Enhancing Application Placement and Prioritizing Emergency Patient in Medical Cloud-Fog Environments: A Multi-Criteria Decision-Making Approach

Abstract

Fog computing (FC) emerged as a solution to the constraints of cloud computing (CC), particularly in IoT and real-time applications, by providing additional computing resources and services. Its essence lies in processing data at the network’s edge via a distributed network of nodes, enabling more efficient, responsive data processing. However, choosing suitable fog nodes that can host and process application modules poses a challenge.
This paper proposes a study to address this challenge by using widely recognized MCDM (multi-criteria decision-making) methods, including AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution). The experiment was conducted using the iFogSim simulator, and the proposed method was evaluated based on CPU power, storage (RAM), latency, and network bandwidth. AHP is used to calculate the priority weights for all evaluation criteria, while the TOPSIS method is used to arrange the appropriate fog nodes.
This comprehensive approach aims to facilitate the selection of the most suitable fog nodes for hosting and processing application modules. The simulation results indicate that the proposed method offers notable benefits in placement time, resource gain, energy consumption, and latency in a fog computing environment.