# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 1 Introduction  
The evolution of 5G technology has ushered in a new era of connectivity, offering enhanced capabilities that extend beyond traditional communication paradigms. Advanced 5G networks facilitate a diverse range of applications, characterized by their complexity, computational demand, and low-latency requirements. These applications, designed to leverage the capabilities of advanced 5G, exhibit a hybrid profile, relying on resources spanning the spectrum from data centers to the cloud to the edge. In particular, the deployment of edge infrastructure in 5G environments is crucial to addressing the unique challenges posed by latency-sensitive and data-intensive applications [1, 2]. The next generation of applications, encompassing smart IoT applications, real-time analytics, machine learning applications, and more, require intelligent orchestration of resources in the edge domain, where computational tasks are strategically placed closer to data sources to minimize latency and enhance overall system performance. Orchestrating edge infrastructure in 5G environments is not without its complexities. The heterogeneous nature of edge devices, coupled with the dynamic and resource-constrained characteristics of these environments, presents challenges for effective resource management. Unlike traditional cloud-centric models, the edge requires a nuanced approach that considers factors such as reliability, security, data protection, and energy efficiency. In light of these challenges, the need for intelligent orchestration becomes paramount. An orchestration system driven by artificial intelligence (AI) and machinelearning (ML) techniques can dynamically allocate and coordinate resources across a distributed edge infrastructure. In the current cloud market, various vendors and tools claim to employ intelligent techniques for resource management and monitoring. For instance, VMware implements a predictive distributed resource scheduler (DRS) [3] for cloud platforms, which forecasts future demand and preemptively addresses potential hot spots, by reallocating workloads well in advance of any contention. OPNI [4], an open source project from SUSE, is another noteworthy example aimed at enhancing observability, monitoring, and logging in Kubernetes-based clusters. It offers a range of AIOps tools for detecting log anomalies, identifying root causes, and spotting metric irregularities. Google Active Assist [5] is another tool that aims to provide intelligent solutions for improving cloud operations, by offering recommendations for cost reduction, performance enhancement, security improvement, and sustainability. Generally, these prediction and optimization techniques deployed by cloud stakeholders are tailored for centralized cloud platforms and often comprise simple, proprietary solutions that may not be adaptable to highly distributed 5G edge environments. On the other hand, recent initiatives like OpenNebula OneEdge [6] enable the deployment and management of geo-distributed edge/cloud infrastructures, leveraging resources from various public cloud and edge infrastructure providers. However, these tools are still nascent and primarily offer basic management functionalities rather than advanced, intelligent orchestration capabilities for optimizing the deployment of large scale edge infrastructures. In this work, we propose a pioneering Smart 5G Edge-Cloud Management Architecture, which seeks to expand the existing edge management platforms by integrating intelligent orchestration capabilities. This integration aims to automate and optimize the provisioning and deployment of geographically distributed 5G edge infrastructures. This architecture, built upon the foundation of OpenNebula [7; 8], will integrate cutting-edge experimental components under development in the ONEedge5G project, as shown in Figure 1. ONEedge5G aims to enable efficient capacity planning, provisioning, and risk prevention in geographically distributed edge infrastructures and applications within the context of advanced 5G networks.