Saudi Cultural Missions Theses & Dissertations
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Item Restricted Cloud computing efficiency: optimizing resource utilization, energy consumption, latency, availability, and reliability using intelligent algorithms(The Universit of Western Australia, 2024) Alelyani, Abdullah Hamed A; Datta, Amitava; Ghulam, Mubasher HassanCloud computing offers significant potential for transforming service delivery with a cost-efficient, pay-as-you-go model, which has led to a dramatic increase in demand. The advantages of virtual machine (VM) and container technologies further optimize resource utilization in cloud environments. Containers and VMs improve application reliability by distributing replicated tasks across different physical machines (PMs). However, several persistent issues in cloud computing remain, including energy consumption, resource management, network traffic costs, availability, latency, service level agreement (SLA) violations, and reliability. Addressing these issues is critical for ensuring QoS. This thesis proposes approaches to address these issues and improve cloud performance.17 0Item Restricted RESOURCE MANAGEMENT ALGORITHMS FOR SOFTWARE DEFINED NETWORKS-BASED EDGE-CLOUD COMPUTING(University Putra Malaysia, 2024) Alomari, Amirah Hassan; Subramaninam, Shamala A/P KThe integration of Software-Defined Networking (SDN), Edge Computing, and Cloud Computing represents a transformative synergy in modern network and computing architectures. SDN enhances network flexibility by separating control and data planes, a concept that becomes particularly valuable when combined with edge computing, which places computational resources closer to data sources. Cloud computing complements these advantages by offering scalable and on-demand resources to a wide range of applications and workloads, and ensuring resource availability across the network. Recent advancements consider the adoption of SDN infrastructure to empower cloud and edge computing for dynamic controllability and manageability. However, the integration of SDN into cloud and edge poses key challenges, including suboptimal resource utilisation in heavily-loaded SDN-Cloud networks, which leads to network congestion, QoS violations, and increased power consumption. Additionally, controller congestion in SDN systems leads to delays, reduces scalability, and prevents the system to handle high traffic loads efficiently, posing a significant challenge for optimising network performance. While conflicts in prioritisation complicate the efficient allocation of resources, which can degrade QoS and network efficiency. To address these challenges, three algorithms are proposed for SDN-Cloud and SDN Edge-Cloud platforms. The Dual-Phase Virtual Machine (VM) allocation algorithm (D-Ph) optimises resources in SDN-Cloud networks, considering processing capacity and memory requirements, to enhance QoS and power efficiencies. The Queue Theory Model-based Adaptive Reinforcement Learning Algorithm (QTM-ARL) optimises load balancing in SDN Edge-Cloud platform while maintaining QoS constraints. Priority-Aware Scheduler (PASQ) based on QoS constraints and incorporated with rate limit mechanism, manages network traffic efficiently while prioritising VoIP traffic over video streaming to enhance network performance. The proposed algorithms are investigated for performance through eventdriven simulation (CloudSimSDN) and MATLAB, employing real workload datasets and delay-sensitive applications. Results demonstrate D-Ph's efficiency in balancing network performance and power consumption in heterogeneous heavily-load large-scale SDN-Cloud networks based on response time, network and CPU performance, QoS violation rate, and power consumption. Furthermore, QTM-ARL's effectiveness in maintaining QoS in hierarchical multi-controller system with fluctuating data flows, and PAS-Q's ability to prioritize low-latency VoIP traffic over video streaming while achieving the desired level of service quality for real-time communication applications and thus fair resource utilisation. Future research can explore advanced AI, emerging technologies, eco-friendly practices, and adaptive SDN architectures to enhance the efficiency, security, and sustainability of SDNbased Edge-Cloud systems.8 0Item Restricted Computational Intelligence Approaches for Energy-Aware Microservice Based SaaS Deployment in a Data Centre(Qeensland University of Technology, 2024) Alzahrani, Amal Saleh; Tang, MaolinMicroservice-based Software as a Service (SaaS) entails a software delivery approach in which software is constructed as a set of loosely coupled, independently deployable services known as microservices. Microservices are autonomous, small-scale services that collaborate to create a more extensive application. The microservice-based SaaS deployment problem refers to the challenge of efficiently deploying microservices within a SaaS to compute servers in a cloud data centre. This deployment leads to a significant increase in overall energy consumption, primarily due to the increased energy usage of the compute servers hosting the microservices. Moreover, the energy consumption of network devices that facilitate connections between in terconnected microservices also contributes to the overall energy usage. This increase in energy consumption raises concerns regarding sustainability, environmental impact and operational costs. In contrast to traditional SaaS deployment approaches, where energy considerations are