Performance Analysis of Non-Polynomial Scheduling Algorithms in Cloud and Fog Environments

In the rapidly evolving landscape of cloud computing and the Internet of Things (IoT), efficient task scheduling and resource management are crucial for optimizing system performance and user satisfaction. The advent of fog computing, which extends cloud capabilities to the network's edge, introduces new challenges and opportunities in distributed computing environments. This research explores the potential of various non-polynomial optimization algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bayesian Optimization (BO), to address the complex task scheduling problems in fog and cloud environments. The study focuses on optimizing Quality of Service (QoS) and Quality of Experience (QoE) requirements, which are critical for user satisfaction and system efficiency. By analyzing the strengths and limitations of these algorithms, the research aims to develop effective scheduling strategies that balance QoS, QoE, resource utilization, and energy consumption. A comparative analysis is conducted to evaluate the performance of these algorithms, identifying the most suitable approaches or hybrid models for specific scheduling scenarios. The findings from this study contribute to the advancement of intelligent scheduling systems in fog and cloud computing, promoting improved system performance and resource allocation. This work enhances the potential for widespread adoption of these technologies by enabling more efficient and effective resource management in distributed computing paradigms.