A microservices-based control plane for time sensitive networking

Time-Sensitive Networking (TSN) is a group of IEEE 802.1 standards that aim at providing deterministic communications over IEEE Ethernet. The main characteristics of TSN are low bounded latency and very high reliability, which complies with the strict requirements of industry and automotive applications. In this context, allocating time slots, configuration paths, and Gate Control Lists (GCLs) to contending TSN streams is often laborious. Software-Defined Networking (SDN) and the IEEE 802.1 Qcc standard provide the basis to design a TSN control plane to face these challenges. However, current SDN/TSN control plane solutions are monolithic applications designed to run on dedicated servers. None of them explores Microservice as a design pattern; these SDN controllers do not provide the required flexibility to escalate when facing increasing service requests. This work presents $\mu$TSN-CP, a microservices-based Control Plane (CP) architecture for TSN/SDN that provides superior scalability in situations with highly dynamic service demands. Using a qualitative approach, we evaluate our $\mu$TSN-CP solution compared to a monolithic solution in terms of CPU usage, RAM usage, latency, and percentage of successfully allocated TSN Streams. Our $\mu$TSN-CP architecture leverages the advantages of microservices, enabling the control plane to scale up or down in response to varying workloads dynamically. We achieve enhanced flexibility and resilience by breaking down the control plane into smaller, independent microservices. The experimental evaluation demonstrates that our TSN-CP outperforms the monolithic solution, with significantly lower CPU and RAM usage, reduced latency, and a higher percentage of successfully allocated TSN Streams. This advancement in TSN/SDN control plane design opens up new possibilities for highly scalable and adaptable networks, catering to the ever-increasing demands of time-sensitive applications in various industries.Objectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur

Autonomic disaggregated multilayer networking

Focused on reducing capital expenditures by opening the data plane to multiple vendors without impacting performance, node disaggregation is attracting the interest of network operators. Although the software-defined networking (SDN) paradigm is key for the control of such networks, the increased complexity of multilayer networks strictly requires monitoring/telemetry and data analytics capabilities to assist in creating and operating self-managed (autonomic) networks. Such autonomicity greatly reduces operational expenditures, while improving network performance. In this context, a monitoring and data analytics (MDA) architecture consisting of centralized data storage with data analytics capabilities, together with a generic node agent for monitoring/telemetry supporting disaggregation, is presented. A YANG data model that allows one to clearly separate responsibilities for monitoring configuration from node configuration is also proposed. The MDA architecture and YANG data models are experimentally demonstrated through three different use cases: i) virtual link creation supported by an optical connection, where monitoring is automatically activated; ii) multilayer self-configuration after bit error rate (BER) degradation detection, where a modulation format adaptation is recommended for the SDN controller to minimize errors (this entails reducing the capacity of both the virtual link and supported multiprotocol label switching-transport profile (MPLS-TP) paths); and iii) optical layer selfhealing, including failure localization at the optical layer to find the cause of BER degradation. A combination of active and passive monitoring procedures allows one to localize the cause of the failure, leading to lightpath rerouting recommendations toward the SDN controller avoiding the failing element(s).Peer ReviewedPostprint (author's final draft

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

API design and implementation of a management interface for SDN whitebox switches

For the past few years, cloud computing has emerged to be one of the most rapidly growing plaforms. This growth must be supported from the data centers, that look to provide the best possible service, while minimising energy and infrastructure costs. As such, many service providers are moving to Software Defined Networking (SDN) based platforms, that allow for new concepts such as the separation of the control and data planes, and the adoption of open source material, in both the switches, in the form of whitebox switches, and the network controllers. BISDN is a company that is developing a SDN controller, that allows to use the linux networking tools, like netlink, to configure and manage ports on switches. The proposed problem, is then extending the existing platform to be able to report statistics such as flow-counts on the switches, the number of packets received, dropped, transmitted in the ports, so that the data center operators can have the best possible information on the state of their network, and act in case of failures and malfunctions

Time-Sensitive Networking for Industrial Automation: Challenges, Opportunities, and Directions

