Software Defined Networking Capabilities Research Articles

BCSDNCC: A Secure Blockchain SDN framework for IoT and Cloud Computing

Rapid progress can be observed in the field of computer network technologies. Blockchain technology(BCT) presents a potentially viable alternative for effectively mitigating performance and security issues encountered in distributed systems. Recent studies have focused on exploring a number of exciting new technologies, including BlockChain (BC), Software-Defined Networking (SDN), and the Internet of Things (IoT). Various technologies offer data integrity and secrecy. One such technology that has been utilized for a number of years is cloud computing (CC). Cloud architecture facilitates the flow of confidential information, enabling customers to access remote resources. CC is also accompanied with notable security dangers, concerns, and challenges. In order to tackle these difficulties, we suggest integrating BC and SDN into a CC framework designed for the IoT. The fundamental flexibility and centralized capabilities of SDN facilitate network management, facilitate network abstraction, simplify network evolution, and possess the capacity to effectively handle the IoT network. The utilization of BCT is widely acknowledged as a means to ensure robust security inside distributed SDN (DSDN) and IoT networks, hence enhancing the efficacy of the detection and mitigation procedures.

Automated Network Services for Exascale Data Movement

The Large Hadron Collider (LHC) experiments distribute data by leveraging a diverse array of National Research and Education Networks (NRENs), where experiment data management systems treat networks as a “blackbox” resource. After the High Luminosity upgrade, the Compact Muon Solenoid (CMS) experiment alone will produce roughly 0.5 exabytes of data per year. NREN Networks are a critical part of the success of CMS and other LHC experiments. However, during data movement, NRENs are unaware of data priorities, importance, or need for quality of service, and this poses a challenge for operators to coordinate the movement of data and have predictable data flows across multi-domain networks. The overarching goal of SENSE (The Software-defined network for End-to-end Networked Science at Exascale) is to enable National Labs and universities to request and provision end-to-end intelligent network services for their application workflows leveraging SDN (Software-Defined Networking) capabilities. This work aims to allow LHC Experiments and Rucio, the data management software used by CMS Experiment, to allocate and prioritize certain data transfers over the wide area network. In this paper, we will present the current progress of the integration of SENSE, Multi-domain end-to-end SDN Orchestration with QoS (Quality of Service) capabilities, with Rucio, the data management software used by CMS Experiment.

Secure Multi-Path Selection with Optimal Controller Placement Using Hybrid Software-Defined Networks with Optimization Algorithm

The Internet's growth in popularity requires computer networks for both agility and resilience. Recently, unable to satisfy the computer needs for traditional networking systems. Software Defined Networking (SDN) is known as a paradigm shift in the networking industry. Many organizations are used SDN due to their efficiency of transmission. Striking the right balance between SDN and legacy switching capabilities will enable successful network scenarios in architecture networks. Therefore, this object grand scenario for a hybrid network where the external perimeter transport device is replaced with an SDN device in the service provider network. With the moving away from older networks to SDN, hybrid SDN includes both legacy and SDN switches. Existing models of SDN have limitations such as overfitting, local optimal trapping, and poor path selection efficiency. This paper proposed a Deep Kronecker Neural Network (DKNN) to improve its efficiency with a moderate optimization method for multipath selection in SDN. Dynamic resource scheduling is used for the reward function the learning performance is improved by the deep reinforcement learning (DRL) technique. The controller for centralised SDN acts as a network brain in the control plane. Among the most important duties network is selected for the best SDN controller. It is vulnerable to invasions and the controller becomes a network bottleneck. This study presents an intrusion detection system (IDS) based on the SDN model that runs as an application module within the controller. Therefore, this study suggested the feature extraction and classification of contractive auto-encoder with a triple attention-based classifier. Additionally, this study leveraged the best performing SDN controllers on which many other SDN controllers are based on OpenDayLight (ODL) provides an open northbound API and supports multiple southbound protocols. Therefore, one of the main issues in the multi-controller placement problem (CPP) that addresses needed in the setting of SDN specifically when different aspects in interruption, ability, authenticity and load distribution are being considered. Introducing the scenario concept, CPP is formulated as a robust optimization problem that considers changes in network status due to power outages, controller’s capacity, load fluctuations and changes in switches demand. Therefore, to improve network performance, it is planned to improve the optimal amount of controller placements by simulated annealing using different topologies the modified Dragonfly optimization algorithm (MDOA).

