Experiments with Honeybee Foraging Inspired Load Balancing

This paper investigates and analyses the dynamics engendered by the engineering of self-organisation in a global Service Oriented Architecture. The effects are assessed via a resource allocation algorithm for load balancing, based on the observed behaviour of foraging honeybees. It is implemented at the application layer of a simulated server farm type system and its impact is investigated across this layer and the resource layer through the analyses of the digital ecosystems arising and the associated partitioning into task specific communities of server functions. The specific topological self-organisation of scale-free connectivity is shown to ensue in a remote layer from the initially artificially engineered honeybee foraging behaviour for self-organisation.

[1]  Franco Zambonelli,et al.  Case studies for self-organization in computer science , 2006, J. Syst. Archit..

[2]  Weixiong Zhang,et al.  Identifying network communities with a high resolution. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  U. Netlogo Wilensky,et al.  Center for Connected Learning and Computer-Based Modeling , 1999 .

[4]  Roberto Baldoni,et al.  Content-Based Publish-Subscribe over Structured Overlay Networks , 2005, 25th IEEE International Conference on Distributed Computing Systems (ICDCS'05).

[5]  Fernando Paganini,et al.  Robustness and the Internet : Theoretical Foundations , 2002 .

[6]  A. Taleb-Bendiab,et al.  A Formal Approach to the Engineering of Emergence and its Recurrence , 2007 .

[7]  Rainer Ruggaber.  Internet of Services SAP Research Vision , 2007, 16th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2007).

[8]  T. Seeley,et al.  Collective decision-making in honey bees: how colonies choose among nectar sources , 1991, Behavioral Ecology and Sociobiology.

[9]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[10]  C. Lee Giles,et al.  Self-Organization and Identification of Web Communities , 2002, Computer.

[11]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[12]  S.,et al.  An Efficient Heuristic Procedure for Partitioning Graphs , 2022 .

[13]  Richard Murch,et al.  Autonomic Computing , 2004 .

[14]  John G. Gibbons.  Knowledge in Action , 2001 .

[15]  Walter Willinger,et al.  Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications , 2005, Internet Math..

[16]  Christoph Schroth,et al.  Web 2.0 and SOA: Converging Concepts Enabling the Internet of Services , 2007, IT Professional.

[17]  Craig A. Tovey,et al.  On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers , 2004, Adapt. Behav..

[18]  Nazim Agoulmine,et al.  Beyond the Knowledge Plane: An Inference Plane to Support the Next Generation Internet , 2007, 2007 First International Global Information Infrastructure Symposium.

[19]  John Scott.  Social Network Analysis , 1988 .

[20]  Jean M. Carlson,et al.  Robustness and Fragility in Immunosenescence , 2006, PLoS Comput. Biol..

[21]  Alex Pothen,et al.  PARTITIONING SPARSE MATRICES WITH EIGENVECTORS OF GRAPHS* , 1990 .

[22]  Raymond Reiter,et al.  Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems , 2001 .

[23]  Eric A. Brewer,et al.  Lessons from Giant-Scale Services , 2001, IEEE Internet Comput..

[24]  David L. Cohn,et al.  Autonomic Computing , 2003, ISADS.

[25]  M. Gustafsson,et al.  Constructing and analyzing a large-scale gene-to-gene regulatory network Lasso-constrained inference and biological validation , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[26]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[27]  M. Newman,et al.  Robustness of community structure in networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.