FScaler: Automatic Resource Scaling of Containers in Fog Clusters Using Reinforcement Learning

Several studies leverage fog computing as a solution to overcome cloud delays, including computation, network, and data storage. Along with the increase in demands for computing resources in fog infrastructures, heterogeneous fog devices are used towards forming highly available clusters. Existing approaches support the use of heterogeneous fogs and enable dynamic updates and management of services through containerization and orchestration technologies. However, none of the existing works proposed a proactive solution to horizontally scale these resources based on the IoT workload fluctuations, in addition to deciding on proper placement of the scaled instances on fogs with minimal cost on the fly. An effective scaling results in improving the response time and avoid service instability on fog devices. Therefore, we propose in this work FScaler, a reinforcement learning agent that horizontally scales container's instances after studying user's demands, and schedules the placement of newly created instances based on defined cost functions after studying the change in resources availability. The environment of FScaler is modeled as an MDP to be solved by any RL algorithm. For this work, we study the efficiency of our MDP formulation by solving the problem using SARSA. Promising results are shown through testing using a real-life dataset presenting the variation of user's demands of a particular service and the change in resource availability over time.