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) into industrial applications, industrial automation is undergoing tremendous change, especially with regard to improving efficiency and reducing the cost of products. Industrial automation applications are often required to transmit time- and safety-critical data to monitor and control industrial processes, especially for critical control systems. There are a number of solutions to meet these requirements (e.g., priority-based real-time schedules and closed-loop feedback control systems). However, due to their different processing capabilities (e.g., in the end devices and network switches), different vendors may come out with distinct solutions, and this makes the large-scale integration of devices from different vendors difficult or impossible. IEEE 802.1 Time-Sensitive Networking (TSN) is a standardization group formed to enhance and optimize the IEEE 802.1 network standards, especially for Ethernet-based networks. These solutions can be evolved and adapted into a cross-industry scenario, such as a large-scale distributed industrial plant, which requires multiple industrial entities working collaboratively. This paper provides a comprehensive review on the current advances in TSN standards for industrial automation. We present the state-of-the-art IEEE TSN standards and discuss the opportunities and challenges when integrating each protocol into the industry domains. Finally, we discuss some promising research about applying the TSN technology to industrial automation applications

Cognitive and Autonomous Software-Defined Open Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Flexible network management in software defined wireless sensor networks for monitoring application systems

Wireless Sensor Networks (WSNs) are the commonly applied information technologies of modern networking and computing platforms for application-specific systems. Today’s network computing applications are faced with high demand of reliable and powerful network functionalities. Hence, efficient network performance is central to the entire ecosystem, more especially where human life is a concern. However, effective management of WSNs remains a challenge due to problems supplemental to them. As a result, WSNs application systems such as in monitored environments, surveillance, aeronautics, medicine, processing and control, tend to suffer in terms of capacity to support compute intensive services due to limitations experienced on them. A recent technology shift proposes Software Defined Networking (SDN) for improving computing networks as well as enhancing network resource management, especially for life guarding systems. As an optimization strategy, a software-oriented approach for WSNs, known as Software Defined Wireless Sensor Network (SDWSN) is implemented to evolve, enhance and provide computing capacity to these resource constrained technologies. Software developmental strategies are applied with the focus to ensure efficient network management, introduce network flexibility and advance network innovation towards the maximum operation potential for WSNs application systems. The need to develop WSNs application systems which are powerful and scalable has grown tremendously due to their simplicity in implementation and application. Their nature of design serves as a potential direction for the much anticipated and resource abundant IoT networks. Information systems such as data analytics, shared computing resources, control systems, big data support, visualizations, system audits, artificial intelligence (AI), etc. are a necessity to everyday life of consumers. Such systems can greatly benefit from the SDN programmability strategy, in terms of improving how data is mined, analysed and committed to other parts of the system for greater functionality. This work proposes and implements SDN strategies for enhancing WSNs application systems especially for life critical systems. It also highlights implementation considerations for designing powerful WSNs application systems by focusing on system critical aspects that should not be disregarded when planning to improve core network functionalities. Due to their inherent challenges, WSN application systems lack robustness, reliability and scalability to support high computing demands. Anticipated systems must have greater capabilities to ubiquitously support many applications with flexible resources that can be easily accessed. To achieve this, such systems must incorporate powerful strategies for efficient data aggregation, query computations, communication and information presentation. The notion of applying machine learning methods to WSN systems is fairly new, though carries the potential to enhance WSN application technologies. This technological direction seeks to bring intelligent functionalities to WSN systems given the characteristics of wireless sensor nodes in terms of cooperative data transmission. With these technological aspects, a technical study is therefore conducted with a focus on WSN application systems as to how SDN strategies coupled with machine learning methods, can contribute with viable solutions on monitoring application systems to support and provide various applications and services with greater performance. To realize this, this work further proposes and implements machine learning (ML) methods coupled with SDN strategies to; enhance sensor data aggregation, introduce network flexibility, improve resource management, query processing and sensor information presentation. Hence, this work directly contributes to SDWSN strategies for monitoring application systems.Thesis (PhD)--University of Pretoria, 2018.National Research Foundation (NRF)Telkom Centre of ExcellenceElectrical, Electronic and Computer EngineeringPhDUnrestricte