Recently, Data Centre (DC) infrastructures have been growing rapidly to support a wide range of emerging services, and provide the underlying connectivity and compute resources that facilitate the "*-as-a-Service" model. This has led to the deployment of a multitude of services multiplexed over few, very large-scale centralised infrastructures. In order to cope with the ebb and flow of users, services and traffic, infrastructures have been provisioned for peak-demand resulting in the average utilisation of resources to be low. This overprovisionning has been further motivated by the complexity in predicting traffic demands over diverse timescales and the stringent economic impact of outages. At the same time, the emergence of Software Defined Networking (SDN), is offering new means to monitor and manage the network infrastructure to address this underutilisation. This dissertation aims to show how measurement-based resource management can improve performance and resource utilisation by adaptively tuning the infrastructure to the changing operating conditions. To achieve this dynamicity, the infrastructure must be able to centrally monitor, notify and react based on the current operating state, from per-packet dynamics to longstanding traffic trends and topological changes. However, the management and orchestration abilities of current SDN realisations is too limiting and must evolve for next generation networks. The current focus has been on logically centralising the routing and forwarding decisions. However, in order to achieve the necessary fine-grained insight, the data plane of the individual device must be programmable to collect and disseminate the metrics of interest. The results of this work demonstrates that a logically centralised controller can dynamically collect and measure network operating metrics to subsequently compute and disseminate fine-tuned environment-specific settings. They show how this approach can prevent TCP throughput incast collapse and improve TCP performance by an order of magnitude for partition-aggregate traffic patterns. Futhermore, the paradigm is generalised to show the benefits for other services widely used in DCs such as, e.g, routing, telemetry, and security.

The ab initio calculation of wave functions for small polyatomic molecules is now feasible but is time-consuming, expensive and limited in accuracy. The most frequently used approach is that of molecular orbital (MO) theory, using the self-consistent field (SCF) method with a linear combination of atomic orbitals (LCAO) approximation to the HO's. On the other hand, semiempirical methods have been widely used and have yielded extremely interesting results in spite of the fact that they have often been based on flimsy theoretical foundations. The first and best known calculations of this type were of course initiated by Huckel and refer to the n-electrons of conjugated molecules. Later semiempirical SCF LCAO MO calculations, in which electron interaction effects are more properly taken into account, were done on n-electron systems. Then the Huckel type LCAO MO method, and later the approximate SCF HO scheme, were applied to more general systems. In this work a new semi-empirical SCF scheme is presented in which an attempt is made to produce a method as close to ab initio procedures as possible. A particular basis of orthogonalised orbitals is chosen to render valid, with a reasonable degree of accuracy, the integral approximations made. The use of a particular set of integral approximations allows the simulation of the results of non-empirical calculations. The semi-empirical calculations described in this work are less empirical than any previously performed on more general systems; this allows the scheme to be built on a sounder basis than other semi-empirical schemes which include all electrons. Results are presented to show that with a relatively simple method of estimating the larger two-electron integrals, over an orthogonal basis, reasonable results can be obtained for small polyatomic molecules. As well as giving good results the method is used as a basis for examining the foundations of more empirical calculations. Two approaches are used to obtain wave functions, the SCF MO LCAO and the self-consistent group function (SCGF) method. It is found that SCGF method has several advantages over the ordinary SCF HO LCAO method in the performance of semi-empirical calculations.

