LIMO: Load-balanced Offloading with MAPE and Particle Swarm Optimization in Mobile Fog Networks

TL;DR

Mobility in fog networks causes uneven load distribution and increased cloud offloading, degrading QoS. The paper introduces LIMO, a mobility-aware load-balancing framework that combines the MAPE control loop with Particle Swarm Optimization to autonomously migrate application modules and balance resource usage across fog nodes. Key contributions include a PSO-based node selection within a MAPE workflow, a formal objective balancing makespan and utilization, and MobFogSim-based validation showing significant reductions in cloud offload and task latency. This approach improves edge proximity and resource efficiency in mobile IoT deployments with practical implications for scalable fog infrastructures.

Abstract

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%.

Figures (5)

• Figure 1: Architecture of LIMO based on the MAPE control loop.
• Figure 2: Utilization of 15 Fog nodes with different number of Tasks.
• Figure 3: Average utilization of fog nodes in various fog scenarios.
• Figure 4: Average Offloading rate of the tasks.
• Figure 5: Average TTC of Tasks for different number of Fog nodes.