Enhanced Particle Swarm Optimization for Task Offloading and Scheduling in Cloud-Fog Environment

The explosion of devices and their varied uses in the Internet of Things (IoT) have created a massive quantity of data that requires significant processing power. Fog computing, as a prolongation of cloud computing, presents a promising new model by bringing pro-cessing power closer to users through fog servers. Compared to accessing distant cloud servers, this significantly reduces latency, or the required time for data to travel. This setup allows users to offload tasks to nearby servers, ultimately improving the Quality of Service (QoS) they experience. Finding the best match between workflow tasks and available resources is critical to minimizing completion time (makespan), especially in delay-sensitive applications requiring fast data processing. However, achieving this op-timal match remains a challenge. This work proposes an Enhanced Particle Swarm Op-timization (EPSO) algorithm specifically designed to address this challenge. The per-formance of EPSO is compared against PSO, Max-Min, and Round-Robin (RR) scheduling methods. Simulations are conducted using diverse scientific workflow domains. The results demonstrate that EPSO outperforms all other methods in minimizing makespan across all tested workflows. Furthermore, EPSO exhibits competitive performance in other metrics like energy consumption and cost while maintaining greater stability and reliability.