Mobile devices when provisioned with intuitive mobile healthcare (mHealth) applications provide a powerful platform that has been recognised to have made a significant impact on healthcare delivery. The popularity of mHealth applications is rapidly expanding amongst consumers and there is a continuous demand to improve the effectiveness of mHealth applications. Personalisation has already been acknowledged by the healthcare industry as a mechanism to improve healthcare delivery, recognising that each consumer is unique. Yet, a typical mHealth application is designed to cater for the needs of large target demographics and are frequently developed without the necessary knowledge and expertise of healthcare providers. As a result, they often fail to meet the consumer’s specific healthcare requirements. Since healthcare professionals understand the specific healthcare requirements of a consumer, they are best suited for developing personalised mobile healthcare applications. However, they do not possess the familiarity, skills and knowledge to address the challenges associated with mobile application development. Therefore, this research addresses the need for a new approach to personalised mHealth application development in the form of an extensible ontology-driven framework that enables healthcare professionals to create personalised mHealth applications for healthcare consumers. This research explored personalisation & the challenges of personalised mobile application development, existing approaches and related works. Followed by a detailed investigation into the various health-related functions available in mHealth applications designed for healthcare consumers, that led to the creation of the mHealth Application Function Taxonomy. The next phase presents the theoretical design and development considerations of the Personalised Mobile Application Development (PMAD) ontology. The PMAD ontology encapsulates key knowledge associated with the development of personalised mHealth applications, that can be operationalised to compensate for the missing domain expertise during the personalised mHealth application development process. The final and contribution of this research describes and defines the approach and components of the Personalised Mobile Application Development ontology-driven framework that addresses the limitations of existing end-user programming solutions and enables healthcare professionals to create personalised mHealth applications for healthcare consumers.

Continuous time Markov chains (CTMCs) are a flexible class of stochastic models that have been employed in a wide range of applications from timing of computer protocols, through analysis of reliability in engineering, to models of biochemical networks in molecular biology. These models are defined as a state system with continuous time transitions between the states. Extensive work has been historically performed to enable convenient and flexible definition, simulation, and analysis of continuous time Markov chains. This thesis considers the problem of Bayesian parameter inference on these models and investigates computational methodologies to enable such inference. Bayesian inference over continuous time Markov chains is particularly challenging as the likelihood cannot be evaluated in a closed form. To overcome the statistical problems associated with evaluation of the likelihood, advanced algorithms based on Monte Carlo have been used to enable Bayesian inference without explicit evaluation of the likelihoods. An additional class of approximation methods has been suggested to handle such inference problems, known as approximate Bayesian computation. Novel Markov chain Monte Carlo (MCMC) approaches were recently proposed to allow exact inference. The contribution of this thesis is in discussion of the techniques and challenges in implementing these inference methods and performing an extensive comparison of these approaches on two case studies in systems biology. We investigate how the algorithms can be designed and tuned to work on CTMC models, and to achieve an accurate estimate of the posteriors with reasonable computational cost. Through this comparison, we investigate how to avoid some practical issues with accuracy and computational cost, for example by selecting an optimal proposal distribution and introducing a resampling step within the sequential Monte-Carlo method. Within the implementation of the ABC methods we investigate using an adaptive tolerance schedule to maximise the efficiency of the algorithm and in order to reduce the computational cost.

In recent years, Optical Wireless (OW) communication techniques have attracted substantial attention as a benefit of their abundant spectral resources in the optical domain, which is a potential solution for satisfying the ever-increasing demand for increased wireless capacity in the conventional Radio Frequency (RF) band. Motivated by the emerging techniques and applications of OW communication, the Institute of Electrical and Electronics Engineers (IEEE) had released the IEEE standard 802.15.7 for short-range optical wireless communications, which categorised the Physical layer (PHY) of the OW communication into three candidate-solutions according to their advantages in different applications and environments: 1) Physical-layer I (PHY I): Free Space Optical (FSO)communication employs high-intensity Light Emitting Diodes (LEDs) or Laser Diodes (LDs) as its transmitter. 2) Physical-layer II (PHY II) uses cost-effective, low-power directional white LEDs for the dual function of illumination and communication. 3) Physical III (PHY-III) relies on the so-called Colour-Shift Keying (CSK) modulation scheme for supporting high-rate communication. Our investigations can be classified into three major categories, namely Optical Orthogonal Frequency Division Multiplexing (OFDM) based Multiple-Input Multiple-Output (MIMO) techniques for FSO communications in the context of PHY I, video streaming in PHY-II and the analysis and design of CSK for PHY-III. To be more explicit, in Chapter 2 we first construct a novel ACO-OFDM based MIMO system and investigate its performance under various FSO turbulence channel conditions. However, MIMO systems require multiple optical chains, hence their power consumption and hardware costs become substantial. Hence, we introduced the concept of Aperture Selection (ApS) to mitigate these problems with the aid of a simple yet efficient ApS algorithm for assisting our ACO-OFDM based MIMO system. Since the channel conditions of indoor Visible Light Communication (VLC) environments are more benign than the FSO-channels of Chapter 2, directional white LEDs are used to create an “attocell” in Chapter 3. More specifically, we investigate video streaming in a multi-Mobile Terminals (MTs) indoor VLC system relying on Unity Frequency Reuse (UFR) as well as on Higher Frequency Reuse Factor based Transmission (HFRFT) and on Vectored Transmission (VT) schemes. We minimise the distortion of video streaming, while satisfying the rate constraints as well as optical constraints of all the MTs. In Chapter 4 we analyse the performance of CSK relying both on joint Maximum Likelihood (ML) Hard-Detection (HD), as well as on the the Maximum A posteriori (MAP) criterion-based Soft-Detection (SD) of CSK. Finally, we conceive both two- stage and three-stage concatenated iterative receivers capable of achieving a substantial iteration gain, leading to a vanishingly low BER.

