Distributed resource allocation as co-evolution problem

Distributed self-organising systems often face conflicts if more than one entity tries to access a limited resource. In order to solve this conflict, research focuses on techniques for resource allocation considering different priorities. In this paper, we propose to tackle the decision problem of whom to assign the resource by means of a co-evolutionary approach. We investigate appropriate fitness estimations, representation schemes, and configuration of the underlying genetic operators. We demonstrate the convergence and efficiency of our approach using an exemplary system model.

[1]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[2]  Jörg Hähner,et al.  Interwoven Systems , 2014, Informatik-Spektrum.

[3]  Risto Miikkulainen,et al.  Competitive Coevolution through Evolutionary Complexification , 2011, J. Artif. Intell. Res..

[4]  Andrew S. Tanenbaum,et al.  Distributed systems - principles and paradigms, 2nd Edition , 2007 .

[5]  Tirtha Ranjeet,et al.  Coevolutionary algorithms for the optimization of strategies for red teaming applications , 2012 .

[6]  Christian Müller-Schloer,et al.  Organic computing: on the feasibility of controlled emergence , 2004, CODES+ISSS '04.

[7]  J. Krebs,et al.  Arms races between and within species , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[8]  Jörg Hähner,et al.  Engineering and Mastering Interwoven Systems , 2014, ARCS Workshops.

[9]  Jörg Hähner,et al.  SmaCCS: Smart Camera Cloud Services - Towards an Intelligent Cloud-based Surveillance System , 2013, ICINCO.

[10]  P. Flick,et al.  Evolutionäre Algorithmen , 2012 .

[11]  Jörg Hähner,et al.  Observation and Control of Organic Systems , 2011, Organic Computing.

[12]  Matthias Werner,et al.  On the Definitions of Self-Managing and Self-Organizing Systems , 2011 .

[13]  W. Daniel Hillis,et al.  Co-evolving parasites improve simulated evolution as an optimization procedure , 1990 .

[14]  Hasan Pirkul,et al.  Algorithms for the multi-resource generalized assignment problem , 1991 .

[15]  Peter Tiño,et al.  Measuring Generalization Performance in Coevolutionary Learning , 2008, IEEE Transactions on Evolutionary Computation.

[16]  Luca Faust,et al.  Modern Operating Systems , 2016 .

[17]  Bernhard Rinner,et al.  The evolution from single to pervasive smart cameras , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

[18]  Albert B. Cherns,et al.  The Principles of Sociotechnical Design , 1976 .

[19]  Edward W. Davis,et al.  A Comparison of Heuristic and Optimum Solutions in Resource-Constrained Project Scheduling , 1975 .

[20]  Jerome D. Wiest Some Properties of Schedules for Large Projects with Limited Resources , 1964 .

[21]  Stuart A. Kauffman,et al.  Escaping the Red Queen Effect , 1995 .

[22]  Richard K. Belew,et al.  Coevolutionary search among adversaries , 1997 .

[23]  Kim Mens,et al.  Co-Evolution of Object-Oriented Software Design and Implementation , 2002 .

[24]  Fernando Gustavo Tinetti,et al.  Distributed systems: principles and paradigms (2nd edition): Andrew S. Tanenbaum, Maarten Van Steen Pearson Education, Inc., 2007 ISBN: 0-13-239227-5 , 2011 .

[25]  Mark Gerstein,et al.  An integrated system for studying residue coevolution in proteins , 2008, Bioinform..

[26]  F. Brian Talbot Optimal methods for scheduling projects under resource constraints , 1979 .

[27]  Duc Truong Pham,et al.  Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks , 2011 .

[28]  Osama Moselhi,et al.  Least impact algorithm for resource allocation , 1993 .

[29]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.