Personalized Service Delivery using Reinforcement Learning in Fog and Cloud Environment

The ability to fulfill the resource demand in runtime is encouraging businesses to migrate to the cloud. Recently, to provide real-time cloud services and to save network resources, fog computing is introduced. To further improve the quality of service in the delivery process, Artificial Intelligence is being applied extensively.

However, the state-of-the-art in this regard is still immature as it mainly focuses on either fog or cloud. To address this issue, a novel reinforcement learning-based personalized service delivery (RLPSD) mechanism is proposed in the paper ‘Personalized Service Delivery using Reinforcement Learning in Fog and Cloud Environment‘, which allows the service provider to combine the fog and cloud environments while providing the service. RLPSD distributes the user’s service requests between fog and cloud, considering the users’ constraints (e.g. the distance from fog), thus resulting in personalized service delivery. The proposed RLPSD algorithm is implemented and evaluated in terms of its success rate, percentage of service requests’ distribution, learning rate, discount factor, etc.

You can access the paper here.