Experiments with a machine-centric approach to realise distributed emergent software systems

Modern distributed systems are exposed to constant changes in their operating environment, leading to high uncertainty. Self-adaptive and self-organising approaches have become a popular solution for runtime reactivity to this uncertainty. However, these approaches use predefined, expertly-crafted policies or models, constructed at design-time, to guide system (re)configuration. They are human-centric, making modelling or policy-writing difficult to scale to increasingly complex systems; and are inflexible in their ability to deal with the unexpected at runtime (e.g. conditions not captured in a policy). We argue for a machine-centric approach to this problem, in which the desired behaviour is autonomously learned and emerges at runtime from a large pool of small alternative components, as a continuous reaction to the observed behaviour of the software and the characteristics of its operating environment. We demonstrate our principles in the context of data-centre software, showing that our approach is able to autonomously coordinate a distributed infrastructure composed of emergent web servers and a load balancer. Our initial results validate our approach, showing autonomous convergence on an optimal configuration, and also highlight the open challenges in providing fully machine-led distributed emergent software systems

Emergent software systems

Contemporary software systems often have millions of lines of code that interact over complex infrastructures. The development of such systems is very challenging due to the increasing complexity of services and the high level of dynamism of current operating environments. In order to support the development and management of such systems, autonomic computing concepts have gained significant importance. The majority of autonomic computing approaches show significant levels of expert dependency in designing adaptive solutions. These approaches usually rely on human-made models and policies to support and guide software adaptation at runtime. These approaches mainly suffer from: i) a significant upfront effort demanded to create such solutions, which adds to the complexity of creating autonomous systems, and ii) unreliability given the high levels of uncertainty in current operating environments, leading the system to degraded performance and error states when subjected to unpredicted operating conditions and unexpected software interactions. Motivated by the problems and limitations of state-of-the-art autonomic computing solutions, this thesis introduces the concept of Emergent Software Systems. These systems are autonomously composed at runtime from discovered components, and are autonomously optimised based on the operating conditions, being able to build their own understanding of their environment and constituent parts. This thesis defines Emergent Software Systems, presenting the challenges of implementing such approach, and presents a fully functioning emergent systems framework that demonstrates this concept in real-world, fully functioning datacentre-based software

Government as a social machine - the implications of government as a social machine for making and implementing market-based policy

This is the second of two reports from the Government as a Social Machine project. The first report gave an overview of the evolution of electronic/digital government, and explored the concept of 21st century government as a \u27social machine\u27. This report identifies seven social machines developed by governments in Australia and New Zealand. These social machines harness digital technologies in order to deliver more effective and efficient services, develop better business practices, and enable better accountability and transparency. The report gives an overview of each social machine in context, describing the social need that is being met and the community that has developed it, and begins to unravel some of the socio-political consequences that might arise from the use of these social machines within the public policy context. These reports are not intended to be comprehensive (further educational materials are being developed as part of the ANZSOG Case Library), but they are intended to begin a conversation amongst those studying or practicing in public policy as to how governments can better understand, manage and employ these evolving social machines for better governance and social benefit

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Heterogeneity, High Performance Computing, Self-Organization and the Cloud

application; blueprints; self-management; self-organisation; resource management; supply chain; big data; PaaS; Saas; HPCaa

Heterogeneity, High Performance Computing, Self-Organization and the Cloud

application; blueprints; self-management; self-organisation; resource management; supply chain; big data; PaaS; Saas; HPCaa