Resource allocation through logistic regression and multicriteria decision making method in IoT fog computing

Abstract

Cloud computing has received a lot of attention from both researcher and developer in last decade due to its unique structure of providing services to the user. As the digitalization of world, heterogeneous devices, and with the emergence of Internet of Things (IoT), these IoT devices produce different type of data with distinct frequency, which require real-time and latency sensitive services. This provides great challenge to cloud computing framework. Fog computing is a new framework to accompaniment cloud platform and is proposed to extend services to the edge of the network. In fog computing, the entire user's tasks are offloaded to distributed fog nodes to the edge of network to avoid delay sensitivity. We select fog computing network dwell different set of fog nodes to provide required services to the users. Allocation of defined resource to the users in order to achieve optimal result is a big challenge. Therefore, we propose dynamic resource allocation strategy for cloud, fog node, and users. In the framework, we first formulate the ranks of fog node using TOPSIS to identify most suitable fog node for the incoming request. Simultaneously logistic regression calculates the load of individual fog node and updates the result to send back to the broker for next decision. Simulation results demonstrate that the proposed scheme undoubtedly improves the performance and give accuracy of 98.25%.