NFV Orchestrator Placement for Geo-Distributed Systems

The European Telecommunications Standards Institute (ETSI) developed Network Functions Virtualization (NFV) Management and Orchestration (MANO) framework. Within that framework, NFV orchestrator (NFVO) and Virtualized Network Function (VNF) Manager (VNFM) functional blocks are responsible for managing the lifecycle of network services and their associated VNFs. However, they face significant scalability and performance challenges in large-scale and geo-distributed NFV systems. Their number and location have major implications for the number of VNFs that can be accommodated and also for the overall system performance. NFVO and VNFM placement is therefore a key challenge due to its potential impact on the system scalability and performance. In this paper, we address the placement of NFVO and VNFM in large-scale and geo-distributed NFV infrastructure. We provide an integer linear programming formulation of the problem and propose a two-step placement algorithm to solve it. We also conduct a set of experiments to evaluate the proposed algorithm.Comment: This paper has been accepted for presentation in 16th IEEE International Symposium on Network Computing and Applications (IEEE NCA 2017

On the Topology of a Large-scale Urban Vehicular Network

Despite the growing interest in a real-world deployment of vehicle-to-vehicle communication, the topological features of the resulting vehicular network remain largely unknown. We lack a clear under- standing of the level of connectivity achievable in large-scale scenarios, the availability and reliability of connected multi-hop paths, or the impact of daytime. In this paper, we adopt a complex network approach to provide a first characterization of a realistic large-scale urban vehicular ad hoc network. We unveil the low connectivity, availability, reliability and navigability of the network, and exploit our findings to derive network design guidelines

A Virtual Network PaaS for 3GPP 4G and Beyond Core Network Services

Cloud computing and Network Function Virtualization (NFV) are emerging as key technologies to overcome the challenges facing 4G and beyond mobile systems. Over the last few years, Platform-as-a-Service (PaaS) has gained momentum and has become more widely adopted throughout IT enterprises. It simplifies the applications provisioning and accelerates time-to-market while lowering costs. Telco can leverage the same model to provision the 4G and beyond core network services using NFV technology. However, many challenges have to be addressed, mainly due to the specificities of network services. This paper proposes an architecture for a Virtual Network Platform-as-a-Service (VNPaaS) to provision 3GPP 4G and beyond core network services in a distributed environment. As an illustrative use case, the proposed architecture is employed to provision the 3GPP Home Subscriber Server (HSS) as-a-Service (HSSaaS). The HSSaaS is built from Virtualized Network Functions (VNFs) resulting from a novel decomposition of HSS. A prototype is implemented and early measurements are made.Comment: 7 pages, 6 figures, 2 tables, 5th IEEE International Conference on Cloud Networking (IEEE CloudNet 2016

Towards Energy Efficiency in RAN Network Slicing

Network slicing is one of the major catalysts to turn future telecommunication networks into versatile service platforms. Along with its benefits, network slicing is introducing new challenges in the development of sustainable network operations. In fact, guaranteeing slices requirements comes at the cost of additional energy consumption, in comparison to non-sliced networks. Yet, one of the main goals of operators is to offer the diverse 5G and beyond services, while ensuring energy efficiency. To this end, we study the problem of slice activation/deactivation, with the objective of minimizing energy consumption and maximizing the users quality of service (QoS). To solve the problem, we rely on two Multi-Armed Bandit (MAB) agents to derive decisions at individual base stations. Our evaluations are conducted using a real-world traffic dataset collected over an operational network in a medium size French city. Numerical results reveal that our proposed solutions provide approximately 11-14\% energy efficiency improvement compared to a configuration where all the slice instances are active, while maintaining the same level of QoS. Moreover, our work explicitly shows the impact of prioritizing the energy over QoS, and vice versa

Human Mobility Flows in the City of Abidjan

International audienceThe growing ubiquity of mobile communications has offered researchers new possibilities to understand human mobility over the last few years. In this work, we analyze Call Detail Records (CDR) made available within the context of the Orange D4D Challenge, focusing on calls of individuals in the city of Abidjan over a period of five months. Our results illustrate how aggregated CDR can be used to tell apart typical and special mobility behaviors, and demonstrate how macroscopic mobility flows extracted from these cellular network data reflect the daily dynamics of a highly populated city. We discuss how these macroscopic mobility flows can help solve problems in developing urban areas

Poster: Privacy-Aware Decentralized Multi-Slice Traffic Forecasting

International audienceIn this work, taking the perspective of Mobile Virtual Network Operators (MVNOs), we tackle the multi-slice traffic forecasting problem, while respecting the data privacy of users. To this end, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train at each base station their local models with their private datasets, without compromising data privacy. Prediction results obtained by evaluating the models on a real-world dataset indicate that the forecast of FPLSTM is as accurate as state-of-the-art solutions while ensuring data privacy as well as computation and communication costs efficiency

Mobile Traffic Forecasting for Network Slices: A Federated-Learning Approach

International audienceNetwork slicing is one of the cornerstones for next-generation mobile communication systems. Specifically, it enables Mobile Virtual Network Operators (MVNOs) to offer various types of services over the same physical infrastructure owned by an Infrastructure Provider (InP). To satisfy the dynamic user requirements and ensure resource efficiency, MVNOs need to estimate the future traffic demand in advance, to pre-allocate/reconfigure the resources at the base stations. However, this per-slice traffic forecasting exploits information that is clearly sensitive for the MVNOs from a business point of view, and which might even disclose private data regarding some users. Hence, it is vital for MVNOs to ensure data privacy while conducting traffic forecasting. Bearing this in mind, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train their local models with their private dataset at each base station without compromising data privacy. Simultaneously, an InP global model is updated through the aggregation of local models weights. Prediction results obtained by training the models on a real-world dataset indicate that the forecasting performance of FPLSTM is as accurate as state-of-the-art solutions, while ensuring data privacy, computation and communication cost efficiency