Enhancing Convergence with Optimal Feedback for Controlled Self-Organizing Networks

To tackle with problems emerging with rapid growth of information networks in scale and complexity, selforganization is one of promising design principles for future networks. Convergence of self-organizing controls, however, is pointed out to be comparatively slow in some practical applications. Therefore, it is important to reveal and enhance convergence of self-organizing controls. In controlled self-organization, which introduces an external observer/controller into self-organizing systems, systems are controlled in order to guide them to the desired state. Although previous controlled self-organization schemes could achieve this feature, convergence speed for reaching an optimal or a semi-optimal solution is still a challenging task. In this paper, we take potential-based self-organizing routing and provide an optimal feedback for faster convergence using the future state of the system. Simulation results show that the convergence speed of potentials is improved by 7.3 times with a proposed mechanism.