Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks

• URL: http://arxiv.org/abs/2501.06242v1
• Date: Wed, 08 Jan 2025 16:19:44 GMT
• Title: Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks
• Authors: Alireza Ebrahimi, Fatemeh Afghah,
• Abstract summary: 5G technology enhances industries with high-speed, reliable, low-latency communication, revolutionizing mobile broadband and supporting massive IoT connectivity. With the increasing complexity of applications on User Equipment, offloading resource-intensive tasks to robust servers is essential for improving latency and speed. This paper introduces a novel methodology to efficiently allocate both communication resources among individual UEs. It provides a robust and efficient solution to the challenges posed by the evolving landscape of 5G technology.
• Score: 6.725133919174076
• License:
• Abstract: 5G technology enhances industries with high-speed, reliable, low-latency communication, revolutionizing mobile broadband and supporting massive IoT connectivity. With the increasing complexity of applications on User Equipment (UE), offloading resource-intensive tasks to robust servers is essential for improving latency and speed. The 3GPP's Multi-access Edge Computing (MEC) framework addresses this challenge by processing tasks closer to the user, highlighting the need for an intelligent controller to optimize task offloading and resource allocation. This paper introduces a novel methodology to efficiently allocate both communication and computational resources among individual UEs. Our approach integrates two critical 5G service imperatives: Ultra-Reliable Low Latency Communication (URLLC) and Massive Machine Type Communication (mMTC), embedding them into the decision-making framework. Central to this approach is the utilization of Proximal Policy Optimization, providing a robust and efficient solution to the challenges posed by the evolving landscape of 5G technology. The proposed model is evaluated in a simulated 5G MEC environment. The model significantly reduces processing time by 4% for URLLC users under strict latency constraints and decreases power consumption by 26% for mMTC users, compared to existing baseline models based on the reported simulation results. These improvements showcase the model's adaptability and superior performance in meeting diverse QoS requirements in 5G networks.

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