frequently disregarded, this thesis addresses the energy increase associated with the deployment of microservice-based SaaS, focusing specifically on reducing the increase in energy consump tion in compute servers and network devices. To address this new microservice-based SaaS deployment problem, three Computational Intelligence (CI) approaches are designed and developed. First, an Adaptive Hybrid Genetic Algorithm (GA) is developed to tackle the problem. It achieves this by dynamically balancing the exploration-exploitation trade-off through an adaptive crossover rate. Furthermore, the adaptive hybrid GA incorporates a local optimiser, which refines the best solutions by improving the exploitation capacity of the adaptive hybrid GA. Second, a Hybrid Particle Swarm optimi sation (HPSO) approach is developed to address the new SaaS deployment problem. HPSO also integrates a local optimiser to enhance its exploitation capacity, thus further refining the best solutions. Additionally, HPSO has the capability to dynamically adjust the inertia weight and its cognitive and social parameters throughout the optimisation process. Third, an Ant Colony Optimisation (ACO) approach, equipped with new heuristic information, is developed to solve the new SaaS deployment problem. During the search for a compute server to host a microservice, this heuristic information aids the ants in selecting a compute server that will result in a lower increase in the energy consumption of both compute servers and network devices. In this thesis, a comparative study is conducted to evaluate the effectiveness, efficiency and scalability of the adaptive hybrid GA, HPSO and ACO approaches in solving the new SaaS deployment problem. The findings reveal that the adaptive hybrid GA is the most effective approach for minimising both total energy increase and the energy increase specifically in the compute servers. Its ability to provide energy-efficient solutions while maintaining good scala bility and fast execution times makes it the optimal choice for addressing the new microservice based SaaS deployment problem. The HPSO is identified as the second most effective and efficient approach, after the adap tive hybrid GA. It also demonstrates good scalability and faster execution times compared to ACO, efficiently generating optimal or near-optimal solutions as the problem size grows despite increasing complexity. Although the ACO is effective at minimising the increase in the energy consumption of network devices, it is the least effective approach for reducing the overall increase in total energy consumption. The cubic or quadratic increases observed in ACO’s execution times highlight its poor performance in scaling effectively with larger problem instances. The ACO’s poor scalability renders it impractical for handling larger problem sizes.15 0Item Restricted Building Secure and Trustworthy Stream Analytics Systems Using Trusted Execution Environment(Monash University, 2024) Bagher, Kassem; Yuan, Xingliang; Cui, ShujieThe exponential growth in data generated by interconnected IoT devices has accelerated the adoption of cloud platforms for near-real-time analytics in various applications, such as smart grids and healthcare. However, cloud centralization presents both security and latency challenges. While edge computing and cryptographic solutions like homomorphic encryption offer partial remedies, they either fail to adequately protect code or incur substantial computational overhead. A promising alternative lies in leveraging a Trusted Execution Environment (TEE), such as Intel SGX, which creates an isolated and secure region in the memory, called enclaves, to protect the confidentiality and integrity of both code and data. Nonetheless, SGX has several limitations, including limited memory size and is vulnerable to information leakage through side-channel attacks. This thesis advances secure and efficient data analytics on hybrid cloud-edge platforms through the integration of Trusted Execution Environments (TEEs), specifically Intel SGX, with cryptographic protocols. It comprises three interrelated studies that collectively enhance data integrity, privacy, and operational efficiency in real-time analytics. The first study introduces a framework that minimises data transmission to the cloud by processing initial data clustering at the edge, significantly reducing latency and enhancing data security. The second study develops a secure stream processing framework within SGX that efficiently handles large data streams, priorities tasks, and minimises query latency, thus enhancing both security and operational efficiency. The third study addresses the mitigation of side-channel attacks in time-series data processing, introducing a novel approach that decouples data operations to improve security and system performance. Each component of this research contributes to building robust, scalable, and secure real-time data analytics solutions, ensuring comprehensive data protection and operational efficiency across various sectors.40 0Item Restricted The Impact of Cloud Computing on the Skills, Autonomy, and Professional Identities of Junior External Auditors Case Studies of Three of the Big Four Audit Firms in Saudi Arabia(The University of Sheffield, 2024-01) Alromaihi, Alaa; Lee, Bill; Matos De, Juliana; Ji, JiaoThis thesis critically applies the Marxist (1954) analysis of capitalism and labour process theory (LPT) to investigate the impact of technological advances – specifically the introduction