LIMO: Load-balanced Offloading with MAPE and Particle Swarm Optimization in Mobile Fog Networks
<|reference_start|>LIMO: Load-balanced Offloading with MAPE and Particle Swarm Optimization in Mobile Fog Networks: Fog computing is essentially the expansion of cloud computing towards the network edge, reducing user access time to computing resources and services. Various advantages attribute to fog computing, including reduced latency, and improved user experience. However, user mobility may limit the benefits of fog computing. The displacement of users from one location to another, may increase their distance from a fog server, leading into latency amplification. This would also increase the probability of over utilization of fog servers which are located in popular destinations of mobile edge devices. This creates an unbalanced network of fog devices failing to provide lower makespan and fewer cloud accesses. One solution to maintain latency within an acceptable range is the migration of fog tasks and preserve the distance between the edge devices and the available resources. Although some studies have focused on fog task migration, none of them have considered load balancing in fog nodes. Accordingly, this paper introduces LIMO; an allocation and migration strategy for establishing load balancing in fog networks based on the control loop MAPE (Monitor-Analyze-Plan-Execute) and the Particle Swarm Optimization (PSO) algorithm. The periodical migration of tasks for load balancing aims to enhance the system's efficiency. The performance of LIMO has been modeled and evaluated using the Mobfogsim toolkit. The results show that this technique outperforms the state-of-the-art in terms of network resource utilization with 10% improvement. Furthermore, LIMO reduces the task migration to cloud by more than 15%, while it reduces the request response time by 18%.<|reference_end|>
@article{seraj2024limo:,
    title={LIMO: Load-balanced Offloading with MAPE and Particle Swarm Optimization
  in Mobile Fog Networks},
    author={Yasaman Seraj, Soheil Fadaei, Bardia Safaei, Ali Javadi, Amir Mahdi
  Hosseini Monazzah, Ali Mohammad Afshin Hemmatyar},
    journal={arXiv preprint arXiv:2408.14218},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14218},
    primaryClass={cs.NI cs.DC}
}