Machine learning-based computation offloading in edge and fog: a systematic review

Today, Mobile Cloud Computing (MCC) alone can no longer respond to the increasing volume of data and satisfy the necessary delays in real-time applications. In addition, challenges such as security, energy consumption, storage space, bandwidth, lack of mobility support, and lack of location awareness have made this problem more challenging. Expanding applications such as online gaming, Augmented Reality (AR), Virtual Reality (VR), metaverse, e-health, and the Internet of Things (IoT) have brought up new paradigms for processing big data. Some of the paradigms that have emerged in the last decade are trying to alleviate cloud computing problems jointly. Mobile Edge Computing (MEC) and Fog Computing (FC) are the most critical techniques that serve the IoT. One of the common points of the above paradigms is the offloading of IoT tasks. This paper reviews machine learning-based computation offloading mechanisms in the edge and fog environment. This review covers three significant areas of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We discuss various performance metrics, tools, and case studies and analyze their advantages and disadvantages. We systematically elaborate on open issues and research challenges that are crucial for the next decade.

Automated summary

Mobile Cloud Computing (MCC) is no longer sufficient to handle the increasing data volume and real-time application delays. Challenges such as security, energy consumption, storage space, bandwidth, mobility support, and location awareness have made this problem more complex. Edge Computing (MEC) and Fog Computing (FC) are critical techniques for the IoT, and machine learning-based computation offloading mechanisms in these environments are reviewed in this paper.