of Cloud Computing – on the work and experiences of junior external auditors at three of the Big Four audit firms in Saudi Arabia. While previous research has predominantly focused on the organisational level, this study shifts the focus to concentrates on the individual level. The study undertakes three case studies, 29 in-depth semi-structured interviews with junior and senior external auditors, managers, and partners providing a comprehansive perspective of the transformative effects of Cloud technology on the career development of jounior auditors. Responding to calls for more qualitative studies in the field of accounting research and more research in developing countries, this thesis expands the scope of Cloud Computing research. The findings reveal aspects of deskilling in the reduced demand for physical and mental effort, while the enhancement of juniors’ technical skills represents a form of reskilling. Notably, the decrease in autonomy among junior auditors, attributed to the centralising effect of the Cloud, provides support for LPT, illustrating how technological and procedural changes can reshape power dynamics in the workplace. The findings emphasis the dynamic interplay between technology and labour processes; highlighting significant shifts in the roles, behaviours, and attitudes of junior auditors due to the adoption of Cloud technology.57 0Item Restricted Machine Learning (ML) Technologies(John Jay College of Criminal Justice, 2024-04-03) Alanazi, Mosa; Seferaj, GentianaIntegrating Machine Learning (ML) technologies into physical security has ignited significant discourse within scholarly circles, focusing on identifying specific ML technologies currently employed and elucidating their tangible outcomes. This integration occurs against a rapidly evolving technological landscape, encompassing advancements such as cloud computing, 5G wireless technology, real-time Internet of Things (IoT) data, surveillance cameras fortified with biometric technologies, and predictive data analytics. Collectively, these innovations augment the transformative potential of ML within security frameworks, ranging from sophisticated video analytics facilitating advanced threat detection to predictive algorithms aiding in comprehensive risk assessment. Moreover, the seamless fusion of disparate data streams and the capability to extract actionable insights in real-time present profound implications for the future trajectory of security protocols, heralding a paradigm shift in the conceptualization, implementation, and Student No: 10001 Page 2 of 14 Comprehensive Exam/Project ̶̶̶ Spring24 Department of Security, Fire and Emergency Management maintenance of physical security measures. This study endeavors to delve into the specifics of ML technologies currently operationalized in physical security contexts, scrutinize the tangible outcomes they yield, and forecast how these trends will shape the future security landscape— additionally, strategic recommendations aimed at optimizing the efficacy of ML-driven security solutions in safeguarding physical environments.133 0Item Restricted Modelling & Simulation the Performance of User Behaviour in Serious Contexts(Saudi Digital Library, 2023-09-27) Alkoradees, Ali Fayez; Thomas, Nigel; Harrison, Michael; Colquhoun, JohnReal-time experiments on healthcare procedural improvement can be infeasible due to the domain’s criticality and sensitivity. For instance, high morbidity rates and escalated patient treatment duration can, in some circumstances, be associated with medical resources exhaustion. Thus, formal methods can be an answer to lower the effects of experimentation within these healthcare domains as such an approach may be effective in deriving new insights and proposing further recommendations to the investigated domain. Specifically, performance modelling formalisms provide a rich theoretical foundation for dynamic systems, which are affected by an extensive collection of interventions, and supported by the existing formalisms toolsets. Hence, investigating healthcare system contexts involves several complex challenges. These challenges range from data collection methods and data analysis formalisms to optimising medical outcomes. This optimisation is beneficial to behaviour analysts and medical administrators. The current thesis contributes to addressing these challenges in many different ways: (i) By presenting an improved web-based version of a sketch simulation that collects the clinician behaviour during massive bleeding scenarios. This unconventional data collection method is proposed to minimise the need to observe the interventions in person where such treatment of these medical cases are performed; (ii) The modelling of two medical scenarios using different modelling formalisms for analysis and evaluation purposes, these modelling formalisms are Performance Evaluation Process Algebra (PEPA), Collective Adaptive Resource-sharing Markovian Agents (CARMA), and Stochastic Petri nets (SPN); (iii) A proposed tool to enhance the log analysis process. Doing so required the implementation of a trace-driven simulation tool. The tool simulates a clinical behaviour that has been recorded using a sketch simulation version. (iv) Proposing different suggestions to improve medical outcomes and to effectively reduce the cost of health resources.5 0Item Restricted The Adoption of CloudComputing: TowardsEnhancing EGovernment Systems in the Saudi Public Sector(2023-07-27) Alyami, Mohammed; Schaefer, DirkGovernments are