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The network function virtualization paradigm replaces dedicated hardware appliances with software running on virtual machines in commodity servers. However, to overcome performance and scalability issues on virtual network functions (VNFs) demanding intensive packet processing, we propose implementing these VNFs as programmable rules distributed among software-defined networking (SDN) switches. Our approach allows infrastructure providers to combine hardware-based and software-based SDN switches, exploring the tradeoff between the packet processing speed and instantiation flexibility. Our proposal includes a scalable mechanism, which decides the type and number of switches to meet network demand, along with a load balancing mechanism to distribute flows among switches, considering their singularities. We first evaluate our proposal in a small testbed with real equipment, confirming its effectiveness. Then, we evaluate our proposal with simulations to further investigate its scalability. Our results show that, even under high traffic loads, the joint operation of both mechanisms improves VNF capacity and saves virtualization resources.

O crescente número de dispositivos conectados à Internet das coisas e a já conhecida fragilidade dos meios de comunicação sem fio demandam por soluções de segurança robustas para mitigar ataques maliciosos nestas redes. Com foco na reprodutibilidade de pesquisas, este artigo apresenta o RSSignal: um arcabouço para geração e validação de chaves a partir do indicador de sinal RSSI. A implementação atual do arcabouço compreende a geração de chaves criptográficas simétricas para redes LoRaWAN a partir de conjuntos de dados reais, e a posterior validação dessas através da suíte de testes NIST 800-22. O RSSignal está disponível como software livre para que pesquisadores possam reproduzir os experimentos e propor melhorias ao processo.

Implementar uma função virtual de rede através de regras distribuídas entre comutadores SDN programáveis possibilita a adoção eficiente do NFV em cenários que necessitem de processamento escalável de pacotes. Nesse sentido, propomos um arcabouço de balanceamento dinâmico de carga para funções virtualizadas sobre comutadores OpenFlow heterogêneos, acompanhado de duas políticas de balanceamento. A política estática distribui novos fluxos aleatoriamente entre os comutadores. A política dinâmica considera as especificidades dos comutadores (em hardware e em software) na alocação inicial dos fluxos e posterior realocação dos mesmos, para melhor aproveitamento dos recursos disponíveis. Avaliamos o arcabouço experimentalmente, sob diferentes níveis de carga. Os resultados confirmam a eficiência do arcabouço mesmo em um cenário com sobrecarga de tráfego, há um aumento da capacidade de processamento da função virtual em ≈ 27%.

Green computing is a central theme in many computer science areas, including computer networks. Dynamic solutions that can properly adjust network resources can prevent infrastructure over-provision and mitigate power consumption during low-demand periods. In this work, we propose DTM (Dynamic mechanism for Traffic Management), an energy-aware dynamic mechanism for traffic management, built upon the SDN paradigm. DTM continuously monitors the use of network links to concentrate traffic and disconnect idle equipment without degrading the offered quality of service. Our simulations show that the mechanism can save up to 46% of energy, on average, in the links’ capacities of homogeneous and heterogeneous scenarios. In scenarios with average to high traffic demands, the mean energy savings are 36.72% and 17.86%, respectively. Compared to a well-known existing mechanism, our approach is up to 7% better for medium-demand scenarios, and approximately 4% better for high-demand scenarios.

Wireless local area networks have become vastly popular, and IEEE 802.11 is the chosen standard for almost all wireless devices. This standard specifies several modulation and channel coding techniques that must be implemented by all wireless interfaces to adapt to changes in wireless channels. As a result, these interfaces support multiple transmission data rates. However, this standard does not define how to dynamically select the appropriate data rate; instead, manufacturers can design and implement their own algorithms. Although several solutions have been proposed in the literature, only a few are used in practice. Moreover, their performance is still limited to specific conditions, such as highly dynamic environments. To tackle these challenges, this paper introduces CogTRA, which is a deployable mechanism built upon an existing cognitive framework called CogProt. Due to its self-adjustment functionality, CogTRA can work not only in stable but also in dynamic environments. It was implemented in the OpenWrt Linux distribution for embedded devices and evaluated through experiments using real network equipment. The results underline performance benefits with respect to existing data rate adaptation algorithms, with CogTRA exhibiting better performance especially in such dynamic networks.

