265 research outputs found

    Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence

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    Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches O(106)\mathbf{O}(10^6) servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these. However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies. This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in ≈500ps\approx 500 ps and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where 3×3\times less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in O(10−3)s\mathbf{O}(10^{-3}) s. This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved >20%>20\% with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work

    Regulating competition in the digital network industry: A proposal for progressive ecosystem regulation

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    The digital sector is a cornerstone of the modern economy, and regulating digital enterprises can be considered the new frontier for regulators and competition authorities. To capture and address the competitive dynamics of digital markets we need to rethink our (competition) laws and regulatory strategies. The thesis develops new approaches to regulating digital markets by viewing them as part of a network industry. By combining insights from our experiences with existing regulation in telecommunications with insights from economics literature and management theory, the thesis concludes by proposing a new regulatory framework called ‘progressive ecosystem regulation’. The thesis is divided in three parts and has three key findings or contributions. The first part explains why digital platforms such as Google’s search engine, Meta’s social media platforms and Amazon’s Marketplace are prone to monopolization. Here, the thesis develops a theory of ‘digital natural monopoly’, which explains why competition in digital platform markets is likely to lead to concentration by its very nature.The second part of the thesis puts forward that competition in digital markets persists, even if there is monopoly in a market. Here, the thesis develops a conceptual framework for competition between digital ecosystems, which consists of group of actors and products. Digital enterprises compete to carve out a part of the digital network industry where they can exert control, and their strong position in a platform market can be used offensively or defensively to steer competition between ecosystems. The thesis then sets out four phases of ecosystem competition, which helps to explain when competition in the digital network industry is healthy and when it is likely to become problematic.The third and final part of the thesis brings together these findings and draws lessons from our experiences of regulating the network industry for telecommunications. Based on the insights developed in the thesis it puts forward a proposal for ‘progressive ecosystem regulation’. The purpose of this regulation is to protect and empower entrants from large digital ecosystems so that they can develop new products and innovate disruptively. This regulatory framework would create three regulatory pools: a heavily regulated, lightly regulated and entrant pool. The layered regulatory framework allows regulators to adjust who receives protection under the regulation and who faces the burdens relatively quickly, so that the regulatory framework reflects the fast pace of innovation and changing nature of digital markets. With this proposal, the thesis challenges and enriches our existing notions on regulation and specifically how we should regulate digital markets

    Regulating competition in the digital network industry: A proposal for progressive ecosystem regulation

    Get PDF
    The digital sector is a cornerstone of the modern economy, and regulating digital enterprises can be considered the new frontier for regulators and competition authorities. To capture and address the competitive dynamics of digital markets we need to rethink our (competition) laws and regulatory strategies. The thesis develops new approaches to regulating digital markets by viewing them as part of a network industry. By combining insights from our experiences with existing regulation in telecommunications with insights from economics literature and management theory, the thesis concludes by proposing a new regulatory framework called ‘progressive ecosystem regulation’. The thesis is divided in three parts and has three key findings or contributions. The first part explains why digital platforms such as Google’s search engine, Meta’s social media platforms and Amazon’s Marketplace are prone to monopolization. Here, the thesis develops a theory of ‘digital natural monopoly’, which explains why competition in digital platform markets is likely to lead to concentration by its very nature.The second part of the thesis puts forward that competition in digital markets persists, even if there is monopoly in a market. Here, the thesis develops a conceptual framework for competition between digital ecosystems, which consists of group of actors and products. Digital enterprises compete to carve out a part of the digital network industry where they can exert control, and their strong position in a platform market can be used offensively or defensively to steer competition between ecosystems. The thesis then sets out four phases of ecosystem competition, which helps to explain when competition in the digital network industry is healthy and when it is likely to become problematic.The third and final part of the thesis brings together these findings and draws lessons from our experiences of regulating the network industry for telecommunications. Based on the insights developed in the thesis it puts forward a proposal for ‘progressive ecosystem regulation’. The purpose of this regulation is to protect and empower entrants from large digital ecosystems so that they can develop new products and innovate disruptively. This regulatory framework would create three regulatory pools: a heavily regulated, lightly regulated and entrant pool. The layered regulatory framework allows regulators to adjust who receives protection under the regulation and who faces the burdens relatively quickly, so that the regulatory framework reflects the fast pace of innovation and changing nature of digital markets. With this proposal, the thesis challenges and enriches our existing notions on regulation and specifically how we should regulate digital markets

