PREDICTING INTERNET TRAFFIC BURSTS USING EXTREME VALUE THEORY

Computer networks play an important role in today’s organization and people life. These interconnected devices share a common medium and they tend to compete for it. Quality of Service (QoS) comes into play as to define what level of services users get. Accurately defining the QoS metrics is thus important. Bursts and serious deteriorations are omnipresent in Internet and considered as an important aspects of it. This thesis examines bursts and serious deteriorations in Internet traffic and applies Extreme Value Theory (EVT) to their prediction and modelling. EVT itself is a field of statistics that has been in application in fields like hydrology and finance, with only a recent introduction to the field of telecommunications. Model fitting is based on real traces from Belcore laboratory along with some simulated traces based on fractional Gaussian noise and linear fractional alpha stable motion. QoS traces from University of Napoli are also used in the prediction stage. Three methods from EVT are successfully used for the bursts prediction problem. They are Block Maxima (BM) method, Peaks Over Threshold (POT) method, and RLargest Order Statistics (RLOS) method. Bursts in internet traffic are predicted using the above three methods. A clear methodology was developed for the bursts prediction problem. New metrics for QoS are suggested based on Return Level and Return Period. Thus, robust QoS metrics can be defined. In turn, a superior QoS will be obtained that would support mission critical applications

PREDICTING INTERNET TRAFFIC BURSTS USING EXTREME VALUE THEORY

Computer networks play an important role in today’s organization and people life. These interconnected devices share a common medium and they tend to compete for it. Quality of Service (QoS) comes into play as to define what level of services users get. Accurately defining the QoS metrics is thus important. Bursts and serious deteriorations are omnipresent in Internet and considered as an important aspects of it. This thesis examines bursts and serious deteriorations in Internet traffic and applies Extreme Value Theory (EVT) to their prediction and modelling. EVT itself is a field of statistics that has been in application in fields like hydrology and finance, with only a recent introduction to the field of telecommunications. Model fitting is based on real traces from Belcore laboratory along with some simulated traces based on fractional Gaussian noise and linear fractional alpha stable motion. QoS traces from University of Napoli are also used in the prediction stage. Three methods from EVT are successfully used for the bursts prediction problem. They are Block Maxima (BM) method, Peaks Over Threshold (POT) method, and RLargest Order Statistics (RLOS) method. Bursts in internet traffic are predicted using the above three methods. A clear methodology was developed for the bursts prediction problem. New metrics for QoS are suggested based on Return Level and Return Period. Thus, robust QoS metrics can be defined. In turn, a superior QoS will be obtained that would support mission critical applications

Financial advisors’ perceptions of ethical and effective attitudes and behaviour in their profession

Unethical behaviour is a concern in the workplace, because of the possible consequences for all stakeholders. This issue is particularly salient in the financial services sector, a highly regulated environment, where breaches of the regulations can result in large fines and reputational damage to the organisation concerned. Unethical behaviour can also have severe effects on customers, such as when inappropriate advice leads to customers losing all or a large part of their savings. Empirical studies have tended to focus mostly on organisational antecedents of unethical intentions and behaviour, with individual factors not being given that much attention. Research on the antecedents of unethical intentions and behaviour has produced inconsistent findings, suggesting that context might play a role. Consequently, my research has attempted to study the individual antecedents of unethical intentions and behaviour in the financial services industry, a specific context where it is salient. Malta presents a particularly pertinent context for this study, as its profile on Hofstede’s cultural dimensions scale has been empirically linked to a higher potential for engaging in unethical behaviour. [Continues.

Development of a remotely supervised digitally facilitated multibehavioural prehabilitation intervention for patients approaching major surgery

Improving outcomes following major surgery is a pressing public health challenge. Postoperative complications drive surgical mortality and a range of poorer outcomes for the individual patient (e.g., quality of life) and wider healthcare system (e.g., length of stay and cost). Preoperative improvement of physical and mental health enhancing readiness for major surgery is known as prehabilitation. Patients may experience fewer postoperative complications and overcome them more easily. Multiple prehabilitation models now exist. Delivery has been predominantly face to face, yet demand is growing for robustly developed, remotely supervised alternatives. The need is now acute following the Covid-19 pandemic. Little is known regarding patient preferences for remotely supervised prehabilitation. Equally, few systematically designed interventions currently exist. This thesis addresses these gaps. A discrete choice experiment undertaken in 164 patients preparing for major surgery across 10 NHS centres explored patient preferences for delivery of support. This work highlighted both appetite for remotely supervised models and strong views regarding their delivery. In particular, demand for a digitally facilitated option. This informed the application of a systematic co-design process utilising the Behaviour Change Wheel to develop a novel, multibehavioural, digitally facilitated prehabilitation programme prototype (iPREPWELL). This work aligned to the Medical Research Council framework for complex intervention development and encompassed structured questionnaires, semi-structured interviews and workshops involving patients preparing for major surgery and perioperative healthcare professionals. These data were combined with the existing evidence base and the input of a multidisciplinary design team. iPREPWELL is the first comprehensively theory and evidence informed intervention of its kind. The programme is poised and approved for feasibility testing in patients approaching major surgery at two NHS centres. If successful, it may offer services a route to improved uptake of prehabilitation support, with potential for flexible and cost-effective implementation across a range of surgical pathways

Modelling and Adaptive Control; Proceedings of an IIASA Conference, Sopron, Hungary, July 1986

One of the main purposes of the workshop on Modelling and Adaptive Control at Sopron, Hungary, was to give an overview of both traditional and recent approaches to the twin theories of modelling and control which ultimately must incorporate some degree of uncertainty. The broad spectrum of processes for which solutions of some of these problems were proposed was itself a testament to the vitality of research on these fundamental issues. In particular, these proceedings contain new methods for the modelling and control of discrete event systems, linear systems, nonlinear dynamics and stochastic processes