The convergence of 5G and Edge computing has revolutionized the technology landscape, ushering in a new era of innovative use cases and accelerating the realization of an intelligent, fully connected digital world at an unprecedented pace. However, the demanding and ever-changing specifications of these emerging applications present significant challenges in terms of orchestration, requiring innovative solutions and efficient management strategies to ensure optimal performance and resource utilization. The mobility of end-users is a critical factor expected to significantly impact Edge operations. As a result, Edge-enabled 5G systems face the daunting task of tracking moving users and meeting their Quality of Experience (QoE) demands simultaneously, which presents a formidable technical challenge that must be addressed for the realization of a truly seamless and ubiquitous user experience. In this paper, we put forward a novel solution based on Reinforcement Learning to optimize the relocation of applications between Edge Hosts while meeting their demands in terms of latency and resource requirements. Our proposed solution aims to achieve a balance between two key objectives: maintaining the Quality of Service (QoS) of Edge Applications and optimizing the utilization of infrastructure resources. By achieving this balance, we can ensure that Edge-enabled 5G systems operate efficiently, with minimal resource wastage and maximal QoS satisfaction for end-users. The intensive simulations and evaluation results demonstrate its effectiveness in terms of QoS assurance and resource load bnlancing.

The convergence of 5G and Edge computing is fostering the development of innovative use cases making the dream of a fully connected, intelligent digital world almost true. However, the stringent requirements coupled to the high dynamicity of these new applications make their orchestration extremely challenging. Specifically, the mobility of end users will undeniably impact Edge operations. Indeed, the Edge-enabled 5G systems need to provide the capability to follow moving users while respecting their latency requirements. To achieve such a goal, we propose, in this paper, an innovative solution optimizing the relocation of applications between Edge clusters while respecting their requirements in terms of latency and resources requests. In doing so, our solution is capable of ensuring the QoS of Edge applications while optimizing the infrastructure resource consumption. Performance evaluation in real platform put forward the efficiency of our solution in terms of (i) QoS assurance, (ii) resource load balancing and, (iii) convergence time.

The European Telecommunications Standards Institute (ETSI) established the key reference architectural framework for Network Function Virtualization (NFV). The NFV framework supports several phases (e.g., design, deploy, and manage) that make up the life-cycle of Virtual Network Functions (VNFs) along with their connectivity through the Network Service (NS) concept. Furthermore, coupled with Software-Defined Networking (SDN), NFV provides a dynamic reshaping of the network control plane. For a proper NS provisioning, network providers should anticipate and handle all the possible and practical VNFs composition scenarios, even those that are either rarely or never used. This results in costly, tedious, and time-consuming procedures when designing, deploying, and managing NSs. To overcome these limitations, NSs can be further improved by using techniques that have not been deeply explored yet. In this paper, we advocate for routing VNFs with re-configurable wiring capabilities as a novel concept that would allow agile and dynamic VNFs wiring from design-time to run-time. To validate this work, we developed a proof-of-concept as a plugin on top of ETSI Open Source MANO, as well as, a running vehicle platooning prototype. The performed experiments show that our approach: (i) performs better than the SDN-based solution in terms of latency and Mean Time-To-Operation (MTTO), and (ii) shows that the associated cost, with regard to SDN-based solution. remains acceptable.