An Advisor Concept for Distributed Self-organizing Systems Acting in Highly Connected Environments

We present an extension to the concept of an advisor for distributed self-organizing systems that allows to improve the efficiency of such systems that act in environments where the actions of a system component have an influence on the whole environment or large parts of it. An advisor periodically reviews the history of the system and identifies recurring tasks that the system did not perform well. For these tasks it then computes exception rules for the agents that indicate how to perform these tasks better. These rules are communicated to the agents when communication is possible and are from then on used if they are triggered. To deal with highly connected environments so-called group exceptions are needed that consist of individual rules for several agents and that require the agents to signal to the other agents that in their local view the group rules should be triggered. We instantiated this group advisor concept to a self-organizing system for controlling water distribution networks that is based on digital infochemical coordination. In our experiments, using the group advisor resulted in improvements in energy use between 1 and 21 percent, with an average savings of $ 5,000 per day for randomly generated water demand behaviors for the real network of a small North-American city.

[1]  Bernhard Bauer,et al.  Pro-active Advice to Improve the Efficiency of Self-Organizing Emergent Systems , 2011, 2011 Eighth IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems.

[2]  Franco Zambonelli,et al.  Field-based coordination for pervasive multiagent systems , 2010, Springer series on agent technology.

[3]  Bernhard Bauer,et al.  Efficiency Testing of Self-adapting Systems by Learning of Event Sequences , 2010 .

[4]  Bernhard Bauer,et al.  Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems , 2011, 2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems.

[5]  Jakobus E. van Zyl,et al.  Operational Optimization of Water Distribution Systems using a Hybrid Genetic Algorithm , 2004 .

[6]  Hartmut Schmeck,et al.  Organic Computing – Addressing Complexity by Controlled Self-Organization , 2006, Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (isola 2006).

[7]  Kevin E Lansey,et al.  Determining pump operations using particle swarm optimization , 2000 .

[8]  Sven A. Brueckner,et al.  RETURN FROM THE ANT SYNTHETIC ECOSYSTEMS FOR MANUFACTURING CONTROL , 2000 .

[9]  H. Golshan,et al.  Fuel Optimization Using Biologically-Inspired Computational Models , 2008 .

[10]  Paul Cooper,et al.  Performance of Centrifugal Pumps , 2008 .

[11]  M. López-Ibáñez,et al.  Ant Colony Optimization for Optimal Control of Pumps in Water Distribution Networks , 2008 .

[12]  Bernhard Bauer,et al.  Design Pattern for Self-Organizing Emergent Systems Based on Digital Infochemicals , 2009, 2009 Sixth IEEE Conference and Workshops on Engineering of Autonomic and Autonomous Systems.

[13]  Bernhard Bauer,et al.  Improving the Efficiency of Self-Organizing Emergent Systems by an Advisor , 2010, 2010 Seventh IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems.

[14]  Bernhard Bauer,et al.  Decentralized Real-Time Control of Water Distribution Networks Using Self-Organizing Multi-agent Systems , 2010, 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[15]  Nagarajan Kandasamy,et al.  A Control-Based Approach to Autonomic Performance Management in Computing Systems , 2006 .