Saudi Cultural Missions Theses & Dissertations
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Item 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 Adapting Vernacular Strategies through New Villas Design in Hot-Dry Climate: A Comparative Study of Using U-shaped Courtyards in Riyadh(University of Liverpool, 2024) Alzuriq, Abdulaziz; Sharples, StephenInternationally, buildings account for 30% of global energy consumption and 27% of global carbon emissions. The residential sector in Saudi Arabia accounted for around 45% of the nation's overall electric energy consumption by 2020. However, vernacular architecture can effectively mitigate the built environment's impact through energy efficiency, design elements, and the use of natural materials. This thesis aims to explore the practical implications of adapting vernacular architectural strategies, specifically focusing on the use of U-shaped courtyard layouts, to enhance energy efficiency in designing new villas in Riyadh's hot-dry climate. The research methodology includes conducting a systematic literature review to consolidate existing information and selecting an existing villa design that meets the Saudi Building Code (SBC) by analysing it and proposing a new villa design that integrates a U-shaped courtyard design, which tests it by using different design parameters such as building orientation, shading devices and increasing thermal insulation thickness. The assessment is conducted by using DesignBuilder modelling software. The results confirmed that incorporating U-shaped courtyards, particularly when combined with optimised building orientation, shading devices, and enhanced insulation, substantially decreases energy use. The courtyard design orientated towards the north and northeast exhibited the most significant energy efficiency, resulting in a 20% decrease in yearly energy use compared to the base model. Furthermore, the research highlighted the efficacy of shading devices and insulation in reducing the need for cooling during the hottest summer months. Moreover, it emphasised the significance of these approaches in future climatic scenarios predicted for RCP (4.5) 2050 and 2080. The findings propose a framework for architects and developers to incorporate traditional strategies with modern technologies. This helps to reduce energy use for upcoming residential developments in hot-dry conditions, providing realistic answers to today's architectural and environmental concerns.25 0Item Restricted Investigating the Impact of Adaptive Façades on Energy Performance Using Simulation and Machine Learning(Cardiff University, 2023-04-05) Alammar, Ammar; Jabi, Wassim; Lannon, SimonBuildings consume approximately 40% of the world's primary energy, and half of this energy demand stems from space cooling and heating. To meet the targets of designing high performance buildings, intelligent solutions need to be integrated into the design process of buildings to achieve indoor environmental comfort and minimize energy consumption. In particular, the building façade plays a crucial role, as it acts as a separator element that can control the indoor environment and energy performance. This is even more important in buildings with extensive glazing systems particularly in harsh, hot climates. As stated in the literature, buildings are exposed to dynamic environmental factors that change continuously throughout the day and the year. Nonetheless, regardless of the climatic variations, building skins have been typically designed as static envelopes, which are limited in terms of their responsiveness to indoor or outdoor environmental conditions. In contrast, adaptive façades (AFs) are flexible regarding the adaptability of the system to climatic conditions enabling them to respond to short-term changes in the environment. From an environmental viewpoint, it is essential to reduce the energy consumption of buildings and mitigate their environmental impacts. Numerous innovative building envelope technologies have been developed to improve indoor comfort and reduce the environmental impact of buildings during their life cycle. As stated in the literature, AFs can make a major and practical contribution to achieving the worldwide zero-energy building targets and sustainability of our cities. In practice, assessing the performance of AFs during the early stages of the design is still a challenging task due to their time-varying dynamic behaviour. Most current building performance tools (BPS) were originally developed to assess fixed façades where changes to the geometry of the façade are not taken into consideration during simulation. To that end, adaptive systems require a more complex workflow that can correctly predict their performance. This research is intended to assist architects and façade specialists in two main aspects; firstly, an algorithmic framework was developed to predict the energy performance of AFs in the early design stages. The algorithmic workflow creates a link between plug-ins including the Ladybug and Honeybee tools, and Energy Plus for running the simulation with the built- in tool energy management system (EMS) to program a code to actuate the AF system in an hourly time step. The workflow considers the time-varying dynamic behaviour of AFs based on different environmental parameters. The aim is to accurately evaluate the potential of AFs in the energy performance of an office tower. Secondly, by exploring the complexity and limitation of current tools, a novel method is proposed to assess the energy performance of AFs using machine learning (ML) techniques. Two different ML models, namely, an artificial neural network (ANN) and a Random Forest (RF), were developed to predict the energy performance of AFs in the early design stages in a significantly faster time compared to simulation. The surrogate models were trained, tested, and validated using the generated synthetic database by simulation (hourly cooling loads of AF and hourly solar radiation). During the training phase, a hyperparameters tuning procedure was carried out to select the most suitable surrogate model. By comparing the static shading system with AFs in terms of energy consumption, the results confirmed that the AFs were more effective in terms of cooling load reduction compared to static façades where cooling loads were reduced by 34.6%. The findings also revealed that the control scenario that triggered both incident solar radiation and operative temperature in a closed loop mechanism performed better than other control scenarios. Regarding the surrogate models, this research found that ML techniques can predict the hourly cooling loads of AFs with a high level of accuracy in the range of 85% to 99%. In particular, the RF model showed a 17% improvement in R2 accuracy over the ANN model in predicting the hourly cooling loads of AFs.19 0