The Internet of Things (IoT) is gaining increasing popularity today by offering to connect billions of devices and exchange data over the internet. However, the large-scale and heterogeneous network environment brings serious challenges to assuring the quality of service in IoT services. By decoupling the control plane from the data plane, Software-Defined Networking (SDN) shows promise in improving both service-level and system-level performance in IoT through the use of logically centralized but physically distributed controllers. However, existing SDN-based distributed architectures address the scalability and management issues in static IoT scenarios only. To address the dynamic issues, this paper utilizes multiple M/M/1 queues to model and optimize both 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 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.