  Wireless networks used for Internet of Things (IoT) are expected to largely
involve cloud-based computing and processing. Softwarised and centralised
signal processing and network switching in the cloud enables flexible network
control and management. In a cloud environment, dynamic computational resource
allocation is essential to save energy while maintaining the performance of the
processes. The stochastic features of the Central Processing Unit (CPU) load
variation as well as the possible complex parallelisation situations of the
cloud processes makes the dynamic resource allocation an interesting research
challenge. This paper models this dynamic computational resource allocation
problem into a Markov Decision Process (MDP) and designs a model-based
reinforcement-learning agent to optimise the dynamic resource allocation of the
CPU usage. Value iteration method is used for the reinforcement-learning agent
to pick up the optimal policy during the MDP. To evaluate our performance we
analyse two types of processes that can be used in the cloud-based IoT networks
with different levels of parallelisation capabilities, i.e., Software-Defined
Radio (SDR) and Software-Defined Networking (SDN). The results show that our
agent rapidly converges to the optimal policy, stably performs in different
parameter settings, outperforms or at least equally performs compared to a
baseline algorithm in energy savings for different scenarios.
