A bio‐inspired OSPF path selection scheme based on an adaptive attractor selection model

Summary Current Internet Protocol routers only support equal cost multi-path routing, which performs the random path selection or the traffic uniform distribution among equal-cost paths. In biology, an adaptive attractor selection model is presented to simulate the concentration changes of two kinds of Escherichia coli's mRNA in changing nutrition environments with bistability equations. Inspired by the metabolism behaviors of E. coli, we propose an adaptive path selection scheme Open Shortest Path First-path selection by attractor selection to dynamically select the transmission path by the real-time path quality. Here, the mRNA concentration is analogous to the path quality. Then, to reflect the multipath quality, multi-stability equations are adopted and redesigned. Our scheme consists of two main features. The first one is a redefined path-activity to indicate multipath transmission goodness, which is inversely proportional to the offset between current path quality and best path quality. And the second one is a new attractor expression of the multi-stability equations to concretely specify the effect of a stochastic item noise in the equations on the path selection. Compared with the greedy selection and the uniform random selection in file transfer protocol (FTP) service, our scheme gains better performance on reducing file transmission time, traffic throughput, and traffic dropped. Copyright © 2015 John Wiley & Sons, Ltd.

[1]  Kenji Leibnitz,et al.  Resilient Multi-path Routing Based on a Biological Attractor Selection Scheme , 2006, BioADIT.

[2]  Kenji Leibnitz,et al.  Biologically inspired self-adaptive multi-path routing in overlay networks , 2006, Commun. ACM.

[3]  Víctor Carrascal Frías,et al.  A game-theoretic multipath routing for video-streaming services over Mobile Ad Hoc Networks , 2011, Comput. Networks.

[4]  Hui Li,et al.  A layer 2 multipath solution and its performance evaluation for Data Center Ethernets , 2013, Int. J. Commun. Syst..

[5]  Sadiq M. Sait,et al.  A memory efficient stochastic evolution based algorithm for the multi-objective shortest path problem , 2014, Appl. Soft Comput..

[6]  Mark S. Leeson,et al.  Meta-heuristic algorithms for optimized network flow wavelet-based image coding , 2014, Appl. Soft Comput..

[7]  Jeffery P. Demuth,et al.  The Evolution of Mammalian Gene Families , 2006, PloS one.

[8]  Marco Chiesa,et al.  Intra-domain routing with pathlets , 2014, Comput. Commun..

[9]  Yaling Dou,et al.  Solving the fuzzy shortest path problem using multi-criteria decision method based on vague similarity measure , 2012, Appl. Soft Comput..

[10]  Layered Attractor Selection for Clustering and Data Gathering in Wireless Sensor Networks , 2010, 2010 IEEE Wireless Communication and Networking Conference.

[11]  Olivier Brun,et al.  Online OSPF weights optimization in IP networks , 2014, Comput. Networks.

[12]  K. Kaneko,et al.  Adaptive Response of a Gene Network to Environmental Changes by Fitness-Induced Attractor Selection , 2006, PloS one.

[13]  Kenji Leibnitz,et al.  Biologically Inspired Adaptive Multi-Path Routing in Overlay Networks , 2005 .

[14]  Fu Miao,et al.  Concurrent multipath traffic impersonating for enhancing communication privacy , 2014, Int. J. Commun. Syst..

[15]  Kenji Leibnitz,et al.  Proposal and evaluation of a future mobile network management mechanism with attractor selection , 2012, EURASIP J. Wirel. Commun. Netw..

[16]  Jingyu Wang,et al.  Paths selection-based resequencing queue length in concurrent multipath transfer , 2015, Int. J. Commun. Syst..

[17]  Edoardo Amaldi,et al.  Energy-aware IP traffic engineering with shortest path routing , 2013, Comput. Networks.

[18]  Naoki Wakamiya,et al.  Error-Tolerant and Energy-Efficient Coverage Control Based on Biological Attractor Selection Model in Wireless Sensor Networks , 2012, Int. J. Distributed Sens. Networks.