Machine Learning Load Balancing Algorithms in SDN-enabled Massive IoT Networks

The Internet of Things (IoT) is a burgeoning field for study and experimentation. It allows users to create and receive a wide bevy of information from a massive array of devices. But as the IoT network gets denser, a load-balancing algorithm is required to keep itself running smoothly. Load Balancing is all but required in large IoT networks to avoid a part of servers getting overloaded and others being free. Existing solutions show both heuristic and machine learning algorithms designed for load balancing. Static algorithms go well with traditional IoT networks but are not built to scale dynamically and respond to loads. Dynamic heterogeneity and massive IoT disrupt load balancing. Machine learning-based algorithms have proven to give better scheduling solutions and improve performance in such networks. To analyze the performance of machine learning algorithms over heuristic ones, we designed an experimental testbed using a POX SDN controller and Mininet. We also show results confirming that the machine learning-based algorithms are better in terms of packet loss and response time.