Task Scheduling Techniques in Cloud and Fog Computing for Health Care

Rapidly influencing the Internet’s future, the Internet of Things (IoT) is emerging as a crucial technology in several industries, most notably healthcare. IoT is essential for managing conditions like high blood pressure, diabetes, and heart disease in domains like mobile health and remote patient monitoring. Any latency in data transport between the cloud and the application is undesirable in these applications, where real-time data processing is essential. We suggest combining fog computing with sensors and cloud computing to solve this. By lowering the amount of data sent between sensors and the cloud, this method improves system efficiency by enabling more effective data collection and processing. Numerous jobs of various priority and length are generated by wireless sensor networks (WSNs) utilized for monitoring in the healthcare industry and submitted to fog computing systems at the same time. To guarantee that jobs are appropriately prioritized, with priority taking precedence over task duration, an efficient task scheduling algorithm is necessary. This study suggests using a unique technique called Tasks Classification and Virtual Machines Categorization (TCVC), which ranks tasks according to their significance, to improve the performance of static task scheduling algorithms. Depending on the patient’s health, the assignments received through IoT will be divided into three categories: high, medium, and low relevance. The MAX-MIN scheduling algorithm will be subjected to the TCVC method in order to assess the efficacy of this approach. The influence on algorithm complexity, resource availability, Total Execution Time (TET), Total Waiting Time (TWT), and Total Finish Time (TFT) will be evaluated using the CloudSim simulator.