An Adaptive and Priority Improved Task Scheduling Model for Fog Computing Environment
Monika, Harkesh Sehrawat · 2024
Fog computing is a lightweight distributed environment that ensures the IoT-based service distribution. The real-time connectivity and response are the key challenges for this environment. As the environment is connected with large number of users, the task execution within timeline is the primary challenge in this environment. In this paper, an adaptive and priority-driven task scheduling method is integrated to optimize the resource allocation and execution of tasks within deadline. The focus of this model is to reduce the execution delay and failure rate in real-time. Task priority and the application adaptive challenges are the key aspects considered in this work. The functional process of this model is divided into three stages. In the first stage, requests are analyzed respective to adaptivity and priority of tasks. In second stage, the resources are shortlisted based on history performance, reliability and load. In the final stage, the rule-based task selection is performed and mapped to the available resources. The objective of the work is to minimize the makespan, failure rate, and execution delay. The model is analyzed against FCFS, SJF, LFJ, PEFT, HEFT, MOPT, and SDBATS model. The analysis results confirm and validate the reliability and effectiveness in the real environment.