Technical Report on DFG Project SDN-App: SDN-enabled Application-aware Network Control Architectures and their Performance Assessment

We present a control plane architecture to enable software-addressable optical acceleration from the application layer. The architecture is experimentally examined on a cluster-computing test-bed by enabling physical layer optical multicasting on-demand for the application layer to achieve non-blocking performance.

Software Defined Networking (SDN) has come up as a promising technology for making the communication networks more flexible and dynamically (re)configurable. With the advent of fifth generation (5G) of mobile networks, the aforementioned flexibility became more urgent and critical than ever. SDN offers the decoupling of the network's control plane from the data plane. As a result, the infrastructure layer can be abstracted from the application and network services and therefore the network control can be done completely through a centralized component/orchestrator. In this work, a detailed practical deployment of an SDN controller for a converged FiberWireless (Fi-Wi) 5G network is presented, followed by the SDN architecture and functional components. Lastly, an experimental trial using an SDN controller directly connected to Fi-Wi lab equipment is described, in which the physical layer parameters are managed and monitored successfully, proving the applicability of SDN in a Fi-Wi network.

The aviation Internet Protocol (IP) network will be comprised of manned and unmanned aircraft, ground infrastructure, air to ground access network service providers, Air Navigation Service Providers (ANSP), content provider networks, OEM networks, airline and operator networks and devices, and supporting infrastructure (includes PKI, mobility and management etc.). There will be a full range of applications and protocols that will adopt and exploit the aviation IP network. It is key to understand the security and performance characteristics of the aviation IP networks. Many of the applications, services and protocols that could drive deployment of Internet Protocol Suite (IPS) networking in aviation can only be developed if control and management planes of the network will be secure. Yet, to really understand these properties we need studies of the whole aviation IP network's control and management plane interfaces and sub interfaces protection and security, which are difficult to obtain. To date most studies have not been completed in this area, or the analyses have been conducted using unrealistic network mobility models. To address this issue, we present an extensive analysis of an aviation IP network control and management plane's security in the context of mobility generated by both air to ground and ground to ground systems. We combined control plane and management plane data security to address the aviation IP network security issues and explore the security provision mechanisms to protect aviation critical infrastructure. Hence, this analysis and security mechanisms are a more accurate representation of future aviation IP networks, since it reflects the realistic aviation network eco-system. We analyzed the network to identify and characterize the key properties starting from the characteristics of the aircraft movement and dependency on the IP network control and management plane's security framework. We then explored the security issus related to the IPS control and management planes and evaluated how the security issue impact the design space for deployment.

Satisfying the needs of users of online video streaming services requires not only to manage the network Quality of Service (QoS), but also to address the user's Quality of Experience (QoE) expectations. While QoS factors reflect the status of individual networks, they do not comprehensively capture the end-to-end features affecting the quality delivered to the user. In this situation, QoE management is the better option. However, traditionally used QoE management models require human interaction and have stringent requirements in terms of time and complexity. Thus, they fail to achieve successful performance in terms of real-timeliness, accuracy, scalability and adaptability. This dissertation work investigates new methods to bring QoE management to the level required by the real-time management of video services. In this paper, we highlight our main contributions. First, with the aim to perform a combined network-service assessment, we designed an experimental methodology able to map network QoS onto service QoE. Our methodology is meant to provide service and network providers with the means to pinpoint the working boundaries of their video-sets and to predict the effect of network policies on perception. Second, we developed a generic machine learning framework that allows deriving accurate predictive No Reference (NR) assessment metrics, based on simplistic NR QoE methods, that are functionally and computationally viable for real-time QoE evaluation. The tools, methods and conclusions derived from this dissertation conform a solid contribution to QoE management of video streaming services, opening new venues for further research.

The measurements of Quality of Experience (QoE) are essential to research for better analysis and optimization of Multi-View and 3D (MV/3D) videos. However, there is a lack of systematic description of QoE mechanism in MV/3D videos yet. This paper aims to give a comprehensive account of QoE assessment and management for MV/3D video services. We first conclude distinctive QoE influencing factors (IFs) in MV/3D videos by dividing them into immersion and interaction related IFs. Then the methods of QoE assessment in MV/3D video services (including subjective assessment and objective assessment) are presented. Finally, we summarize QoE management for improving immersion and interaction. We hope our work is capable of providing a useful guideline on the related issues and the future research directions in MV/3D video services.KeywordsQuality of Experience (QoE)Multi-View Video (MVV)3D video

Software-Defined Networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in active detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using community detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimizes the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three community detection algorithms: Louvain, Label Propagation, and Girvan-Newman. We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%.

