A Self-Adaptive Load Balancing Approach for Software-Defined Networks in IoT

The Internet of Things (IoT) is gaining popularity as it offers to connect billions of devices and exchange data over the internet. However, the large-scale and heterogeneous IoT network environment brings serious challenges to assuring the quality of service of IoT-based services. In this context, Software-Defined Networking (SDN) shows promise in improving the performance of IoT services by decoupling the control plane from the data plane. However, existing SDN-based distributed architectures are able to address the scalability and management issues in static IoT scenarios only. In this paper, we utilize multiple M/M/1 queues to model and optimize the service-level and system-level objectives in dynamic IoT scenarios, where the network switches and/or their request rates could change dynamically over time. We propose several heuristic-based solutions including a genetic algorithm, a simulated annealing algorithm and a modified greedy algorithm with the goal of minimizing the queuing and processing times of the requests from switches at the controllers and balancing the controller loads while also incorporating the switch migration costs. Empirical studies using Mininet-based simulations show that our algorithms offer effective self-adaptation and self-healing in dynamic network conditions.