Bruno Astuto A. Nunes

Decentralizing SDN's control plane

Motivated by the internets of the future, which will likely be considerably larger in size as well as highly heterogeneous and decentralized, we propose Decentralize-SDN, D-SDN, a framework that enables not only physical- but also logical distribution of the Software-Defined Networking (SDN) control plane. D-SDN accomplishes network control distribution by defining a hierarchy of controllers that can “match” an internets organizational- and administrative structure. By delegating control between main controllers and secondary controllers, D-SDN is able to accommodate administrative decentralization and autonomy.It incorporates security as an integral part of the framework. This paper describes D-SDN and presents two use cases, namely network capacity sharing and public safety network services.

Software-defined-networking-enabled capacity sharing in user-centric networks

In this article, we discuss user-centric networks as a way of, if not completely solving, considerably mitigating the problem of sharing limited network capacity and resources efficiently and fairly. UCNs are self-organizing networks where the end user plays an active role in delivering networking functions such as providing Internet access to other users. We propose to leverage the recently proposed SDN paradigm to enable cooperation between wireless nodes and provide capacity sharing services in UCNs. Our SDNbased approach allows coverage of existing network infrastructure (e.g., WiFi or 3GPP) to be extended to other end users or ad hoc networks that would otherwise not be able to have access to network connectivity and services. Moreover, the proposed SDN-based architecture also takes into account current network load and conditions, and QoS requirements. Another important feature of our framework is that security is an integral part of the architecture and protocols. We discuss the requirements for enabling capacity sharing services in the context of UCNs (e.g., resource discovery, node admission control, cooperation incentives, QoS, security) and how SDN can aid in enabling such services. The article also describes the proposed SDN-enabled capacity sharing framework for UCNs.

Software-defined networking based capacity sharing in hybrid networks

This paper proposes a novel approach to capacity sharing in hybrid networked environments, i.e., environments that consist of infrastructure-based as well as infrastructureless networks. The proposed framework is based on Software-Defined Networking (SDN) and provides flexible, efficient, and secure capacity sharing solutions in a variety of hybrid network scenarios. In this paper, we describe the challenges raised by capacity sharing in hybrid networks, describe our framework in detail and how it addresses these challenges, and discuss implementation issues. To the best of our knowledge, this is the first SDN-based capacity sharing solution that targets hybrid networks and that incorporates security as an integral part of the proposed approach.

A machine learning framework for TCP round-trip time estimation

In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the experts framework. In our proposal, each of several ‘experts’ guesses a fixed value. The weighted average of these guesses estimates the RTT, with the weights updated after every RTT measurement based on the difference between the estimated and actual RTT.Through extensive simulations, we show that the proposed machine-learning algorithm adapts very quickly to changes in the RTT. Our results show a considerable reduction in the number of retransmitted packets and an increase in goodput, especially in more heavily congested scenarios. We corroborate our results through ‘live’ experiments using an implementation of the proposed algorithm in the Linux kernel. These experiments confirm the higher RTT estimation accuracy of the machine learning approach which yields over 40% improvement when compared against both standard transmission control protocol (TCP) as well as the well known Eifel RTT estimator. To the best of our knowledge, our work is the first attempt to use on-line learning algorithms to predict network performance and, given the promising results reported here, creates the opportunity of applying on-line learning to estimate other important network variables.

SCORPION: a heterogeneous wireless networking testbed

During the last decade, the success and popularity of wireless standards such as IEEE 802.11 have drawn the attention of the research community to wireless networks. A great amount of effort has been invested into research in this area, most of which relies heavily on simulation and analysis techniques. However, simulations do not precisely control hardware interrupts, packet timing and real physical and MAC layer behaviors. As a result, simulation results need to be validated by real implementations, which is evident by the change in focus of research activities increasingly moving towards real implementations, including the deployment of testbeds as a main tool to analyze network protocol functionality. Under this context, we present an overview of SCORPION (Santa Cruz mObile Radio Platform for Indoor and Outdoor Networks), a heterogeneous wireless networking testbed that includes a variety of nodes ranging from ground vehicles to autonomous aerial vehicles. The purpose of SCORPION to is to deploy and investigate nascent networking protocols using a variety of mobile platforms utilizing structured as well as unstructured mobility patterns.

On the symmetry of user mobility in wireless networks

In this paper analyzed WLAN-, GPS-, and synthetic traces that record mobility in a variety of network environments. We observe that from a macroscopic level, human mobility is symmetric. In other words, the number of users that move from point A to point B approximates the number of users that go in the opposite direction, i.e., from B to A. We show that this type of symmetry is more accentuated in synthetic mobility models, in particular, in random way-point mobility. We also study the direction of movement which also exhibits symmetric behavior in both real- as well as synthetic mobility. Additional contributions of our work include metrics to quantify mobility symmetry. We conclude the paper with a discussion of possible applications of our results in mobile networking.

Modeling spatial node density in waypoint mobility

This paper introduces a modeling framework to analyze spatial node density in mobile networks under “waypoint”-like mobility regimes. The proposed framework is based on a set of first order ordinary differential equations (ODEs) that take as parameters (1) the probability of going from one subregion of the mobility domain to another and (2) the rate at which a node decides to leave a given subregion. We validate our model by using it to describe the steady-state behavior of real user mobility recorded by GPS traces in different scenarios. To the best of our knowledge, this is the first node density modeling framework generic enough that can be applied to any “waypoint”-based mobility regime.

On the Heavy Tail Properties of Spatial Node Density for Realistic Mobility Modeling

In this paper, we show empirically that the spatial node density resulting from human mobility follows a power law. We also show that the number of locales visited by users also exhibit heavy-tail behavior. We develop a stochastic model that confirms our empirical observations by showing that node mobility resulting from our model closely approximates mobility recorded in real traces collected from a variety of scenarios. Besides corroborating our empirical observations, we showcase another application of our model by using it to generate mobility regimes whose spatial node density exhibit heavy-tail behavior. We validate the resulting mobility generator by comparing its output against real traces.