Task Prioritization and Scheduling of Fog Computing Model in Healthcare Systems

Abstract

Health-related applications are one of the most sensitive areas which should be delivered on time e ciently. For the storage and processing of enormous health data, Cloud Computing could not be e cient as Cloud Data Centers take a large time to process and send back the results. The new paradigm, called Fog Computing is applicable in cases like these. In this research, we utilize the sample time-critical healthcare system where the IoT sensors' data is divided into critical and normal tasks where critical tasks are prioritized over normal patients' data. To address their management, Fog Computing is used at the edge of the network. In this paper, a new fog-cloud-based algorithm called Prioritized Latency Aware Energy E cient Algorithm (PLAEE) is developed by utilizing the existing algorithms in the fog system and also by process optimization of the core evaluation metrics, latency, and energy usage. This algorithm shows superiority to the existing algorithms in terms of performance metrics. The experimentation is performed using Blood Pressure data collected from the University of Piraeus. In terms of response time, the PLAEE is performing 36.40%, 14.82%, 14.70%, and 6.03% better than Cloud only, Edge-wards, Resource Aware, and SCATTER Algorithm respectively. In terms of Energy Consumption, the PLAEE is performing 23.85%, 14.96%, 10.84%, and 2.83% better than Cloud only, Edge-wards, Resource Aware, and SCATTER Algorithm respectively. Almost 98% of critical data are placed in the FNs according to the Tasks Managed value calculated where 91.70%, 6.28%, and 2.01% of Critical Tasks are placed in FZ1, FZ2, and CDC respectively