# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 1 Introduction
However, most of the works available concerning MEC focus on traditional video networking functionalities, such as video caching or video streaming [5; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. Conversely, the problem of successfully combining end-user apps and ME apps to provide interactive and immersive video services to consumers on a peer-to-peer basis is a topic that has received little attention from researchers so far. Finally, to provide high-resolution video services, ME apps necessarily originate CPU-intensive workloads. Unfortunately, moving resource-demanding video applications from large data centers to the edge infrastructure can be a difficult task. The IT resources available at the network edge are often subject to strict constraints in terms of power consumption, thermal dissipation, or space occupation, which can severely limit their compute performance. In this case, the use of High-Performance Computing (HPC) devices, such as Graphics Processing Unit(s) (GPUs), could provide significant benefits [25]. Nonetheless, though some results about the use of offloading techniques in virtualization infrastructure are available [22; 25], the analysis of the computing performance achievable by ME systems when performing video-processing tasks has not been thoroughly analyzed yet. As a contribution to the development of an effective MEC in an NFV framework, this paper describes a novel experimental ME system, which can provide some simple and effective solutions to the problems discussed above. At the management level, the proposed system combines some specific MEC functional blocks with a standard NFV Orchestrator (NFVO, [26]) and solves most of the main practical integration issues listed in Reference [10]. Moreover, the resulting architecture can straightforwardly re-use available NFV frameworks and makes it possible to easily upgrade already deployed NFVOs into ME systems, capable of running both standard VNFs and ME apps. As an example, this paper focuses on the Italtel i-Enhanced Video System (EVS), an ME app specifically developed to provide innovative video services to consumers during crowded events [24]. Besides demonstrating the capabilities of the system to manage ME apps, the use of i-EVS also highlights the advantages that MEC can provide compared to a standard NFV framework in the provision of an immersive video-processing appliance. The i-EVS services can be easily accessed through any browser. Nonetheless, to improve consumers' Quality of Experience (QoE), an ad hoc end-user app downloadable on end-user devices has been developed, to demonstrate how an end-to-end integrated service can fully exploit the potentialities offered by MEC for peer-to-peer media content sharing. A prototypal version of the proposed architecture has been implemented in a real-life testbed, and integrated into a 4G Long Term Evolution (LTE) network, by adopting a \"Distributed SGW with Local Breakout (SGWLBO)\" approach, which can evolve to a 5G network architecture [27]. The virtualization infrastructure has been realized using portable servers hosting GPUs, to achieve the required compute performance and satisfy the energy consumption and space occupation constraints typically encountered at the network edge. In particular, the use of GPUs is analyzed, and the gain they can provide in terms of processing performance, energy consumption, and space saving quantitatively is evaluated through ad hoc experiments. The structure of the paper is as follows. The next section introduces the main MEC and NFV concepts and summarizes the state of the art in MEC-NFV integration. Moreover, it provides a survey of traditional video-processing services at the network edge, to highlight the advances made possible by the proposed approach. Section 3 describes the presented ME system architecture and analyzes in detail two fundamental ME app management activities, i.e., on-boarding and instantiation, to show how the main MEC in NFV integration issues can be overcome.