The emerging technology of mobile devices and cloud computing has brought a new and efficient way for data to be collected, processed and stored by mobile users. With improved specifications of mobile devices and various mobile applications provided by cloud servers, mobile users can enjoy tremendous advantages to manage their daily life through those applications instantaneously, conveniently and productively. However, using such applications may lead to the exposure of user data to unauthorised access when the data is outsourced for processing and storing purposes. Furthermore, such a setting raises the privacy breach and security issue to mobile users. As a result, mobile users would be reluctant to accept those applications without any guarantee on the safety of their data. The recent breakthrough of Fully Homomorphic Encryption (FHE) has brought a new solution for data processing in a secure motion. Several variants and improvements on the existing methods have been developed due to efficiency problems. Experience of such problems has led us to explore two areas of studies, Mobile Sensing Systems (MSS) and Mobile Cloud Computing (MCC). In MSS, the functionality of smartphones has been extended to sense and aggregate surrounding data for processing by an Aggregation Server (AS) that may be operated by a Cloud Service Provider (CSP). On the other hand, MCC allows resource-constraint devices like smartphones to fully leverage services provided by powerful and massive servers of CSPs for data processing. To support the above two application scenarios, this thesis proposes two novel schemes: an Accountable Privacy-preserving Data Aggregation (APDA) scheme and a Lightweight Homomorphic Encryption (LHE) scheme. MSS is a kind of WSNs, which implements a data aggregation approach for saving the battery lifetime of mobile devices. Furthermore, such an approach could improve the security of the outsourced data by mixing the data prior to be transmitted to an AS, so as to prevent the collusion between mobile users and the AS (or its CSP). The exposure of users’ data to other mobile users leads to a privacy breach and existing methods on preserving users’ privacy only provide an integrity check on the aggregated data without being able to identify any misbehaved nodes once the integrity check has failed. Thus, to overcome such problems, our first scheme APDA is proposed to efficiently preserve privacy and support accountability of mobile users during the data aggregation. Furthermore, APDA is designed with three versions to provide balanced solutions in terms of misbehaved node detection and data aggregation efficiency for different application scenarios. In addition, the successfully aggregated data also needs to be accompanied by some summary information based on necessary additive and non-additive functions. To preserve the privacy of mobile users, such summary could be executed by implementing existing privacy-preserving data aggregation techniques. Nevertheless, those techniques have limitations in terms of applicability, efficiency and functionality. Thus, our APDA has been extended to allow maximal value finding to be computed on the ciphertext data so as to preserve user privacy with good efficiency. Furthermore, such a solution could also be developed for other comparative operations like Average, Percentile and Histogram. Three versions of Maximal value finding (Max) are introduced and analysed in order to differentiate their efficiency and capability to determine the maximum value in a privacy-preserving manner. Moreover, the formal security proof and extensive performance evaluation of our proposed schemes demonstrate that APDA and its extended version can achieve stronger security with an optimised efficiency advantage over the state-of-the-art in terms of both computational and communication overheads. In the MCC environment, the new LHE scheme is proposed with a significant difference so as to allow arbitrary functions to be executed on ciphertext data. Such a scheme will enable rich-mobile applications provided by CSPs to be leveraged by resource-constraint devices in a privacy-preserving manner. The scheme works well as long as noise (a random number attached to the plaintext for security reasons) is less than the encryption key, which makes it flexible. The flexibility of the key size enables the scheme to incorporate with any computation functions in order to produce an accurate result. In addition, this scheme encrypts integers rather than individual bits so as to improve the scheme’s efficiency. With a proposed process that allows three or more parties to communicate securely, this scheme is suited to the MCC environment due to its lightweight property and strong security. Furthermore, the efficacy and efficiency of this scheme are thoroughly evaluated and compared with other schemes. The result shows that this scheme can achieve stronger security under a reasonable cost.

