Evaluation of topology optimization objectives

Two network-wide optimization contexts are traffic engineering and topology optimization. Various optimization objective functions and metrics have been proposed for both contexts. Yet, it is hard to evaluate the efficiency of those optimization objectives. Previously, a study analyzed the efficienc...

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Bibliographic Details
Published in2015 IEEE 40th Conference on Local Computer Networks (LCN) pp. 458 - 461
Main Authors Hanay, Y. Sinan, Arakawa, Shin'ichi, Murata, Masayuki
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2015
Subjects
Delays
IP networks
Optical fibers
Telecommunication traffic
Topology
Yttrium
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Summary:Two network-wide optimization contexts are traffic engineering and topology optimization. Various optimization objective functions and metrics have been proposed for both contexts. Yet, it is hard to evaluate the efficiency of those optimization objectives. Previously, a study analyzed the efficiency of some optimization metrics for traffic engineering by using linear programming (LP). On the other hand, in the topology optimization domain, there has not been any work on evaluation of different metrics. Because, it is hard to evaluate these metrics as the optimization algorithms are objective function tailored heuristics generally. As a result, a fair comparison of different objectives becomes hard. In this work, using machine learning we compare and analyze different traffic optimization objectives for topology optimization.
DOI:10.1109/LCN.2015.7366352