Empowering DDoS Attack Mitigation with Programmable Switches

Distributed denial-of-service (DDoS) attacks have long been the most severe and destructive attack on modern networks. Some solutions place several middleboxes that run security-oriented network functions (SNFs) in the network to defend against DDoS attacks. However, middleboxes are proprietary and fixed-function, making them costly and inflexible when handling attack dynamics. Another class of solutions exploits the capability of software-defined networking (SDN) and network function virtualization (NFV) to run virtualized SNFs on commodity servers. This reduces the cost of DDoS attack mitigation while enabling high flexibility by dynamically removing or adding SNF instances. However, this class of solutions sacrifices packet processing performance and incurs non-trivial end-to-end latency, which is unacceptable for many latency-sensitive Internet services. Recently, the emergence of programmable switches brings a promising alternative solution: arbitrary SNFs can be directly performed in line-rate ASIC pipelines of programmable switches, enabling low-cost, flexible, and high-performance DDoS attack mitigation. In this article, we present an illustrative survey of recent solutions that leverage programmable switches to provide DDoS attack mitigation. Our survey can help understand how to make full use of the benefits of programmable switches to defend against DDoS attacks.

Intent-based network slicing for SDN vertical services with assurance: Context, design and preliminary experiments

Network slicing is announced to be one of the key features for 5G infrastructures enabling network operators to provide network services with the flexibility and dynamicity necessary for the vertical services, while relying on Network Function Virtualization (NFV) and Software-defined Networking (SDN). On the other hand, vertical industries are attracted by flexibility and customization offered by operators through network slicing, especially if slices come with in-built SDN capabilities to programmatically connect their application components and if they are relieved of dealing with detailed technicalities of the underlying (virtual) infrastructure.In this paper, we present an Intent-based deployment of a NFV orchestration stack that allows for the setup of Qos-aware and SDN-enabled network slices toward effective service chaining in the vertical domain. The main aim of the work is to simplify and automate the deployment of tenant-managed SDN-enabled network slices through a declarative approach while abstracting the underlying implementation details and unburdening verticals to deal with technology-specific low-level networking directives. In our approach, the intent-based framework we propose is based on an ETSI NFV MANO platform and is assessed through a set of experimental results demonstrating its feasibility and effectiveness.

QoS-Aware Fog Node Placement for Intensive IoT Applications in SDN-Fog Scenarios

The advent of the Internet of Things (IoT) paradigm to intensive domains, such as industry, is a key enabler for the automation of critical, real-world processes. The strict Quality-of-Service (QoS) requirements of these domains make low-latency computing paradigms, such as fog computing, very attractive for meeting these requirements. Moreover, the requirements of scalability and flexibility in the underlying network communications motivate the use of software-defined networking (SDN) in the infrastructure. To enable these fog-SDN environments, fog nodes (FNs) that have both computing and SDN capabilities can be deployed, thus easing the deployment of fog in SDN networks. However, the exact placement of these FNs is key to the latency of the hosts that make use of them, and thus, must be carefully assessed to meet the stringent QoS requirements of critical, time-strict IoT applications. This article focuses on this FN placement problem by formalizing it and solving it through both optimal and approximated methods, including comparisons with state-of-the-art benchmarks. In particular, we analyze the performance of each of these methods in terms of latency and execution time in both SDN Internet topologies and Industrial IoT infrastructures. Our proposed heuristic provides placements with near-optimal latencies, with smaller optimality gaps than the benchmark, and computes them in tractable times.

