TL;DR
MOFARO is introduced, a multi-objective model within the Artificial Rabbit Optimization Algorithm for task allocation in edge-fog-cloud MCS environments that outperforms state-of-the-art methods by 1.8–3.9% in success rate, validating its superiority for scalable, robust MCS in dynamic settings.
The rapid growth of Internet of Things (IoT) applications has increased the need for task-offloading mechanisms that can operate efficiently under heterogeneous Fog resources, dynamic workloads, and stringent latency requirements. A recurring limitation in existing allocation and matching approaches is that the choice...
Internet of Things (IoT) applications and the increasing demand for cloud-based services have created major challenges in task scheduling for energy efficiency and carbon awareness in multi-cloud environments. This study proposes an Energy and Carbon Emission Aware Task Scheduling framework for IoT applications based o...
A novel unified benchmarking framework that systematically evaluates five prominent metaheuristics — Particle Swarm Optimization (PSO), Genetic Algorithm, Genetic Algorithm (GA), Bacterial Foraging Optimization (BFO), Ant Colony Optimization (ACO), and Artificial Bee Colony — for load balancing within a three-tier arch...
These findings demonstrate that DI-MNA provides an effective balance between solution quality, scalability, and computational efficiency for resource allocation in large-scale IoT networks.
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