The increasing popularity of video (i.e., audio-visual) applications or services over both wired and wireless links has prompted recent growing interests in the investigations of quality of experience (QoE) in online video transmission. Conventional video quality metrics, such as peak-signal-to-noise-ratio and quality of service, only focus on the reception quality from the systematic perspective. As a result, they cannot represent the true visual experience of an individual user. Instead, the QoE introduces a user experience-driven strategy which puts special emphasis on the contextual and human factors in addition to the transmission system. This advantage has raised the popularity and widespread usage of QoE in video transmission. In this paper, we present an overview of selected issues pertaining to QoE and its recent applications in video transmission, with consideration of the compelling features of QoE (i.e., context and human factors). The selected issues include QoE modeling with influence factors in the end-to-end chain of video transmission, QoE assessment (including subjective test and objective QoE monitoring) and QoE management of video transmission over different types of networks. Through the literature review, we observe that the context and human factors in QoE-aware video transmission have attracted significant attentions since the past two to three years. A vast number of high quality works were published in this area, and will be highlighted in this survey. In addition to a thorough summary of recent progresses, we also present an outlook of future developments on QoE assessment and management in video transmission, especially focusing on the context and human factors that have not been addressed yet and the technical challenges that have not been completely solved so far. We believe that our overview and findings can provide a timely perspective on the related issues and the future research directions in QoE-oriented services over video communications.

In this paper, we introduce Mayflower, a new distributed filesystem that is co-designed from the ground up to work together with a network control plane. In addition to the standard distributed filesystem components, Mayflower has a flow monitor and manager running alongside a software-defined networking controller. This tight coupling with the network controller enables Mayflower to make intelligent replica selection and flow scheduling decisions based on both filesystem and network information. It further enables Mayflower to perform global optimizations that are unavailable to conventional distributed filesystems and network control planes. Our evaluation results from both simulations and a prototype implementation show that Mayflower reduces average read completion time by more than 25% compared to current state-of-the-art distributed filesystems with an independent network flow scheduler, and more than 75% compared to HDFS with ECMP.

Betas: Deriving quantiles from MOS-QoS relations of IQX models for QoE management

In recent years, SDN has evolved as a promising alternative to the traditional networking paradigm. This is because the traditional network is decentralized, making it difficult to manage and provide trouble-shooting. SDN is an approach to network management whereby the forwarding process of the data plane (DP) is disassociated from the control plane (CP). The CP is responsible for decision-making. SDN centrally control the network infrastructure and make it programmable. Communication between the two planes (data and control) is carried out by the OpenFlow (OF) protocol. The deployment of SDN was initiated by Mr. Matrin Casado and his research team in 2008. The original projects developed at Stanford University were SANE and ETHANE. Security reports and web articles suggest that SDN (Software Defined Network) has the caliber to defend DDoS attacks. The year 2017 has been declared as the year of SDN adaption and DDoS mitigation. The initial effort in the direction of compiling the detection and mitigation approaches has been done in (Dayal et al., 2016). Recently a similar effort has been done in (Rochak et al., 2019). The CP of SDN has a complete visibility of the network which makes traffic monitoring feasible. Moreover, SDN open flow switches contain forwarding logic only; decision-making capability is softwarized at 58controller. This helps the controller to instruct and configure the switches with new flow rules which the switches follow. This facilitates the network admins to detect and mitigate these attacks easily. SDN have gained momentum among the possible solutions of DDoS attacks due to its decentralized nature of network management, but solutions come with its repercussions. The centralization and open architecture framework makes SDN difficult for widespread adaptation. The chapter begins with an introduction to DDoS attacks and its classification such as volumetric attack, protocol exploitation attack and application attack. In the next section, we discuss the possible DDoS attacks over SDN framework. We have categorized the vulnerability into three classes: DDoS attack on data, control, and application planes (APs). The DDoS attack on DP can further be classified into TCAM exhaustion, switch DDoS, ICMP flood, TCP flood, TCP_SYN flood, etc. We would discuss Resource depletion, OF bandwidth exhaustion, amplification attacks in DDoS attack over CP. HTTP flooding and Slowloris attack has been discussed in DDoS attack over AP. The next section highlights the working of attack tools used in performing DDoS attacks. Various DDoS tools have been widely exploited to render DDoS attacks and facilitate attackers to carry out dangerous attack on target; here target may be network resources, server resources, or applications. The fourth section focuses on the state-of-the-art for detection and mitigation approaches of DDoS attacks. Detection approaches are classified into statistical, policy, and machine learning-based approaches. DDoS mitigation OF switches, including Alcatel Lucent’s OmniSwitch, Brocade’s MLXe series routers, and applications such as DefensePro by Radware that are available in market; also depend on the statistical threshold to detect the DDoS attack. In addition, this section also incorporates a brief analysis of the previous defense mechanism of the last 10 years and analyzes and compares the products of different security vendors like Cloudflare, Radware, Akamai, Arbor, Nexusguard, etc.

