Design and Evaluation of a Flexible Task Scheduling System Using Metaheuristic Algorithms in Fog-Cloud Computing Environment

نوع: Type: Thesis

مقطع: Segment: PHD

عنوان: Title: Design and Evaluation of a Flexible Task Scheduling System Using Metaheuristic Algorithms in Fog-Cloud Computing Environment

ارائه دهنده: Provider: sakine sohrabi

اساتید راهنما: Supervisors: Dr Mohammad nassiri and Dr Mehdi Sakhaei-nia

اساتید مشاور: Advisory Professors: Dr Reza Mohammadi

اساتید ممتحن یا داور: Examining professors or referees: Dr shakoor vakilian and Dr fereshteh Azadi parand and Dr hasan bashiri

زمان و تاریخ ارائه: Time and date of presentation: 2026

مکان ارائه: Place of presentation:

چکیده: Abstract: The rapid growth of the Internet of Things (IoT) and the massive volume of generated data have introduced challenges such as increased latency, network congestion, bandwidth limitations, and high energy consumption in cloud environments. Fog computing architecture, by bringing computation closer to the data sources, can substantially mitigate these issues. However, designing an efficient and flexible task scheduling mechanism in hybrid fog–cloud environments remains a fundamental challenge due to the dynamic and heterogeneous nature of requests. In this study, a multi-objective intelligent model for task scheduling in fog–cloud environments is proposed, aiming to simultaneously reduce task cancellation rates under congestion, prioritize requests efficiently, minimize cost and latency, and enhance workload balancing. To achieve these objectives, a Cheetah Optimization algorithm combined with Simulated Annealing is initially employed, followed by enhancements through Genetic Algorithms and improved Particle Swarm Optimization. The resulting model, IJFSAOPG, demonstrates superior performance compared to advanced algorithms such as IJFA, PWAO, EETSPSO, and PGABC-PSO. Simulation results indicate that the proposed model is particularly effective in reducing execution time and energy consumption, improving workload distribution under congestion, and enhancing service quality metrics in latency-sensitive and real-time applications. Therefore, the IJFSAOPG model can serve as a reliable and efficient solution for resource management and task scheduling in modern fog–cloud infrastructures, ultimately contributing to improved service quality and overall system efficiency at large scale.