Abstract. Network Functions Virtualization (NFV) is an emerging initiative where virtualization is used to consolidate Network Functions (NFs) onto high volume servers (HVS), switches, and storage. In addition, NFV provides flexibility as Virtual Network Functions (VNFs) can be moved to different locations in the network. One of the major challenges of NFV is the allocation of demanded network services in the network infrastructures, commonly referred to as the Network Functions Virtualization -Resource Allocation (NFV-RA) problem. NFV-RA is divided into three stages: (i) Service Function Chain (SFC) composition, (ii) SFC embedding and (iii) SFC scheduling. Up to now, existing NFV-RA approaches have mostly tackled the SFC embedding stage taking the SFC composition as an assumption. Few approaches have faced the composition of the SFCs using heuristic approaches that do not guarantee optimal solutions. In this paper, we solve the first stage of the problem by characterizing the service requests in terms of NFs and optimally building the SFC using an Integer Linear Programming (ILP) approach.

Mobile broadband networks, although increasingly popular, suffer large fluctuations in performance. Download speeds can drop by 50% or more during peak hours. Hence, understanding and dissecting the causes of these fluctuations is central to improving current and future networks. In this paper, we propose a congestion detection and localisation method, Q-TSLP, that combines and extends the two state-of-the-art congestion detection tools: Q-Probe and TSLP. Q-Probe monitors patterns in packet arrivals, while TSLP tracks shifts in RTT to detect bottleneck at different segments of an end-to-end path. QProbe can attribute congestion, at a very coarse level, to either radio or non-radio related. TSLP on the other hand cannot pinpoint radio related conegstion. QTSLP provides a per-hop congestion attribution thus addressing these limitations.To this end, we build two small scale LTE testbeds and experiment with a series of congestion scenarios. These controlled experiments show that apart from correct congestion localisation to finer granularity, the detection accuracy improves significantly with Q-TSLP, up to 100% in some cases. We then run a three-month long measurement campaign of congestion over two commercial operators in Norway. Overall, we run 17 million tests from a large number of geographically distributed probes. We find that both operators suffer congestion at different parts of the network. Our findings indicate that apart from mobile radio access, a non-trivial fraction of cases is related to congested mobile operator and Internet paths beyond the mobile network core. These findings hint that operators may need significant infrastructure upgrades to cope with potential 5G traffic volumes.

Network Function Virtualization is a key enabler to building future mobile networks in a flexible and cost-efficient way. Such a network is expected to manage and maintain itself with minimum human intervention. With early deployments of the fifth generation of mobile technologies -5G -around the world, setting up 4G/5G experimental infrastructure is necessary to optimally design Self-Organising Networks (SON). In this demo, we present a custom small-scale 4G/5G testbed. As a step towards self-healing, the testbed integrates Programming Protocol-independent Packet Processors (P4) virtual switches, that are placed along interfaces between different components of transport and core network. This demo not only shows the administration and monitoring of the Evolved Packet Core VNF components, using OPEN SOURCE MANO, but also serves as a proof of concept for the potential of P4-based telemetry in detecting anomalous behaviour of the mobile network, such as a congestion in the transport part.

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