    Risk driven models & security framework for drone operation in GNSS-denied environments

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    Flying machines in the air without human inhabitation has moved from abstracts to reality and the concept of unmanned aerial vehicles continues to evolve. Drones are popularly known to use GPS and other forms of GNSS for navigation, but this has unfortunately opened them up to spoofing and other forms of cybersecurity threats. The use of computer vision to find location through pre-stored satellite images has become a suggested solution but this gives rise to security challenges in the form of spoofing, tampering, denial of service and other forms of attacks. These security challenges are reviewed with appropriate requirements recommended. This research uses the STRIDE threat analysis model to analyse threats in drone operation in GNSS-denied environment. Other threat models were considered including DREAD and PASTA, but STRIDE is chosen because of its suitability and the complementary ability it serves to other analytical methods used in this work. Research work is taken further to divide the drone system into units based in similarities in functions and architecture. They are then subjected to Failure Mode and Effects Analysis (FMEA), and Fault Tree Analysis (FTA). The STRIDE threat model is used as base events for the FTA and an FMEA is conducted based on adaptations from IEC 62443-1-1, Network and System Security- Terminology, concepts, and models and IEC 62443-3-2, security risk assessment for system design. The FTA and FMEA are widely known for functional safety purposes but there is a divergent use for the tools where we consider cybersecurity vulnerabilities specifically, instead of faults. The IEC 62443 series has become synonymous with Industrial Automation and Control Systems. However, inspiration is drawn from that series for this work because, drones, as much as any technological gadget in play recently, falls under a growing umbrella of quickly evolving devices, known as Internet of Things (IoT). These IoT devices can be principally considered as part of Industrial Automation and Control Systems. Results from the analysis are used to recommend security standards & requirements that can be applied in drone operation in GNSS-denied environments. The framework recommended in this research is consistent with IEC 62443-3-3, System security requirements and security levels and has the following categorization from IEC 62443-1-1, identification, and authentication control, use control, system integrity, data confidentiality, restricted data flow, timely response to events and resource availability. The recommended framework is applicable and relevant to military, private and commercial drone deployment because the framework can be adapted and further tweaked to suit the context which it is intended for. Application of this framework in drone operation in GNSS denied environment will greatly improve upon the cyber resilience of the drone network system

    Development of high-performance, cost-effective quantum dot lasers for data-centre and Si photonics applications

