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Improved Reinforcement Computing to Implement AntNet-Based Routing Using General NPs for Ubiquitous Environments

  • Hyuntae Park
  • Byung In Moon
  • Sungho Kang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4412)

Abstract

In the ubiquitous convergence era, the traffic managements and quality of services will be made much of a role. Because traditional routing mechanisms are lacking scalability and adaptability, a kind of adaptive routing algorithm called AntNet has attracted the attention. AntNet is an adaptive agent-based routing algorithm that imitates the activities of the social insect. In AntNet, there are implementation constraints due to complex arithmetic calculations for determining a reinforcement value. Besides, a housekeeping core in network processors will be overwhelmed by increasing routing workload for a processing of agents. In this paper, we propose a new reinforcement computing algorithm to overcome these problems. This can be implemented efficiently on packet forwarding engines of conventional network processors. The simulation results show that the proposed AntNet is more adaptive and effective in the performance of the implementation than the original AntNet.

Keywords

Source Node Destination Node Original Algorithm Good Path Subtractive Term 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Hyuntae Park
    • 1
  • Byung In Moon
    • 2
  • Sungho Kang
    • 1
  1. 1.Department of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemoon-Gu, Seoul, 120-749Korea
  2. 2.School of Electrical Engineering & Computer Science, Kyungpook National University, 1370 Sankyuk-dong, Buk-gu, Daegu, 702-701Korea

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