Dynamic Controller Provisioning in Software Defined Networks

TLDR

Software‑Defined Networking offers programmability, but a single centralized controller limits performance and scalability in large WANs, prompting proposals for multiple cooperative controllers and giving rise to the Dynamic Controller Provisioning Problem that seeks to adapt controller count and placement to reduce flow‑setup time and communication overhead. This work proposes a framework for deploying multiple controllers in a WAN. The framework dynamically adjusts the number of active controllers and assigns each a subset of OpenFlow switches based on network dynamics, formulated as an integer linear program and solved by two heuristics to minimize flow‑setup time and communication overhead. Simulations demonstrate that the proposed solution achieves minimal flow‑setup time while incurring very low communication overhead.

Abstract

Software Defined Networking (SDN) has emerged as a new paradigm that offers the programmability required to dynamically configure and control a network. A traditional SDN implementation relies on a logically centralized controller that runs the control plane. However, in a large-scale WAN deployment, this rudimentary centralized approach has several limitations related to performance and scalability. To address these issues, recent proposals have advocated deploying multiple controllers that work cooperatively to control a network. Nonetheless, this approach drags in an interesting problem, which we call the Dynamic Controller Provisioning Problem (DCPP). DCPP dynamically adapts the number of controllers and their locations with changing network conditions, in order to minimize flow setup time and communication overhead. In this paper, we propose a framework for deploying multiple controllers within an WAN. Our framework dynamically adjusts the number of active controllers and delegates each controller with a subset of Openflow switches according to network dynamics while ensuring minimal flow setup time and communication overhead. To this end, we formulate the optimal controller provisioning problem as an Integer Linear Program (ILP) and propose two heuristics to solve it. Simulation results show that our solution minimizes flow setup time while incurring very low communication overhead.