Performance Evaluation of Fog-Computing Based on IoT Healthcare Application

Abstract

Healthcare applications like emergency response systems are one of the Internet of Things (IoT) applications. These applications are classified as application-sensitive, where delay and throughput play an important role. To overcome the latency issue over these applications, a Fog computing paradigm has been introduced, in which cloud services are expanded to the edge of the network in order to reduce the delay. Several obstacles of the Fog Computing such as resource-allocation and job-scheduling are still in its infancy. However, fog devices located at the edge of the network are resource restricted. Thus, it is vital to determine the assignment and scheduling of a job on a fog device. An intelligent fog computing scheduling model that offers service-provisioning for IoT while reducing the latency has been proposed in this paper. A case study with a critical healthcare application (An electrocardiogram (ECG)) has been presented. This will optimally schedule the requests of ECG sensors on a fog environment and proficiently handle their demands on existing resources for each fog node. The proposed model was evaluated using iFogSim toolkit in terms of delay performance metric. The results show that the proposed model performance outperforms the existing approaches.