The growing data traffic demand is forcing network operators to deploy more base stations, culminating in dense heterogeneous networks that require a high-connectivity backhaul. This scenario imposes significant challenges for current and future cellular networks, and Software Defined Networking (SDN) has been pointed as an enabling technology to overcome existing limitations. This paper shows how the OpenFlow protocol can be integrated into existing Long Term Evolution (LTE) networks to provide the required Quality of Service (QoS) in the network infrastructure. Three OpenFlow-based mechanisms are proposed: a traffic routing, an admission control function, and a traffic coexistence mechanism. Together, they can effectively control the bandwidth usage in the backhaul infrastructure, improving the QoS and ensuring a better user experience. Simulations were performed to validate the proposed mechanisms and highlight the benefits that can be achieved with the flexibility offered by the SDN technology.

The world is witnessing the rapid evolution of communication technologies, and meeting current market requirements is virtually impossible with traditional network architectures. Many works point to the use of Software Defined Networking (SDN) paradigm and the OpenFlow protocol as enabling solutions to overcome current limitations. Despite the fact that the Network Simulator 3 (ns-3 ) already has a module that supports OpenFlow simulations, it is possible to note that the available implementation provides a very outdated protocol version (0.8.9). As many new major features were introduced up to the latest versions, it is interesting to have some of them available for use. In this context, this paper presents the OFSwitch13: a module to enhance the ns-3 with OpenFlow 1.3 technology support. This module provides both an OpenFlow 1.3 switch device and a controller application interface. Details about module design and implementation are discussed throughout this paper, and a case study scenario is used to illustrate some of the available OFSwitch13 module features.

The world is witnessing a rapid growth in mobile communication, and operators have been forced to handle network traffic more resourcefully. Many works point to the use of Software-Defined Networking (SDN) as an enabling technology to overcome current limitations, making possible upcoming 5G networks. In this paper we discuss how the OpenFlow protocol can be integrated into existing Long-Term Evolution (LTE) networks, going through state-of-the-art solutions and highlighting open issues. We also introduce a novel OpenFlow module for ns-3 simulator, allowing reliable simulations of LTE-SDN integrated networks, as illustrate in a case study example.

Modern large-scale grid computing systems for processing advanced science and engineering applications rely on geographically distributed clusters. In such highly distributed environments, estimating the available bandwidth between clusters is a key issue for efficient task scheduling. We analyze the performance of two well known available bandwidth estimation tools, pathload and abget, with the aim of using them in grid environments. Differently than previous investigations (Jain et al., http://www.caida.org/workshops/isma/0312/slides/rprasad-best.pdf ; Shriram et al., in Passive and active network measurement: 6th international workshop, PAM 2005. Springer, Berlin, 2005), our experiments consider a series of relevant metrics such as accuracy of the estimation, convergence time, degree of intrusion in the grid links, and ability to handle multiple simultaneous estimations. No previous work has analyzed the use of available bandwidth tools for the derivation of efficient grid scheduling.

Sophisticated wireless interfaces support multiple transmission data rates and the selection of the optimal data rate has a critical impact on the overall network performance. Proper rate adaptation requires dynamically adjusting data rate based on current channel conditions. Despite several rate adaptation algorithms have been proposed in the literature, there are still challenging issues related to this problem. The main limitations of current solutions are concerned with how to estimate channel quality to appropriately adjust the rate. In this context, we propose a Cognitive Rate Adaptation mechanism for wireless networks. This mechanism includes a distributed self-configuration algorithm in which the selection of data rate is based on past experience. The proposed approach can react to changes in channel conditions and converge to the optimal data rate, while allowing a fair channel usage among network nodes. Simulation results obtained underline performance benefits with respect to existing rate adaptation algorithms.