# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 8 Conclusions and Future Work  
This work introduced a novel Smart 5G Edge-Cloud Management Architecture based on OpenNebula. The proposed architecture incorporates experimental components from the ONEedge5G project which, in its current developmental phase, will incorporate predictive intelligence mechanisms for CPU utilization forecasting and optimization algorithms for the optimal allocation of virtual resources (VMs or containers) on geographically distributed 5G edge infrastructures. This study emphasized infrastructure-level techniques for CPU usage forecasting, employing different statistical and ML time-series forecasting methods. Bayesian regression demonstrated the highest accuracy among the methods evaluated. The optimization problem addressed involved finding the optimal mapping of virtual resources to edge servers using different criteria and constraints. An ILP formulation was proposed for solving this problem. The scenario included a distributed edge infrastructure with physical servers, each with specific computing and memory capacities. Virtual resources with their computing requirements, as well as preferred edge locations based on proximity criteria, were deployed in the infrastructure. The results showed that optimizing different objective functions, such as minimizing the number of servers, reducing latency violations, or balancing server loads, led to improved management of the infrastructure. Allocating virtual resources based on their preferred edge locations without optimization resulted in no latency violations but severe server overloading. However, optimization algorithms successfully prevented overloading, while maintaining an average daily load per server below 100%. By merging various optimization criteria and constraints, such as the number of servers in use and the number of latency violations, different optimized solutions can be obtained. For instance, one approach is to minimize latency violations to a minimum of 29 by utilizing all available servers, while another option is to reduce the number of servers to nine with only 59 latency violations. However, these solutions introduce significant  
Figure 13: Sensitivity analysis results (actual load per core). imbalances in load distribution among servers and may result in overloading on certain servers and time slots. To address this issue, incorporating the load balancing objective function proves effective, as it can achieve solutions with a limited number of latency violations, while improving load distribution and preventing overloading. In our future work, we have various plans to enhance the prediction and optimization models. First, we aim to incorporate additional hardware metrics, including memory, bandwidth, and disk usage, to explore their potential correlation and integrate them into the mathematical models for optimization. Additionally, we plan to propose new optimization criteria based on the previous metrics, as well as integrating different objective functions using various multi-objective approaches. We also intend to investigate alternative optimization techniques, such as bio-inspired algorithms or reinforcement learning algorithms, to further improve the efficiency of the system. Another aspect not addressed in this study but worthy of consideration in future research is the potential utilization of nested virtualization, where containers are not run directly on bare-metal servers but within VMs. In such scenarios, two levels of allocation should be addressed: containers to VMs, and VMs to physical servers. Finally, expanding the capabilities of the ONEedge5G modules is on our agenda, encompassing functionality for capacity planning, prediction, and anomaly detection, as well as proactive auto-scaling mechanisms to facilitate elasticity management. These advancements will contribute to the comprehensive development of our Smart 5G Edge-Cloud Management Architecture and enable more robust and adaptive management of distributed 5G edge infrastructures. All authors participated in the definition of the architecture.