An efficient service dispersal mechanism for fog and cloud computing using deep reinforcement learning

Abstract

Thousands of high-end physical servers are used to fulfill the huge resource demand of diverse applications or services, ranging from healthcare data analytic services to gaming services. The network latency, as one of the major limitations of cloud computing, becomes the primary reason for introducing fog computing by pushing the computing environment towards the edge of the network. The ability to offer computing environments in close proximity to the user's device improves the delivery of high-quality services. The majority of the research is devoted to providing the high quality of services using either fog or cloud environment. In this paper, a novel deep reinforcement learning-based service dispersal approach for fog and cloud computing (DRLSD-FC) is adopted for offering the service using both environments simultaneously. The request to avail services is sliced and dispersed between the nearby fog and cloud environments. By taking advantage of cloud resources, the proposed approach minimizes the workload on the fog environment without compromising the service quality. The proposed approach is implemented using the Keras framework. Implementation results show that DRLSD-FC can outperform over other related approaches.