Intelligent and resizable control plane for software defined vehicular network: a deep reinforcement learning approach

Software-defined networking (SDN) has become one of the most promising paradigms to manage large scale networks. Distributing the SDN control proved its performance in terms of resiliency and scalability. However, the choice of the number of controllers to use remains problematic. A large number of controllers may be oversized inducing an overhead in the investment cost and the synchronization cost in terms of delay and traffic load. However, a small number of controllers may be insufficient to achieve the objective of the distributed approach. So, the number of used controllers should be tuned in function of the traffic charge and application requirements. In this paper, we present an intelligent and resizable control plane for software defined vehicular network architecture, where SDN capabilities coupled with deep reinforcement learning (DRL) allow achieving better QoS for vehicular applications. Interacting with SDVN, DRL agent decides the optimal number of distributed controllers to deploy according to the network environment (number of vehicles, load, speed etc.). To the best of our knowledge, this is the first work that adjusts the number of controllers by learning from the vehicular environment dynamicity. Experimental results proved that our proposed system outperforms static distributed SDVN architecture in terms of end-to-end delay and packet loss.

Software Defined Internet of Vehicles for Automation and Orchestration

We are introducing an innovative networking approach to the Internet of Vehicles (IoV) for their automation and orchestration by redesigning the wireless edge framework. To this end, we propose to expand the Software Defined Networking (SDN) functionality from the edge network to the cars, i.e. equipping our vehicles with mobile base stations having SDN capabilities. By adopting such a novel approach, most of the impending IoV automation and orchestration challenges are ameliorated, which has been extremely difficult and non-trivial to tackle by using standard existing approaches. The three primary challenges identified in this context are a) the scalability of the network of connected autonomous vehicles, b) the desirable security of the IoV data networking, and c) the flexibility of quality of experience (QoE) perceived by the vehicles. To tackle the aforementioned three challenges, we design a policy-driven framework for a secure and efficient IoV networking paradigm and then investigate its performance via analytic modeling. More importantly, from the IoV data networking perspective, we design an intent-based flow offloading scheme to facilitate enhanced and adjustable QoE. Furthermore, we develop a rigorous analysis to quantify the efficiency of the proposed SDN-capable IoV data networking by modeling TCP connections over WiFi. Using mathematics as a tool for reasoning the three key challenges, our feasibility analysis is validated with extensive simulations. Finally, we provide new insights by deriving the stability conditions for the data flow dynamics of the proposed approach.

Blockchain-Based Cyber-Physical Security for Electrical Vehicle Aided Smart Grid Ecosystem

The ever-growing trend of making the traditional power grids smarter than before has resulted in their gradual evolution to more sophisticated grids, referred to as Smart Grids (SGs) Cyber-Physical Systems with complex networking technologies. The integration of Information and Communication Technologies with power grids fosters seamless data sharing between different SG entities, which supports effective and smart governance in terms of demand response management, frequency support, and voltage stabilization. Nonetheless, this integration opens up several security and privacy concerns, namely, electricity theft, power loss, battery exhaustion, infrastructure mapping, etc. These issues become even more important with the addition of distributed energy sources, e.g. electric vehicles (EVs), battery energy storage systems, and renewable energy sources, into the SGs. We present a framework based on Software Defined Networking (SDN) and BlockChain (BC) to address two challenging issues of EV-aided SG ecosystems, namely, privacy assurance and power security. We leverage the capabilities of SDN to handle the complex interactions between different subsystems of the SG. Furthermore, we also employ BC and smart contracts' properties to secure energy transactions and data communications. We design a secure and efficient mutual authentication protocol based on Elliptic Curve Cryptography (ECC) and BC for privacy preservation during smart energy trading. We also proposed a BC-based smart contract for effective Demand Response Management (DRM) during bidirectional energy transfer between EVs and SG. Finally, we present experimental evaluations to validate the proposed framework's performance. The results obtained demonstrate the improved performance of the proposed scheme compared with current state-of-the-art approaches. The mutual authentication protocol designed is not only secure against major attack vectors (namely, session key security, message integrity, anonymity, forward secrecy, and so on), but it is also cost-efficient in terms of communication and computational costs. Additionally, the SC designed assures power security and maintains an adequate balance between demand and supply.

