A Model-Driven Framework for Optimum Application Placement in Fog Computing Using a Machine Learning Based Approach

The pervasiveness of ubiquitously connected smart devices are the main factors in shaping the computing. With the advent of Internet of things (IoTs), massive amount of data is being generated from different sources. The centralized architecture of cloud has become inefficient for the services provision to IoT enabled applications. For better support and services, fog layer is introduced in order to manage the IoT applications demands like latency, responsiveness, deadlines, resource availability and access time etc. of the fog nodes. However, there are some issues related to resource management and fog nodes allocation to the requesting application based on user expectations in the fog layer that need to be addressed. In this paper, we have proposed a Framework, based on Model Driven Software Engineering (MDSE) that practices Machine Learning algorithms and places fog enabled IoT applications at a most suitable fog node. MDSE is meant to develop software by exploiting the problem at domain model level. It is the abstract representation of knowledge that enhances productivity by maximization of compatibility between the systems. The proposed framework is a meta-model that prioritizes the placement requests of applications based on their required expectations and calculates the abilities of the fog nodes for different application placement requests. Rules based machine learning methods are used to create rules based on user’s requirements metrics and then results are optimized to get requesting device placement in the fog layer. At the end, a case study is conducted that uses fuzzy logic for application mapping and shows how the actual application placement will be done by the framework. The proposed meta-model reduces complexity and provides flexibility to make further enhancements according to the user’s requirement to use any of the Machine Learning approaches.