DRL4HFC: Deep Reinforcement Learning for Container-Based Scheduling in Hybrid Fog/Cloud System

The IoT-based applications have a set of complex requirements, such as a reliable network connection and handling data from multiple sources quickly and accurately. Therefore, combining a Fog environment with a Cloud environment can be beneficial for IoT-based applications, as it provides a distributed computing system that can handle large amounts of data in real time. However, the microservice provision to execute such applications with achieving a high Quality of Service (QoS) and low bandwidth communications. Thus, the container-based microservice scheduling problem in a hybrid Fog and Cloud environment is a complex issue that has yet to be fully solved. In this work, we first propose a container-based microservice scheduling model for a hybrid architecture. Our model is a multi-objective scheduler, named DRL4HFC, for Hybrid Fog/Cloud architecture. It is based on two Deep Reinforce Learning (DRL) agents. DRL-based agents learn the inherent properties of the various microservices, nodes, and environments to determine the appropriate placement of each microservice instance required to execute each task within the Business Process (BP). Our proposal aims to reduce the execution time, compute and network resource consumption, and resource occupancy rates of Fog/Cloud nodes. Second, we present a set of experiments in order to evaluate the effectiveness of our algorithm in terms of cost, quality, and time. The experimental results demonstrate that DRL4HFC achieves faster execution times, lower communication costs and better balanced resource loads.