Profit Maximization of Offloading Tasks for Mobile Edge Computing in C-RAN Using Ant Colony Algorithm | ||
| International Journal of Telecommunications | ||
| Volume 05, Issue 02, July 2025, Pages 1-23 PDF (1.92 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijt.2025.399953.1123 | ||
| Authors | ||
| Sherif M. Aboul* 1; Hala M. Abd El Kader2; Esraa M. Eid3; Shimaa S. Ali4 | ||
| 11. Communication Department, Faculty of Engineering at Shoubra, Benha University, Cairo, 11629, Egypt. 2. Electrical Department, Faculty of Engineering, MTI University, Cairo, 11439, Egypt. | ||
| 2Communication Department, Faculty of Engineering at Shoubra, Benha University, Cairo, 11629, Egypt. | ||
| 31. Communication Department, Faculty of Engineering at Shoubra, Benha University, Cairo, 11629, Egypt. 2. Faculty of Computer Science, Benha National University, Cairo, 13518, Egypt. | ||
| 4Communication Department, Faculty of Engineering at Shoubra, Benha University, Cairo, 11629, Egypt | ||
| Abstract | ||
| Abstract: Mobile Edge Computing (MEC) is vital for next-generation low-latency services, enabling resource-constrained mobile devices to offload intensive tasks to cloud-based infrastructure. This reduces energy consumption and latency, making MEC a key component of future mobile networks. Utilizing the Cloud-Radio Access Network (C-RAN) architecture, which integrates Baseband-Units (BBU) with MEC servers and Remote-Radio-Heads (RRHs), complex tasks are executed closer to users, improving service quality and creating new revenue streams for network operators. This paper examines computational offloading profitability from a network operator’s perspective. The offloading process involves optimizing radio and computational resources, posing a non-deterministic polynomial-time (NP) hard problem. To address this complexity, four optimization algorithms are evaluated: Ant-Colony Optimization (ACO), Normal-Genetic-Algorithm (NGA), Fast-Genetic-Algorithm (FGA), and Modified Spectrum Efficiency-Based Joint Optimization for Offloading and Resource-Allocation (Modified SJOORA). ACO, inspired by ant behavior, seeks optimal paths, while NGA and FGA simulate natural selection, with FGA offering faster convergence. Modified SJOORA is designed to optimize resource-allocation in MEC environments. The study compares these algorithms under various conditions to identify the most effective for profit maximization. Additionally, a novel approach reduces computational time by using a strategically seeded population and regression-based machine learning to estimate resource-allocation, maintaining accuracy while enhancing efficiency. | ||
| Keywords | ||
| Profit Maximization; Resource Allocation; Cloud Radio Access Network; Mobile Edge Computing; Optimization Algorithms | ||
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