P-UCB: A Preference-Based Upper Confidence Bound Strategy for Efficient Edge Server Placement

Mobile Edge Computing (MEC) is an emerging network architecture designed to enhance Quality of Service (QoS) by bringing computational resources and storage closer to end users. In MEC environments, strategic placement of edge servers is crucial to minimize costs and optimize QoS. The Edge Server Placement (ESP) problem has been effectively addressed by using Multi-Armed Bandit (MAB) algorithms, known for their efficiency and adaptability, with the Upper Confidence Bound (UCB) algorithm being particularly prominent due to its stable performance and low dependency on parameters. However, UCB suffers excessive exploration and high uncertainty in reward estimation during its initial phase, leading to slow convergence. To overcome these challenges, we propose a novel Preference-based UCB (P-UCB) algorithm, which integrates the preference function into the UCB framework, drawing inspiration from the Gradient Bandit (GB) method. This modification not only accelerates convergence, but also improves overall efficiency. Furthermore, to address the Base Station Allocation (BSA) issue within the ESP context, we introduce a Weighted Base Station Allocation (WBSA) algorithm, which helps better manage access delay and workload balance. The P-UCB algorithm is evaluated through a comprehensive metric that based on access delay and workload balance, showing significant improvements over existing methods such as Multiple Choice (MC)-UCB, Q- Particle Swarm Optimization (QPSO), and Genetic Algorithm (GA). Experimental results on a real-world dataset show that P-UCB achieves a notable performance increase of at least 12.9%, effectively optimizing access delay and workload balance across various experimental settings, including the number of base stations, edge servers, and other relevant system parameters.