Network Tomography for Efficient Monitoring in SDN-Enabled 5G Networks and Beyond: Challenges and Opportunities

Efficient monitoring plays a vital role in software-defined networking (SDN)-enabled 5G networks, involving the monitoring of performance metrics for both physical and virtual networks and considering the requirements of a variety of traffic types originating from different applications. However, conventional network monitoring tools based on traditional IP-based mechanisms may not be able to keep up with the dynamic nature of 5G networks. Network tomography (NT) is an emerging monitoring approach that estimates network performance based on measurements realized at a limited subset of network elements, presenting benefits over traditional monitoring techniques, but susceptible to identifiability issues. In this article, we demonstrate how NT can respond to current monitoring challenges, complementing and working together with SDN, yielding accurate estimations with low overhead, while exploiting SDN capabilities such as the centralized view of the entire network, direct flow-level measurements, and controllable routing. Furthermore, we present the spectrum of applications of NT-based solutions in 5G networks and beyond, ranging from virtual to vehicular networks.

SYNGuard: Dynamic threshold‐based SYN flood attack detection and mitigation in software‐defined networks

SYN flood attacks (half-open attacks) have been proven a serious threat to software-defined networking (SDN)-enabled infrastructures. A variety of intrusion detection and prevention systems (IDPS) have been introduced for identifying and preventing such security threats, but they often result in significant performance overhead and response time. Therefore, those existing approaches are inflexible for large-scale networks and real-time applications. For this reason, a novel and adaptive threshold-based kernel-level intrusion detection and prevention system by leveraging SDN capabilities are proposed. The proposed systems to detect and mitigate the aforementioned threats within an SDN over widely used traditional IDPS technologies, Snort and Zeek, are comparatively examined. The approach is evaluated using a mixture of fundamental adverse attacks and SDN-specific threats on a real-world testbed. The experimental results demonstrate the efficacy of the mechanism to detect and mitigate SYN flood attacks within an SDN environment.

A Hybrid SDN Switch Based on Standard P4 Code

This letter presents an enhanced hybrid Software-Defined Networking (SDN) layer-2 switch whose behavior is specified by the Programming Protocol-independent Packet Processors (P4) language. Its SDN capabilities are enabled by using P4Runtime as control plane protocol to specify the forwarding rules used by its programmable data plane. Additionally, the device is also able to exploit P4 registers for an autonomous self-definition of its forwarding capabilities, with the goal of avoiding an overload of the SDN control plane. Its performance is better than other P4 proposals based on non-standard externs and similar to other platform-dependent implementations.

PARD: Hybrid Proactive and Reactive Method Eliminating Flow Setup Latency in SDN

Advantages of Software Defined Networking are unquestionable and are widely described in numerous scientific papers, business white papers and press articles. However, to achieve full maturity, crucial impediments to this concept and its shortcomings must be overcame. One of the most important issues regards significant setup latency of a new flow. To address this issue we propose PARD: a hybrid proactive and reactive method to manage flow table entries. Additional advantages of the proposed solution are, among the others, its ability to preserve all capabilities of Software Defined Networking, utilization of multiple flow tables, a possibility to employ fine-grained traffic engineering and, finally, compatibility with existing protocol and hardware design. It is shown that the proposed solution is able to significantly reduce latency of first packets of a new flow, which directly impacts packet loss and perceived throughput. Thus, our solution is expected to enable a wide deployment of Software Defined Networking concept without any need for protocol changes or, what is extremely important, hardware modifications.