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    Photonic technologies have been considered new methods to achieve high bandwidth data communication and transmission. Si-photonics was proposed to address the discrepancy between bulky photonic devices and advanced electronics and create high-density integrated photonics. One of the challenges is integrating all the components necessary for full-functionality photonic integrated circuits (PIC). Great efforts have been devoted to overcoming the inherent limitations of Group-IV materials to provide sufficient gain, efficient modulation and sensitive detections. Making Si the host material for efficient light emission poses the most stringent requirements and is the primary missing component in the Si-photonics platform. Incorporating III-V materials with the Si photonics platform and quantum dot (QD) structure is a promising solution to the problem of a fully-integrated and high-functioning PIC. High-performance QD lasers on III-V substrate or epitaxially on silicon have been developed in the last few decades with low threshold current density, low-temperature sensitivity, great reliability and large injection efficiency. Moreover, from the dynamic aspect, the intrinsic frequency of direct modulated laser and noise intensity is important for its applications in a data centre. QD is considered an alternative to quantum wells (QWs); however, the demonstrated QD laser has not fulfilled initial expectations, mainly due to its high gain compression and low differential gain. Another feature that needs to be noticed is feedback sensitivity, as the properties of semiconductor lasers are greatly degraded by reflection from external reflectors, such as the fibre connects and facets of integrated devices. QD devices are predicted to have stronger feedback resistance due to their large damping and small linewidth enhancement factor (LEF). These properties have attracted much research, and high-performance QD devices have been developed. In this thesis, we comprehensively investigated QD laser performance and applied our QD laser in the optical module instead of the commercial QW distributed feedback (DFB) laser. The background of Si photonics, the development of QD devices, and the fundamentals of QD lasers are presented in Chapter 1. The basic static and dynamic performances are demonstrated in Chapters 2 and 3. The GaAs-based QD laser provides a low threshold, high-temperature stability, and low noise operation with a limited small signal bandwidth. Chapter 4 provides a comprehensive study of the feedback resistance of the QD laser. The onset of coherence collapse is determined as -14 dB, verified by the static optical and electrical spectra and small signal response. Based on previous measurements, the QD laser is proven to be a high-performance, low-cost candidate for the Si-photonics module. In Chapter 5, the QD laser is used in practical applications, including a large signal transmission system with and without feedback and a commercial optical module. Although the intrinsic bandwidth of the QD laser is limited to around 5GHz due to the large damping and unoptimised capacitance, 30 Gbps data transmission has been demonstrated by a directly modulated QD laser. Large, high-speed signal modulation is achieved due to its high gain compression factor. Regarding the laser with intentional feedback, there is little degradation in the eye diagram under the whole feedback level up to -8dB. We also replaced the commercial QW DFB laser in 100G data-centre reach (DR)-1 optical module with our QD Fabry Perot (FP) laser without an isolator which gives a clear eye diagram under 53 Gbps 4-level pulse amplitude modulation (PAM4) with an extinction ratio (ER) of 4.7 dB. In conclusion, this thesis verifies the feasibility of adopting the QD laser as a light source for the Si-photonics module. The QD laser is selected over other lasers because of its low threshold, high-temperature stability and maximum operating temperature, and strong tolerance to unintentional feedback. This is the first project to measure critical feedback levels with different characteristics and to theoretically analyse the inconsistent value. More importantly, this thesis’ most original contribution is investigating the commercial applications of QD lasers in a Si-photonics module in an isolator-free state. In summary, the QD laser has been proven to be a feasible solution for the next-generation optical system

    Understanding the factors influencing the adoption of cloud computing in higher education during coronavirus disease: a case of University of KwaZulu-Natal.

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    Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Cloud computing (CC) as a model for internet-based service provisioning, enables the delivery and access of services based on dynamically scalable and virtualized resources (infrastructure, platforms, etc.). For higher education institutions (HEIs) cloud computing provides services anywhere and anytime, as a result of its scalability and pay-as-you-use approach. Although scalable processing and storage, data sharing, and anytime, anywhere access are some of the key advantages that CC may offer enterprises, there are also risks and barriers to adoption, and it is still in its infancy in developing nations. The Coronavirus (Covid-19) pandemic, which struck the entire world in 2020, compelled institutions to alter their procedures and methods as a result of the social distancing laws that were put in place to stop the spread of Covid-19. The sudden surge of the Covid-19 pandemic caused a quick acceleration towards the adoption and use of CC in learning and education to ensure the continuation of classes. CC had a significant impact in fighting the epidemic and became a saviour for various fields including the education sector. This study seeks to investigate the factors influencing the adoption of CC in HEIs during the upsurge of the Covid-19 virus. The research model utilised is the unified theory of acceptance and use of a technology (UTAUT). The study used a quantitative technique to identify the factors that influence the adoption of cloud computing through a questionnaire survey that was administered to a convenient sample at the UKZN Pietermaritzburg campus. The study found that effort expectancy (EE), performance expectancy (PE) and social influence (SI) all positively influence the behavioural intention (BI) to use CC for learning purposes, with performance expectancy being the highest predictor of behavioural intention to adopt CC for students. Additionally, facilitating conditions (FC) and behavioural intention (BI) were also found to influence the actual sage of CC for learning purposes. These findings are useful as they give university’s policymakers, designers insights into what factors are crucial when implementing CC to ensure the successful adoption by students
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