Nowadays, artificial intelligence provides an excellent opportunity for scientists to improve the efficiency of resource allocation in communication networks. In this paper, we focus on applying two methods: Long-Short Term Memory and Monte Carlo Tree Search, to solve the problem of cloud resource allocation in dynamic, real-time traffic scenarios. We use a framework of Software Defined Elastic Optical Networks and cloud resources available from Amazon Web Services. Results show that the application of Monte Carlo Tree Search and Long-Short Term Memory provides superior performance, which is an excellent opportunity for network operators to achieve better utilization of their networks, with lower operational costs.