5G RAN: Functional Split Orchestration Optimization

5G RAN aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the functions of the Radio Access Network (RAN) between centralized and distributed locations. Such paradigms improve RAN flexibility and reduce the infrastructure deployment cost without impacting the user quality of service. We propose a novel functional split orchestration scheme that aims at minimizing the RAN deployment cost, while considering the requirements of its processing network functions and the capabilities of the Cloud infrastructure. With a fine grained approach on user basis, we show that the proposed solution optimizes both processing and bandwidth resource usage, while minimizing the overall energy consumption compared to i) cell-centric, ii) distributed and iii) centralized Cloud-RAN approaches. Moreover, we evaluate the effectiveness of our proposal in a 5G experimental prototype, based on Open Air Interface (OAI). We show that our solution achieves good performance in terms of total deployment cost and resolution time.

IT and Multi-layer Online Resource Allocation and Offline Planning in Metropolitan Networks

Metropolitan networks are undergoing a major technological breakthrough leveraging the capabilities of software-defined networking (SDN) and network function virtualization (NFV). NFV permits the deployment of virtualized network functions (VNFs) on commodity hardware appliances which can be combined with SDN flexibility and programmability of the network infrastructure. SDN/NFV-enabled networks require decision-making in two time scales: short-term online resource allocation and mid-to-long term offline planning. In this paper, we first tackle the dimensioning of SDN/NFV-enabled metropolitan networks paying special attention to the role that latency plays in the capacity planning. We focus on a specific use-case: the metropolitan network that covers the Murcia - Alicante Spanish regions. Then, we propose a latency-aware multilayer service-chain allocation (LA-ML-SCA) algorithm to explore a range of maximum latency requirements and their impact on the resources for dimensioning the metropolitan network. We observe that design costs increase for low latency requirements as more data center facilities need to be spread to get closer to the network edge, reducing the economies of scale on the IT infrastructure. Subsequently, we review our recent joint computation of multi-site VNF placement and multilayer resource allocation in the deployment of a network service in a metro network. Specifically, a set of subroutines contained in LA-ML-SCA are experimentally validated in a network optimization-as-a-service architecture that assists an Open-Source MANO instance, virtual infrastructure managers and WAN controllers in a metro network test-bed.

Transition to SDN is HARMLESS: Hybrid Architecture for Migrating Legacy Ethernet Switches to SDN

Software-Defined Networking (SDN) offers a new way to operate, manage, and deploy communication networks and to overcome many long-standing problems of legacy networking. However, widespread SDN adoption has not occurred yet due to the lack of a viable incremental deployment path and the relatively immature present state of SDN-capable devices on the market. While continuously evolving software switches may alleviate the operational issues of commercial hardware-based SDN offerings, namely lagging standards-compliance, performance regressions, and poor scaling, they fail to match the cost-efficiency and port density. In this paper, we propose HARMLESS, a new SDN switch design that seamlessly adds SDN capability to legacy network gear, by emulating the OpenFlow switch OS in a separate software switch component. This way, HARMLESS enables a quick and easy leap into SDN, combining the rapid innovation and upgrade cycles of software switches with the port density and cost-efficiency of hardware-based appliances into a fully dataplane-transparent and vendor-neutral solution. HARMLESS incurs an order of magnitude smaller initial expenditure for an SDN deployment than existing turnkey vendor SDN solutions while, at the same time, yields matching, or even better, data plane performance for smaller enterprises.

The Network-Integrated Storage System

We present NICE, a key-value storage system design that leverages new software-defined network capabilities to build cluster-based network-efficient storage system. NICE presents novel techniques to co-design network routing and multicast with storage replication, consistency, and load balancing to achieve higher efficiency, performance, and scalability. We implement the NICEKV prototype. NICEKV follows the NICE approach in designing four essential network-centric storage mechanisms: request routing, replication, consistency, and load balancing. Our evaluation shows that the proposed approach brings significant performance gains compared with the current systems design: up to $7\times $ 7 × put/get performance improvement, up to $2\times $ 2 × reduction in network load, $3\times $ 3 × to $9\times $ 9 × load reduction on the storage nodes, and the elimination of scalability bottlenecks present in current designs.

