# Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation
## 1 Introduction  
In recent years, there has been a significant evolution in mobile communications due to the widespread proliferation of mobile devices, which generate an unprecedented amount of data traffic. According to a recent Ericsson forecast, mobile subscriptions worldwide are expected to reach 8.6 billion by the end of 2028. Furthermore, global mobile data traffic is projected to increase nearly fourfold between 2023 and 2028, reaching around 53 exabytes per month by the end of 2028. This significant growth is driven by the ongoing expansion of 5G networks and the increasing use of data-intensive applications [1]. Thus, the new generation of mobile communications has to evolve to cope with this growth and ensure that emerging services and applications meet the specific demands of mobile users. Advanced 5G and 6G are being proposed as technologies that will address complex challenges related to ultra-dense deployments with strict latency and reliability requirements. In these next-generation network environments, producing both highly reliable and low latency enhancements simultaneously is complex. Cutting-edge technologies such as mobile edge computing (MEC) enable advancements in this regard, although bringing computing capabilities closer to the end mobile users is still required. The main MEC principle is to locate an edge data center (EDC) at the edge of the mobile network, promoting the distribute computation and storage capabilities to reduce communications distance, and, consequently, the communication delay between the mobile network and end users. The MEC approach seeks the joint optimization of mobile access and core network functions localized together in the edge cloud [2]. MEC has been standardized by the European Telecommunications Standards Institute (ETSI) [3] with the goal of delivering computingservices closer to the end user. This makes it particularly useful in scenarios where low latency and locality are critical [4]. The placement of EDCs in mobile edge computing environments presents a significant challenge. EDCs are positioned near wireless components, such as base stations (BSs), within infrastructure owned by a mobile network operator (MNO) to ensure effective radio coverage [5]. The locations of these EDCs are crucial for minimizing access delays for mobile users and optimizing resource utilization, particularly in smart cities where hundreds or thousands of base stations connect users to EDCs. Due to the vast size of these networks, poor placement of edge servers may lead to long access delays and uneven workload distribution, with some servers becoming significantly overloaded. In contrast, others remain underutilized or even idle. Thus, strategically placing edge servers is essential for enhancing the performance of various mobile applications, including reducing edge server access delays. Additionally, some research studies have identified a strong correlation between user mobility and the physical infrastructure of smart cities and the cellular access network [6; 7]. Therefore, it is essential to analyze the deployment of EDCs and their interconnection with the cellular network. This interconnection has been extensively studied and is classified as an NP-hard problem [8], indicating that solving it requires significant computational resources. As the complexity of the problem increases exponentially with the number of base stations in the network, this challenge becomes even more pronounced in smart cities, where a dense 5G/6G network of base stations is typical. This is because these cities rely on an ultra-dense deployment of base stations, meaning many more stations compared with less urbanized areas, with the goal of providing coverage for all the services and applications that are part of the smart city ecosystem [9; 10]. An efficient management of this interconnection is crucial, as it directly impacts the performance and scalability of proposed solutions.