Moving services to the Cloud environment is a trend that has been increasing in recent years, with a constant increase in sophistication and complexity of such services. Today, even critical infrastructure operators are considering moving their services and data to the Cloud. As Cloud computing grows in popularity, new models are deployed to further the associated benefits. Federated Clouds are one such concept, which are an alternative for companies reluctant to move their data out of house to a Cloud Service Providers (CSP) due to security and confidentiality concerns. Lack of collaboration among different components within a Cloud federation, or among CSPs, for detection or prevention of attacks is an issue. For protecting these services and data, as Cloud environments and Cloud federations are large scale, it is essential that any potential solution should scale alongside the environment adapt to the underlying infrastructure without any issues or performance implications. This thesis presents a novel architecture for collaborative intrusion detection specifically for CSPs within a Cloud federation. Our approach offers a proactive model for Cloud intrusion detection based on the distribution of responsibilities, whereby the responsibility for managing the elements of the Cloud is distributed among several monitoring nodes and brokering, utilising our Service-based collaborative intrusion detection – “Security as a Service” methodology. For collaborative intrusion detection, the Dempster-Shafer (D-S) theory of evidence is applied, executing as a fusion node with the role of collecting and fusing the information provided by the monitoring entities, taking the final decision regarding a possible attack. This type of detection and prevention helps increase resilience to attacks in the Cloud. The main novel contribution of this project is that it provides the means by which DDoS attacks are detected within a Cloud federation, so as to enable an early propagated response to block the attack. This inter-domain cooperation will offer holistic security, and add to the defence in depth. However, while the utilisation of D-S seems promising, there is an issue regarding conflicting evidences which is addressed with an extended two stage D-S fusion process. The evidence from the research strongly suggests that fusion algorithms can play a key role in autonomous decision making schemes, however our experimentation highlights areas upon which improvements are needed before fully applying to federated environments.

The heavy demands on cloud computing resources have led to a substantial growth in energy consumption of the data transferred between cloud computing parties (i.e., providers, datacentres, users, and services) and in datacentre’s services due to the increasing loads on these services. From one hand, routing and transferring large amounts of data into a datacentre located far from the user’s geographical location consume more energy than just processing and storing the same data on the cloud datacentre. On the other hand, when a cloud user submits a job (in the form of a set of functional and non-functional requirements) to a cloud service provider (aka, datacentre) via a cloud services broker; the broker becomes responsible to find the best-fit service to the user request based mainly on the user’s requirements and Quality of Service (QoS) (i.e., response time, latency). Hence, it becomes a high necessity to locate the lowest energy consumption route between the user and the designated datacentre; and the minimum possible number of most energy efficient services that satisfy the user request. In fact, finding the most energy-efficient route to the datacentre, and most energy efficient service(s) to the user are the biggest challenges of multi-cloud broker’s environment. This thesis presents and evaluates a novel multi-cloud broker solution that contains three innovative models and their associated algorithms. The first one is aimed at finding the most energy efficient route, among multiple possible routes, between the user and cloud datacentre. The second model is to find and provide the lowest possible number of most energy efficient services in order to minimise data exchange based on a bin-packing approach. The third model creates an energy-aware composition plan by integrating the most energy efficient services, in order to fulfil user requirements. The results demonstrated a favourable performance of these models in terms of selecting the most energy efficient route and reaching the least possible number of services for an optimum and energy efficient composition.

