MOBILE EDGE COMPUTING TARGETED TASK OFFLOADING BASED ON LOAD BALANCING CONCEPT

Abstract

With the rapid growth of mobile applications and IoT devices, the demand for low-latency and highperformance computing has increased significantly. Mobile Edge Computing (MEC) addresses this challenge by bringing computation and storage resources closer to end-users, reducing latency and improving the Quality of Service (QoS). However, the limited computational capacity of edge servers can lead to overloading, resource contention, and uneven task distribution. This research proposes a targeted task offloading strategy based on the load balancing concept to optimize resource utilization in MEC environments. Tasks from mobile devices are analyzed in real-time and offloaded to appropriate edge servers considering server load, network conditions, and task priority. By implementing dynamic load balancing algorithms, the system minimizes response time, reduces task rejection rates, and ensures fair resource allocation among multiple edge servers. Simulation results demonstrate that the proposed approach improves task processing efficiency, reduces energy consumption on mobile devices, and enhances overall system performance compared to traditional offloading methods. This framework provides a scalable and efficient solution for managing computational tasks in heterogeneous MEC environments, ensuring reliable and responsive services for end-users.