A Review on Resource Allocation Methodologies in Fog/Edge Computing

Abstract

Off late we have seen an increase in the usage of IOT devices in various domains. Such devices are equipped with advanced data sensors that can gather environmental data, processing it to certain extent and transmitting them back to backend devices. This is possible due to reduction in computation cost, easy access to Wireless Technologies and a plethora of sensors being made available to the end user. However, one of the open challenges in this regard is to be able to be complete the assigned task reliably within a specific period to be able to satisfy the latency requirements of latency sensitive applications. Since the IOT devices generally tend to have a limited amount of computing resource, it needs support from the backend computing machines that can perform a part of the heavy computation. In most cases these back end devices remain in the edge of the network are not offloaded to the cloud as high network latency is introduced otherwise. This makes Edge computing an ideal choice for Latency sensitive IOT applications. In this research paper we will be exploring the IOT based Edge Computing Applications and Machine Learning Tools that are used for the same