Decentralized Solutions for Monitoring Large-Scale Software-Defined Networks

Software-Defined Networking (SDN) technologies offer the possibility to automatically and frequently reconfigure the network resources by enabling simple and flexible network programmability. One of the key challenges to address when developing a new SDN-based solution is the design of a monitoring framework that can provide frequent and consistent updates to heterogeneous management applications. To cope with the requirements of large-scale networks (i.e. large number of geographically dispersed devices), a distributed monitoring approach is required. This PhD aims at investigating decentralized solutions for resource monitoring in SDN. The research will focus on the design of monitoring entities for the collection and processing of information at different network locations and will investigate how these can efficiently share their knowledge in a distributed management environment

Global state, local decisions: Decentralized NFV for ISPs via enhanced SDN

The network functions virtualization paradigm is rapidly gaining interest among Internet service providers. However, the transition to this paradigm on ISP networks comes with a unique set of challenges: legacy equipment already in place, heterogeneous traffic from multiple clients, and very large scalability requirements. In this article we thoroughly analyze such challenges and discuss NFV design guidelines that address them efficiently. Particularly, we show that a decentralization of NFV control while maintaining global state improves scalability, offers better per-flow decisions and simplifies the implementation of virtual network functions. Building on top of such principles, we propose a partially decentralized NFV architecture enabled via an enhanced software-defined networking infrastructure. We also perform a qualitative analysis of the architecture to identify advantages and challenges. Finally, we determine the bottleneck component, based on the qualitative analysis, which we implement and benchmark in order to assess the feasibility of the architecture.Peer ReviewedPostprint (author's final draft

Decentralized monitoring for large-scale Software-Defined Networks

The Software-Defined Networking (SDN) paradigm can allow network management solutions to automatically and frequently reconfigure network resources. When developing SDN-based management architectures, it is of paramount importance to design a monitoring system that can provide frequent and consistent updates to heterogeneous management applications. For the monitoring functionality to scale according to the requirements of large-scale networks a distributed monitoring approach is required. In this paper we present a decentralized approach for resource monitoring in SDN, which is designed to support a wide range of measurement tasks and requirements in terms of monitoring rates and information granularity levels. Our solution leverages effective processing of the monitoring requests to reduce the consumption of limited resources, such as the control plane bandwidth of OpenFlow switches. To demonstrate the benefits of the proposed approach, our evaluation is based on a realistic and demanding use case, where a distributed management application coordinates a content distribution service in an ISP network

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