Mobility-Aware Offloading Decision for Multi-Access Edge Computing in 5G Networks.

Area of Science:

• Computer Science
• Telecommunications Engineering

Background:

• Multi-access Edge Computing (MEC) is crucial for 5G networks, enabling low-latency applications by processing data near users.
• Ultra-dense 5G networks present challenges in managing user mobility and frequent handovers in MEC environments.

Purpose of the Study:

• To develop algorithms for optimizing task offloading decisions in MEC networks, considering handover costs.
• To enhance MEC performance by balancing handover, delay, and energy consumption for online task offloading.

Main Methods:

• Proposed two online task offloading algorithms: one utilizing user and base station (BS) information, and another employing a BS-learning approach based on observed costs.
• Incorporated handover cost into optimization functions for decision-making under unknown future tasks.

Main Results:

• The proposed algorithms demonstrated superior performance compared to existing literature and baseline scenarios.
• Simulation results confirmed the effectiveness of the developed methods in improving overall system performance in MEC-enabled 5G networks.

Conclusions:

• The developed algorithms provide an effective solution for managing user mobility and optimizing task offloading in dense MEC networks.
• The research contributes to efficient resource management in 5G MEC by considering critical factors like handover, delay, and energy costs.