the extent to which they hold or develop a collective identity as heterodox economists. Design/methodology/approach – Traditional approaches to citation analysis are extended by the use of techniques from social network analysis. In addition to citation counts, measures of network position and clique membership are used to identify key journals and turning points in a longitudinal analysis. Findings – Important shifts in the nature of citation within the network of journals are identified in the 1998-2001 period and evidence is found of the emergence of a collective identity. Research limitations/implications – The methods prove a valuable extension of citation analysis and also focus greater consideration on the social relationships that citations represent. They are well suited to addressing the principal limitation of the study, its restriction to journals within the defined community rather than journals in general. Originality/value – This extends traditional approaches to citation analysis, provides an important new technique in identifying emergent collective identities and provides insight into the history and nature of the heterodox economic community.