TM-MOAOA: a two-stage task scheduling approach using TOPSIS and multi-objective Archimedes optimization in fog-cloud environment - Khaledian, Navid; Razzaghzadeh, Shiva; Moazzami, Setareh; Kivi, Parisa Norouzi (Computing, 2025)
The explosive growth of the Internet of Things (IoT) has introduced significant challenges in real-time data processing, placing substantial pressure on fog and cloud computing infrastructures. Efficient and rapid task processing is essential to manage the massive volume of heterogeneous tasks with varying priority levels. This study proposes a two-stage framework for optimizing load balancing and task scheduling in cloud and fog environments. The framework integrates Multi-Criteria Decision-Making (MCDM) with the Multi-Objective Archimedes Optimization Algorithm (MOAOA). In the first stage, MCDM prioritizes tasks based on key factors such as estimated execution time and deadlines. MOAOA schedules the prioritized tasks within the cloud environment in the second stage. This innovative integration of MCDM and MOAOA enables multi-dimensional optimization for load distribution and resource allocation in distributed IoT systems, thereby enhancing Quality of Service (QoS) and reducing operational costs. Simulation results demonstrate statistically significant improvements in key performance metrics, including makespan, cost, and processing speed. Specifically, compared to baseline algorithms such as Optimal Fuzzy Load Balancing (OFLB), Hybrid Grey Wolf and Improved Particle Swarm Optimization Algorithm (HGWIPSOA), and Multi-objective Cat Swam Optimization Fault tolerant Load Balancing (MCSOFLB), the proposed approach achieves an average reduction of 26.75% in makespan, 22.48% in cost, and a 7.42% improvement in processing speed.