HTTP adaptive streaming (HAS) is being adopted with increasing frequency and becoming the de-facto standard for video streaming. However, the client-driven, on-off adaptation behavior of HAS results in uneven bandwidth competition and this is exacerbated when a large number of clients share the same bottleneck network link and compete for the available bandwidth. With HAS each client independently strives to maximize its individual share of the available bandwidth, which leads to bandwidth competition and a decrease in end-user quality of experience (QoE). The competition causes scalability issues, which are quality instability, unfair bandwidth sharing and network resource underutilization. We propose a new software defined networking (SDN) based dynamic resource allocation and management architecture for HAS systems, which aims to alleviate these scalability issues and improve the per-client QoE. Our architecture manages and allocates the network resources dynamically for each client based on its expected QoE. Experimental results show that the proposed architecture significantly enhances scalability by improving per-client QoE by at least 30% and supporting up to 80% more clients with the same QoE compared to the conventional schemes.

Driven by the requirement of increasing performance and flexibility, networks are being softwarized by paradigms such as software-defined networking (SDN) and network function virtualization (NFV). These solutions reduce the complexity and the specialization of hardware devices, by extracting the inherently distributed control plane of forwarding network elements such as switches and routers, to a logically centralized control plane (referred as controller in SDN). The control plane acts as a broker between the network applications (e.g. monitoring, traffic engineering) and the data plane (i.e. physical network infrastructure). For scalability and robustness, the logically centralized control plane is implemented by physically distributing different controllers throughout the network. This chapter presents different solutions to increase the reliability of both planes: data and control planes. The reliability of the data plane can be increased by considering survivable virtual network embedding solutions. This chapter proposes a survivable embedding against single and double failures at either links or nodes. Furthermore, in order to provide a programmable and resilient data plane, BPFabric has been proposed for SDN which supports high performance functions suitable for detecting attacks. On the other hand, the reliability of the control plane applied to SDN can be addressed by considering enhanced controller placement solutions providing redundancy against uncorrelated as well as targeted failures while coping with latency and capacity requirements. Furthermore, a solution to increase the security and robustness of the control channel is also addressed in this chapter.

In wireless and mobile IP networks there is a need to support a set of functions that are related to wireless QoS provisioning, resource optimizations, mobility and security management and operator policy enforcement. These requirements have lead to the frequent usage of so called middleboxes including header/payload compressors, performance enhancement proxies, tunnel endpoints, packet filters and redirectors, QoS schedulers and others. Unfortunately, the increasing usage of these types of devices often leads to undesirable artifacts called feature interactions. In this paper we propose a framework that aims to coordinate the operation of middleboxes such that feature interactions are avoided. The three main ideas are (1) the separation of the control plane from the distributed network nodes, (2) the usage of a middlebox function independent horizontal protocol that allows mobile hosts to communicate their desired service requests to the network and (3) the usage of a vertical protocol between the network control and user planes. We present two examples in which this framework helps eliminate the risk for feature interactions. We comment on the relationship between the end-to-end principle and this framework. Finally, we discuss how future middleboxes can fit into this framework.

Artificial intelligence based load balancing in SDN: A comprehensive survey

Software-defined network (SDN) radically changes the network architecture by decoupling the network logic from the underlying forwarding devices. This architectural change rejuvenates the network-layer granting centralized management and reprogrammability of the networks. From a security perspective, SDN separates security concerns into control and data plane, and this architectural recomposition brings up exciting opportunities and challenges. The overall perception is that SDN capabilities will ultimately result in improved security. However, in its raw form, SDN could potentially make networks more vulnerable to attacks and harder to protect. In this paper, we provide a comprehensive review of SDN security domain while focusing on its data plane, which is one of the least explored but most critical aspects in securing this technology. We review the most recent enhancements in SDNs, identify the main vulnerabilities of SDNs, and provide a novel attack taxonomy for SDNs. Thereafter, we provide a comprehensive analysis of challenges involved in protecting SDN data plane and control plane and provide an in-depth look into available solutions with respect to the identified threats and identify their limitations. To highlight the importance of securing the SDN platform, we also review the numerous security services built on top of this technology. We conclude the paper by offering future research directions.