A Highly Tunable Virtual Topology Controller

TL;DR

This paper introduces a highly tunable Virtual Topology controller, analyzing and transforming existing algorithms for better controllability, and identifying optimal control parameters through probabilistic analysis validated by simulations.

Contribution

It provides a new tunable VTR algorithm by analyzing and transforming existing methods and determining optimal control parameters via probabilistic analysis.

Findings

Analyzed controllability of heuristic and neural network VTR algorithms

Transformed existing algorithms into tunable versions

Identified optimal control parameter through probabilistic analysis

Abstract

Much research in the last two decades has focused on Virtual Topology Reconfiguration (VTR) problem. However, most of the proposed methods either has low controllability, or the analysis of a control parameter is limited to empirical analysis. In this paper, we present a highly tunable Virtual Topology (VT) controller. First, we analyze the controllability of two previously proposed VTR algorithms: a heuristic method and a neural networks based method. Then we present insights on how to transform these VTR methods to their tunable versions. To benefit from the controllability, an optimality analysis of the control parameter is needed. In the second part of the paper, through a probabilistic analysis we find an optimal parameter for the neural network based method. We validated our analysis through simulations. We propose this highly tunable method as a new VTR algorithm.