Machine Learning for Fog Computing: Review, Opportunities and a Fog Application Classifier and Scheduler

In recent, there has been a huge increase in the number of context-aware and latency-sensitive IoT applications. Executing these applications on traditional cloud servers is infeasible due to strict latency requirements. Emerging edge technologies such as fog/edge computing, cloudlets, edge clouds etc. have been proposed recently to fulfill latency requirements of these applications. In these edge technologies, computing infrastructure is available near to the end-user devices. Scheduling the IoT applications on heterogeneous and distributed fog/edge nodes is an important and complex research problem and has been extensively studied in these domains by applying various traditional approaches. In recent times, there has been tremendous growth in machine learning research and its applications in many domains. This work makes a detailed study of the machine learning techniques and their applicability for the fog/edge computing by reviewing various machine learning applications and opportunities in fog/edge computing. As most of the existing works in machine learning confines to resource allocation, therefore, a fog application classifier and scheduling model is proposed that would help researchers to understand how a resource allocation and scheduling problem can be solved. We present detailed algorithms that schedule the IoT applications on multiple fog layers based on applications’ QoS. The performance of the proposed work has been validated through simulation study. Various challenges in the realization of machine learning in fog/edge computing along with the future possibilities are also presented.