Proposition and Real-Time Implementation of an Energy-Aware Routing Protocol for a Software Defined Wireless Sensor Network.

A wireless sensor network (WSN) has achieved significant importance in tracking different physical or environmental conditions using wireless sensor nodes. Such types of networks are used in various applications including smart cities, smart building, military target tracking and surveillance, natural disaster relief, and smart homes. However, the limited power capacity of sensor nodes is considered a major issue that hampers the performance of a WSN. A plethora of research has been conducted to reduce the energy consumption of sensor nodes in traditional WSN, however the limited functional capability of such networks is the main constraint in designing sophisticated and dynamic solutions. Given this, software defined networking (SDN) has revolutionized traditional networks by providing a programmable and flexible framework. Therefore, SDN concepts can be utilized in designing energy-efficient WSN solutions. In this paper, we exploit SDN capabilities to conserve energy consumption in a traditional WSN. To achieve this, an energy-aware multihop routing protocol (named EASDN) is proposed for software defined wireless sensor network (SDWSN). The proposed protocol is evaluated in a real environment. For this purpose, a test bed is developed using Raspberry Pi. The experimental results show that the proposed algorithm exhibits promising results in terms of network lifetime, average energy consumption, the packet delivery ratio, and average delay in comparison to an existing energy efficient routing protocol for SDWSN and a traditional source routing algorithm.

SDN-enabled recovery for Smart Grid teleprotection applications in post-disaster scenarios

Maintaining Smart Grid communications is crucial for providing power services. This requires a resilient communication architecture that can instantly self-repair any failures in the communication links or routes. Emerging Software Defined Networking (SDN) technology provides excellent flexibilities that can be applied to critical power grid applications. In this paper, we consider the problem of link failures in inter-substation communications and provide self-recovery by relying on wireless links that can be the only viable means for communication after disasters. Specifically, we propose an autonomous framework, which can not only detect link failures, but also establish either a WiFi or LTE-based link among substations through SDN capabilities. To be able to effectively evaluate the performance of this proposed SDN-enabled framework, we developed it in Mininet emulator. Since Mininet does not support LTE connections, we proposed several unique extensions to Mininet by integrating it with ns- 3 simulator that supports the LTE/WiFi protocol stacks. We conducted extensive experiments by considering a teleprotection application using GOOSE to assess the recovery performance of the proposed framework. The results show that SDN-based framework can meet the deadlines for teleprotection on wireless links during the times of link failures in a reliable fashion.

NLES: A Novel Lifetime Extension Scheme for Safety-Critical Cyber-Physical Systems Using SDN and NFV

The cyber-physical system (CPS) is a promising technique that enables a safety-critical industry ecosystem. In general, wireless sensor networks (WSNs) and the Internet of Things are the sensing and communication infrastructures for CPS. Currently, software-defined networking (SDN) have been used as the new networking architecture for typical WSNs and CPS. However, there are two unresolved problems for a software-defined CPS. First, a feasible systemic architecture is a must for software-defined CPS, which should provide a global virtualization management and closed-loop control between the cyber side and the physical side in a CPS. Second, the lifetime of a software-defined CPS scenario needs to be extended for critical applications. To address the above challenges, this paper proposes a systematic virtual networking architecture to perform the global virtualization control and monitoring of a CPS, in which network functions virtualization (NFV) configuration and orchestration can be realized. Moreover, based on the proposed architecture, a novel lifetime extension scheme (NLES), is proposed for a software-defined CPS. To orchestrate the resource dynamically and efficiently, the instant programmability of an SDN and instant deployment capability of NFV are utilized to control the topology of node modes of a CPS. Then, a game theoretic topology decision approach is proposed to control the topology of the clustering and virtual network function deployment of sensors at run-time in a CPS. The experimental results show that NLES has longer lifetime compared to those of the traditional schemes.