Toward intelligent resource management in dynamic Fog Computing‐based Internet of Things environment with Deep Reinforcement Learning: A survey

• Published in: International journal of communication systems Vol. 36; no. 4
• Main Authors: Gupta, Shally, Singh, Nanhay
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 10.03.2023
• Subjects: Algorithms; AoI (age of information); Cloud computing; Deep learning; deep reinforcement learning (DRL); fog computing (FC); Graph theory; Internet of Things; Internet of Things (IoT); Machine learning; Markov decision process; Q‐learning; Resource management; Resource scheduling
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.5411

Summary Fog computing has already started to gain a lot of momentum in the industry for its ability to turn scattered computing resources into a large‐scale, virtualized, and elastic computing environment. Resource management (RM) is one of the key challenges in fog computing which is also related to the success of fog computing. Deep learning has been applied to the fog computing field for some time, and it is widely used in large‐scale network RM. Reinforcement learning (RL) is a type of machine learning algorithms, and it can be used to learn and make decisions based on reward signals that are obtained from interactions with the environment. We examine current research in this area, comparing RL and deep reinforcement learning (DRL) approaches with traditional algorithmic methods such as graph theory, heuristics, and greedy for managing resources in fog computing environments (published between 2013 and 2022) illustrating how RL and DRL algorithms can be more effective than conventional techniques. Various algorithms based on DRL has been shown to be applicable to RM problem and proved that it has a lot of potential in fog computing. A new microservice model based on the DRL framework is proposed to achieve the goal of efficient fog computing RM. The positive impact of this work is that it can successfully provide a resource manager to efficiently schedule resources and maximize the overall performance. As the fog computing industry continues to grow, there is a growing need for automated fog resource management solutions. Reinforcement learning (RL) and deep reinforcement learning (DRL) algorithms have shown significant promise in addressing the problem of managing resources in the fog environment. We examine current research in this area, comparing RL and DRL approaches with traditional algorithmic methods for managing resources in fog computing environments. A new microservice model based on the DRL framework is proposed to achieve the goal of efficient fog computing resource management.