Open AccessEstimation of Traffic Matrix from Links Load using Genetic Algorithm
Author(s) -
Joseph L. Pachuau,
Arnab Roy,
Gopal Krishna,
Anish Kumar Saha
Publication year - 2021
Publication title -
scalable computing practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.192
H-Index - 18
ISSN - 1895-1767
DOI - 10.12694/scpe.v22i1.1834
Subject(s) - representation (politics) , matrix (chemical analysis) , algorithm , computer science , set (abstract data type) , link (geometry) , traffic generation model , genetic algorithm , mathematical optimization , data mining , mathematics , real time computing , politics , computer network , programming language , law , composite material , materials science , political science
Traffic Matrix (TM) is a representation of all traffic flows in a network. It is helpful for traffic engineering and network management. It contains the traffic measurement for all parts of a network and thus for larger network it is difficult to measure precisely. Link load are easily obtainable but they fail to provide a complete TM representation. Also link load and TM relationship forms an under-determined system with infinite set of solutions. One of the well known traffic models Gravity model provides a rough estimation of the TM. We have proposed a Genetic algorithm (GA) based optimization method to further the solutions of the Gravity model. The Gravity model is applied as an initial solution and then GA model is applied taking the link load-TM relationship as a objective function. Results shows improvement over Gravity model.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom