Fog computing is a recent paradigm leveraging available resources at the edge of the network intended to extend the traditional cloud model towards the novel cloud continuum computing model. Recognized the unstoppable growth of highly dynamic and heterogeneous edge devices, as well as the pop up of a large set of diverse and ever more demanding services, the selection of those edge resources best meeting service requirements while also matching the expected QoS guarantees is, with no doubt, a challenging task. This paper presents a rankbased model aimed at both evaluating edge nodes’ characteristics and selecting nodes best performing the controller role, whilst simultaneously satisfying the required QoS constraints, coining the so-called Control-as-a-Service concept. To that end, a yet simple prediction strategy, based on Dynamic Branch Prediction is introduced to avoid unnecessary controller exchanges and QoS degradation. In the performed experiments, the proposed method yielded a reduction in the number of exchanges when compared to a solution with no prediction, under different scenarios. Comparing distinct selection strategies, the proposed model presented an improvement in controller availability as well as in relevant controllers’ characteristics, such as battery and memory capacity.