always trying to find ways to improve their services to citizens; and in order to achieve this they need to restructure their processes and use information technology (IT) effectively. Pressure to do this comes from citizens who increasingly have access to digital technologies and expect better e-services from their governments. Public sector organisations in Saudi Arabia, therefore, need to proactively implement technological innovation to enhance their services. One way to achieve this is to develop a cloud computing infrastructure and the appropriate applications. Cloud computing is understood, however, more needs to be known about how it impacts public service organisations and the provision of services. This research aims to identify and discuss the importance of particular factors pertaining to the fitness and viability of adopting the cloud for Saudi public organisations. The model that forms the theoretical framework for this integrates the Diffusion of Innovation (DOI) theory and the Fit Viability Model (FVM). The cloud computing adoption performance within Saudi public organizations, together with determining the best cloud model for these organizations are also discussed in this research. This research adopts a mixed methodology which includes one survey conducted with 408 IT staff and 21 IT experts for the second and third surveys. The analysis of quantitative data was processed by structural equation modelling (SEM) and descriptive statistics. The qualitative analysis phase was conducted using semi-structured interviews with IT heads and experts in four government organizations to deeply understand and analyse the research problem and to find the optimal solution that would lead decision makers to cloud adoption. The thematic analysis approach was chosen to analyse the qualitative data. The outcomes confirmed that the proposed model worked well and the quantitative data collected showed that fit, viability, task, relative advantage, compatibility, trialability, top management support, IT skills, ROI and asset specificity had a direct and significant effect on the adoption of cloud computing while IT policy, IT infrastructure, cloud knowledge, security, complexity and uncertainty had no direct and significant effect. The qualitative data largely confirmed these findings but shed further light on cloud adoption and suggested that other factors such as trust, service quality, accessibility and ease of interaction also needed to be considered.24 0Item Restricted Self-adaptive System Supporting Elasticity and Quality of Service in Edge Computing(2023-05-30) Aljulayfi, Abdullah Fawaz A; Djemame, Karim; Xu, JieThe Edge Computing (EC) paradigm is seen as a promising paradigm to address the Internet of Things’ (IoT) application requirements, such as low latency to support responsiveness. It is a complementary paradigm of the Cloud Computing (CC) which leverages CC’s resources to the network proximity closer the data source in a distributed fashion. EC is a complex operational environment due to its nature which consists of limited resources and experiences a highly dynamic workload. This complexity is also augmented by the massive growth of the number of end devices, e.g., IoT. Such complexity requires efficient resource and task management to support both the elasticity, which aims to provision and deprovision the resources in order to adapt with the workload dynamicity and cope with the massive growth of the number of IoT devices in such resource restricted environment, and the Quality of service (QoS) in terms of the latency, which aims to manage the tasks by avoiding resource overutilisation to support the fulfilment of the latency requirement as EC has limited resources, experiences a high workload, and emerged to support such requirement. Such management requires a continuous monitoring of the operation environment, including the behaviour of the end users and their applications’ requirements, in order to have a full control over the EC infrastructure. This can be performed using a Self-adaptive System (SAS) which enables the system to monitor the operational environment, hence, adapt to response to the environment changes without human interaction. To this end, this thesis proposes a novel SAS for EC environment. The proposed SAS consists of three frameworks which are the elasticity framework, that aims to provision and deprovision the containerised applications in accordance to the workload dynamicity using Machine Learning (ML) algorithms, the QoS framework, whereby it is responsible for performing efficient task management by avoid resource overutilisation to support the latency requirement, and the offloading framework, which aims to consider the cloud layer by offloading some workload to extend the edge capability as it has limited resources. Moreover, a simulation-based environment is used to implement and evaluate the proposed SAS under different scenarios to demonstrate its effectiveness. The performance evaluation results show that it is essential to study and understand the operational environment, such as workload and applications scenarios, in order to have a robust SAS that can support both elasticity and QoS. For instance, the improvement of the SAS performance in the acceptance rate can reach ~70% in supporting the elasticity once a suitable adaptive approach is selected. Additionally, the internal design of the SAS to support the latency requirement can significantly improve the system objectives fulfilment, which can reach 50%.35 0