Decentralised Autonomic Computing: Analysing Self-Organising Emergent Behaviour using Advanced Numerical Methods
Autonomic Computing, International Conference on (2005)
June 13, 2005 to June 16, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICAC.2005.20
Tom De Wolf , KULeuven
Giovanni Samaey , KULeuven
Tom Holvoet , KULeuven
Dirk Roose , KULeuven
When designing decentralised autonomic computing systems, a fundamental engineering issue is to assess systemwide behaviour. Such decentralised systems are characterised by the lack of global control, typically consist of autonomous cooperating entities, and often rely on self-organised emergent behaviour to achieve the requirements. A well-founded and practically feasible approach to study overall system behaviour is a prerequisite for successful deployment. On one hand, formal proofs of correct behaviour and even predictions of the exact systemwide behaviour are practically infeasible due to the complex, dynamic, and often non-deterministic nature of selforganising emergent systems. On the other hand, simple simulations give no convincing arguments for guaranteeing system-wide properties. We describe an alternative approach that allows to analyse and assess trends in systemwide behaviour, based on so-called "equation-free" macroscopic analysis. This technique yields more reliable results about the system-wide behaviour, compared to mere observation of simulation results, at an affordable computational cost. Numerical algorithms act at the system-wide level and steer the simulations. This allows to limit the amount of simulations considerably. We illustrate the approach by studying a particular system-wide property of a decentralised control system for Automated Guided Vehicles and we outline a road map towards a general methodology for studying decentralised autonomic computing systems.
D. Roose, T. Holvoet, T. De Wolf and G. Samaey, "Decentralised Autonomic Computing: Analysing Self-Organising Emergent Behaviour using Advanced Numerical Methods," Autonomic Computing, International Conference on(ICAC), Seattle, Washington, 2005, pp. 52-63.