A Survey on the Contributions of Software-Defined Networking to Traffic Engineering

Since the appearance of OpenFlow back in 2008, software-defined networking (SDN) has gained momentum. Although there are some discrepancies between the standards developing organizations working with SDN about what SDN is and how it is defined, they all outline traffic engineering (TE) as a key application. One of the most common objectives of TE is the congestion minimization, where techniques such as traffic splitting among multiple paths or advanced reservation systems are used. In such a scenario, this manuscript surveys the role of a comprehensive list of SDN protocols in TE solutions, in order to assess how these protocols can benefit TE. The SDN protocols have been categorized using the SDN architecture proposed by the open networking foundation, which differentiates among data-controller plane interfaces, application-controller plane interfaces, and management interfaces, in order to state how the interface type in which they operate influences TE. In addition, the impact of the SDN protocols on TE has been evaluated by comparing them with the path computation element (PCE)-based architecture. The PCE-based architecture has been selected to measure the impact of SDN on TE because it is the most novel TE architecture until the date, and because it already defines a set of metrics to measure the performance of TE solutions. We conclude that using the three types of interfaces simultaneously will result in more powerful and enhanced TE solutions, since they benefit TE in complementary ways.European Commission through the Horizon 2020 Research and Innovation Programme (GN4) under Grant 691567 Spanish Ministry of Economy and Competitiveness under the Secure Deployment of Services Over SDN and NFV-based Networks Project S&NSEC under Grant TEC2013-47960-C4-3-

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin

New concepts for traffic, resource and mobility management in software-defined mobile networks

The evolution of mobile telecommunication networks is accompanied by new demands for the performance, portability, elasticity, and energy efficiency of network functions. Network Function Virtualization (NFV), Software Defined Networking (SDN), and cloud service technologies are claimed to be able to provide most of the capabilities. However, great leap forward will only be achieved if resource, traffic, and mobility management methods of mobile network services can efficiently utilize these technologies. This paper conceptualizes the future requirements of mobile networks and proposes new concepts and solutions in the form of Software-Defined Mobile Networks (SDMN) leveraging SDN, NFV and cloud technologies. We evaluate the proposed solutions through testbed implementations and simulations. The results reveal that our proposed SDMN enhancements supports heterogeneity in wireless networks with performance improvements through programmable interfaces and centralized control

Evaluating Application-Layer Traffic Optimization Cost Metrics for P2P Multimedia Streaming

To help users of P2P communication systems perform better-than-random selection of communication peers, Internet Engineering Task Force standardized the"br/"Application Layer Traffic Optimization (ALTO) protocol. The ALTO provided data-routing cost metric, can be used to rank peers in P2P communication systems. However, the method to derive the data-routing cost metric is undefined by the standard. This paper proposes and evaluates three methods to derive the data-routing cost metric for use in ALTO servers