Multicarrier (MC) transmissions are proposed for the space time shift keying (STSK) concept. Specifically, OFDM, MC CDMA and OFDMA/SC-FDMA-aided STSK are proposed for transmissions over dispersive wireless channels. Additionally, a successive relaying (SR) aided cooperative MC STSK scheme is conceived for gleaning cooperative space time diversity and for mitigating the half-duplex throughput loss of conventional relaying. Furthermore, a multiple-symbol differential sphere decoding (MSDSD) aided multicarrier STSK arrangement is proposed to dispense with channel estimation (CE). We design a novel modality of realizing STSK amalgamated with OFDM for facilitating high-rate data-transmissions through a number of low-rate parallel subchannels, thus overcoming the dispersion induced by broadband channels. A MC-CDMA aided STSK system is also proposed for mitigating the channel-induced dispersion, while providing additional frequency-domain (FD) diversity and supporting multiuser transmissions. As a further advance, we design OFDMA and SC FDMA-aided STSK systems, which are capable of communicating in dispersive multiuser scenarios, whilst maintaining a low peak-to-average power ratio (PAPR) in the SC-FDMA-aided STSK uplink. Additionally, complexity reduction techniques are proposed for OFDMA/SC-FDMA-aided STSK. We also conceive the concept of SR aided cooperative multicarrier STSK for frequency-selective channels for mitigating the typical 50% throughput loss of conventional half-duplex relaying in the context of MC-CDMA and for reducing the SR-induced interferences. We additionally propose a differentially encoded cooperative MC-CDMA STSK scheme for facilitating communications over hostile dispersive channels without requiring CE. Finally, the noncoherent multicarrier STSK arrangement is further developed by using MSDSD. The conventional differential detection suffers from a typical 3-dB performance loss, which is further aggravated in the presence of high Doppler frequencies. Hence, for the sake of mitigating this performance loss in the face of high Doppler scenarios, while maintaining a modest decoding complexity, both a hard-decision-based as well as an iterative soft-decision multiple-symbol differential sphere decoding aided multicarrier STSK arrangement is developed.

Reduced-complexity near-maximum-likelihood Ant-Colony Optimization (ACO) assisted Multi-User Detectors (MUDs) are proposed and investigated. The exhaustive search complexity of the optimal detection algorithm may be deemed excessive for practical applications. For example, a Space-Time Block Coded (STBC) two transmit assisted K = 32-user system has to search through the candidate-space for finding the final detection output during 264 times per symbol duration by invoking the Euclidean-distance-calculation of a 64-element complex-valued vector. Hence, a nearoptimal or near-ML MUDs are required in order to provide a near-optimal BER performance at a significantly reduced complexity. Specifically, the ACO assisted MUD algorithms proposed are investigated in the context of a Multi-Carrier DS-CDMA (MC DS-CDMA) system, in a Multi-Functional Antenna Array (MFAA) assisted MC DS-CDMA system and in a STBC aided DS-CDMA system. The ACO assisted MUD algorithm is shown to allow a fully loaded MU system to achieve a near-single user performance, which is similar to that of the classic Minimum Mean Square Error (MMSE) detection algorithm. More quantitatively, when the STBC assisted system support K = 32 users, the complexity imposed by the ACO based MUD algorithm is a fraction of 1 × 10−18 of that of the full search-based optimum MUD. In addition to the hard decision based ACO aided MUD a soft-output MUD was also developed,which was investigated in the context of an STBC assisted DS-CDMA system using a three-stage concatenated, iterative detection aided system. It was demonstrated that the soft-output system is capable of achieving the optimal performance of the Bayesian detection algorithm.