# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 2 Edge Computing and Advanced 5G Networks  
Advanced 5G networks bring a multitude of advantages [9], including significantly faster data speeds, through enhanced mobile broadband (eMBB), ultra-reliable and low-latency communication (URLLC), and support for a large number of connected devices with massive machine-type communication (mMTC). The implementation of network slicing allows customizable network services, tailoring offerings to specific requirements, while technologies like beamforming and MIMO contribute to improved network coverage and efficiency. Deploying edge computing infrastructures on advanced 5G networks offers several key benefits. First, it significantly reduces latency by processing data closer to the source, ensuring quicker response times for applications. This is particularly crucial for real-time applications like augmented reality and autonomous vehicles. Second, edge computing enhances energy efficiency by minimizing data transmission between central clouds and end devices, contributing to a more sustainable network. Third, the proximity of computational resources at the edge ensures improved application performance, particularly for latency-sensitive tasks. The combination of these technologies, along with the growth of the Internet of Things (IoT), has given rise to the emergence of new computing paradigms, such as multi-access edge computing (MEC) [10], which is aimed at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, and low-latency access to radio network resources. According to [11], the main objectives of edge computing in 5G environments are the following: (1) improving data management, in order to handle the large amounts of real-time delay-sensitive data generated by user equipments (UEs); (2) improving quality of service (QoS) to meet diverse QoS requirements, thereby improving the quality of experience (QoE) for applications that demand low latency and high bandwidth; (3) predicting network demand, which involves estimating the network resources required to cater to local proximity network (or user) demand, and subsequently providing optimal resource allocation; (4) managing location awareness, to enable geographically distributed edge servers to infer their own locations and track the location of UEs to support location-based services; and (5) improving resource management, in order to optimize network resource utilization for network performance enhancement in the edge cloud, acknowledging the challenges of catering to diverse applications, user requirements, and varying demands with limited resources compared to the central cloud. Focusing on the last objective, efficient resource management and orchestration in 5G edge computing [12; 13] are crucial as they not only contribute to latency reduction by strategically deploying computing resources, minimizing the time for data processing and enhancing application responsiveness, but also play a pivotal role in optimizing energy consumption through dynamic allocation based on demand, thereby reducing the environmental footprint. Furthermore, proper resource management is essential for performance optimization, preventing bottlenecks, ensuring balanced workload distribution, and maintaining consistent application performance, all of which collectively enhance the overall user experience in these advanced network environments. In this context, AI and ML techniques prove instrumental in addressing these challenges [14; 15; 16; 17; 18]. For example, through predictive analytics and forecasting, ML algorithms can anticipate future demands, enabling proactive resource allocation and strategic deployment for minimized data processing times and reduced latency. AI-driven dynamic allocation can optimize resource utilization and, consequently, energy consumption by adapting to real-time demand patterns.