Meta FOA: Classifying and Scheduling of IoT Tasks in Cloud Computing Environment

Task scheduling in cloud computing plays a critical role in ensuring reliable and time-constrained execution of applications, especially for mission-critical services such as airline reservation systems (ARS). To address challenges related to low reliability, limited fault tolerance, and poor deadline adherence in existing schedulers, an efficient Metaheuristic Falcon Optimization Algorithm (FOA)-based scheduling framework is introduced for optimal task-to-virtual machine mapping. The proposed approach is implemented using the CloudSim simulation toolkit and evaluated under realistic ARS workload conditions, with comparative analysis against Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA). Experimental results demonstrate that the FOA-based scheduler achieves significant performance gains, improving system reliability by up to 18.6%, enhancing fault tolerance by 21.3%, and increasing deadline adherence by 19.8% compared to the benchmark methods. These improvements are attributed to the effective exploration-exploitation balance and adaptive search strategy of the FOA, which enables robust scheduling decisions under dynamic workload conditions. The findings confirm that the FOA-based scheduling framework provides a dependable and resilient solution for time-critical cloud applications, particularly in airline reservation environments where service continuity and timeliness are paramount.