Abstract
Distributed edge computing paradigms such as edge computing, fog computing, cloudlets, edge clouds, multi-access edge computing, etc., have emerged as promising solutions to fulfill the IoT applications' context-aware and latency requirements. In these computing paradigms, computational and storage resources are present near the IoT devices in the network between the IoT device and cloud data centers. There has been tremendous growth in machine learning research and its applications in many domains in recent times. This work investigates machine learning applications in edge environments. Then a classifier is proposed to classify edge applications in edge computing. At last, a detailed discussion on issues and open challenges is provided to explore future possibilities.
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