Bruno A. A. Nunes

Bio: Bruno A. A. Nunes is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Mobility model & Node (networking). The author has an hindex of 10, co-authored 20 publications receiving 2112 citations. Previous affiliations of Bruno A. A. Nunes include University of California, Santa Cruz & University of California, San Francisco.

A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks

Abstract: The idea of programmable networks has recently re-gained considerable momentum due to the emergence of the Software-Defined Networking (SDN) paradigm. SDN, often referred to as a ''radical new idea in networking'', promises to dramatically simplify network management and enable innovation through network programmability. This paper surveys the state-of-the-art in programmable networks with an emphasis on SDN. We provide a historic perspective of programmable networks from early ideas to recent developments. Then we present the SDN architecture and the OpenFlow standard in particular, discuss current alternatives for implementation and testing of SDN-based protocols and services, examine current and future SDN applications, and explore promising research directions based on the SDN paradigm.

Calibration-free WLAN location system based on dynamic mapping of signal strength

Abstract: In this work we present a calibration-free system for locating wireless local area network devices, based on the radio frequency characteristics of such networks. Calibration procedures are applied in a great number of proposed location techniques and are considered to be not practical or a considerable barrier to wider adoption of such methods. Thus, we addressed issues related to some aspects of location systems through, an architecture based on wireless sniffers and by constructing a location model based on signal propagation models, in which its parameters are calculated in real time. This guarantee good self-sufficiency and adaptation capacity to the proposed system, once it does not need human intervention to work, neither from the network administrator or the wireless user being located. Moreover, a probabilistic method was used for estimating wireless devices positions, based on the previous constructed model. We later demonstrate the feasibility of our approach by reporting results of field tests in which the proposed technique was implemented and validated in a real-world indoor environment.

Decentralizing SDN's control plane

Abstract: 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 internet's 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.

A machine learning framework for TCP round-trip time estimation

Abstract: 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.

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

Abstract: In this paper, we discuss User Centric Networks (UCNs) 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 Software Defined Networking (SDN) paradigm to enable cooperation between wireless nodes and provide capacity sharing services in UCNs. Our SDN-based approach allows to extend coverage of existing network infrastructure (such as WiFi or 3GPP) 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 quality-of service (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, etc) and how SDN can aid in enabling such services. The paper also describes the proposed SDN-enabled capacity sharing framework for UCNs.

Software-Defined Networking: A Comprehensive Survey

Abstract: The Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms—with a focus on aspects such as resiliency, scalability, performance, security, and dependability—as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

Software-Defined Networking: A Comprehensive Survey

Abstract: Software-Defined Networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound APIs, network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms -- with a focus on aspects such as resiliency, scalability, performance, security and dependability -- as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment.

Network Function Virtualization: State-of-the-Art and Research Challenges

Abstract: Network function virtualization (NFV) has drawn significant attention from both industry and academia as an important shift in telecommunication service provisioning. By decoupling network functions (NFs) from the physical devices on which they run, NFV has the potential to lead to significant reductions in operating expenses (OPEX) and capital expenses (CAPEX) and facilitate the deployment of new services with increased agility and faster time-to-value. The NFV paradigm is still in its infancy and there is a large spectrum of opportunities for the research community to develop new architectures, systems and applications, and to evaluate alternatives and trade-offs in developing technologies for its successful deployment. In this paper, after discussing NFV and its relationship with complementary fields of software defined networking (SDN) and cloud computing, we survey the state-of-the-art in NFV, and identify promising research directions in this area. We also overview key NFV projects, standardization efforts, early implementations, use cases, and commercial products.

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Abstract: Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges

Abstract: Distributed denial of service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking (SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.