Reinforcement Learning for Resource Allocation in Cloud Datacenter

Cloud technologies provide capabilities that can guarantee to the end user high availability, performance and scalability. However, the growing use of IoT technologies and devices, have made the applications not only more computationally intensive, but also data intensive. Because of this, dynamically scaling applications running on clouds can lead to varied and unpredictable results due to highly time-varying workloads distinguishes this new kind of applications. These applications are also often composed of different independent modules that could be easily moved across devices. Automatic scheduling and allocation of these modules is not an easy task, because there could be many conditions that prevent the design of a smart solutions. Thus determining appropriate scaling policies in a dynamic non-stationary environment is non-trivial, as a problem arises concerning resource allocation. Decision making about which resources should be added and removed, when the underlying performance of the resource is in a constant state of flux, becomes an issues. In this work we model both the applications and the infrastructure in order to formulate e Reinforcement Learning problem for automatically find the best configuration for the applications modules, taking into account the environment in which they are placed and the applications already running.