The Origin and Evolution of Open Programmable Networks and SDN

Czachórski

2021

Sensors

The increasing use of Software-Defined Networks brings the need for their performance analysis and detailed analytical and numerical models of them. The primary element of such research is a model of a SDN switch. This model should take into account non-Poisson traffic and general distributions of service times. Because of frequent changes in SDN flows, it should also analyze transient states of the queues. The method of diffusion approximation can meet these requirements. We present here a diffusion approximation of priority queues and apply it to build a more detailed model of SDN switch where packets returned by the central controller have higher priority than other packets.

Section: Introduction and An Overview Of Existing Resultsmentioning

confidence: 99%

Diffusion Model of Preemptive-Resume Priority Systems and Its Application to Performance Evaluation of SDN Switches

Czachórski

2021

Sensors

“…Recently, several research efforts have focused on the adoption of the novel paradigm of software-defined networking (SDN) for the efficient management of NDNs [7], [20], [21]. In SDN, network control logic and algorithms are executed at the control-plane, which then communicates a set of forwarding rules directly to the switch data-plane using protocols such as OpenFlow.…”

Section: B Data-plane Programmabilitymentioning

confidence: 99%

“…Network operators are envisioned to take advantage of NDN switch functionalities to offer novel per-application, per-content, or per-consumer highly customized network services such as time-sensitive delivery using prefetching and caching, semantics-based forwarding, and content encryption and decryption [6]. This vision is motivated by emerging technologies, such as software-defined networks [7], that succeeded in separating the network control functionality from that of the data-plane packet forwarding process. More recently, the emergence of switch programming languages, such as P4 [8], has enabled the notion of programmable data-planes (PDPs).…”

Section: Introductionmentioning

confidence: 99%

Mapping Applications Intents to Programmable NDN Data-Planes via Event-B Machines

2022

Location-agnostic content delivery, in-network caching, and native support for multicast, mobility, and security are key features of the novel named data networks (NDN) paradigm. NDNs are ideal for hosting content-centric next-generation applications such as Internet of things (IoT) and virtual reality. Intent-driven management is poised to enhance the performance of the offered NDN services to these applications while reducing its management complexity. This article proposes I2DN, intent-driven NDN, a novel architecture that aims at realizing the first step towards intent modeling and mapping to data-plane configurations for NDNs. In I2DN, network operators and application developers express their abstract and declarative content delivery and network service goals and constraints using uttered or written intents. The intents are classified using built-in intent templates, and a slot filling procedure identifies the semantics of the intent. We then employ Event-B machine (EBM) language modeling to represent these intents and their semantics. The resulting EBMs are then gradually refined to represent configurations at the NDN programmable data-plane. The advantages of the proposed adoption of EBM modeling are twofold. First, EBMs accurately capture the desired behavior of the network in response to the specified intents and automatically refine it into concrete configurations. Second, EBM's formal verification property, referred to as its proof obligation, ensures that the desired properties of the network or its services, as defined by the intent, remain satisfied by the refined EBM representing the final data-plane configurations. Experimental evaluation results demonstrate the feasibility and efficiency of our proposed work.

Intelligent Automation &Amp; Soft Computing

“…A softwaredefined network (SDN) is a new form of network that permits the detachment of control and data planes in addition to the centralization of control in one place. This separation offers more flexibility for the network administration and effective usage of network resources [1]. The control plane helps secure the network by producing a global overview of the network state.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning Models for Mitigating DDoS Attack in Software-Defined Network

Alanazi¹,

Jambi²,

Eassa³

et al. 2022

Software-defined network (SDN) is an enabling technology that meets the demand of dynamic, adaptable, and manageable networking architecture for the future. In contrast to the traditional networks that are based on a distributed control plane, the control plane of SDN is based on a centralized architecture. As a result, SDNs are susceptible to critical cyber attacks that exploit the single point of failure. A distributed denial of service (DDoS) attack is one of the most crucial and risky attacks, targeting the SDN controller and disrupting its services. Several researchers have proposed signature-based DDoS mitigation and detection techniques that rely on manually configuring the policies. As the massive traffic from heterogeneous networks increases, conventional solutions are ineffective due to the lack of automation and human interference. This necessitates producing a detection solution, more effective than traditional ones, to ensure SDN security, resiliency, and availability. This paper addresses this problem by proposing a deep learning (DL)-based ensemble solution for efficient DDoS attack detection in SDN. Four hybrid models are presented by adopting three ensemble techniques and different DL architectures, namely convolutional neural network, long short-term memory, and gated recurrent unit, to improve the SDN traffic classification. The experimentation was conducted on the benchmark flow-based dataset CICIDS2017. High detection accuracy (99.77%) with a small number of flow-based features was achieved by our ensemble model, as our experimental results will demonstrate. The proposed solution was evaluated by several standard assessment matrices and by comparing against other state-of-the-art algorithms from the network security literature.