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 Sustainable and Cost-Effective Work Zone Scheduling on Two-Lane Highways with Managed Lanes(New Jersey Institute of Technology, 2024-04-23) Edrees, Ahmed; Chien, StevenRoadway maintenance projects greatly influence the roadway capacity, resulting in potential traffic disturbances captured by delays. Additionally, costs associated with these projects tend to be exorbitantly extensive. Most agencies and planners try to find a solution that minimizes roadway maintenance costs, traffic delays, crash risks, and environmental impact. Work zones on two-way two-lane roadway typically avoids high-demand periods. Lane-closure scenario is commonly used and converts the open lane into a phantom intersection, alternating two-direction movements on one lane with the help of a flagger or a temporary signal. Alternatively, using shoulders as temporarily managed lanes allows for simultaneous two-way movements with minimal interruptions. This scenario can potentially enhance the efficiency of the work zone by allowing for longer work zone segments and fewer setups, while increasing the project cost due to shoulder preparation cost, which is sensitive to the condition of the existing shoulders and the amount of preparation work needed. This study addresses the feasibility of utilizing managed lanes scenarios for two-way two-lane highways, while previous work focused on assessing and optimizing one-lane scenarios. The objective of this study is to develop a cost optimization algorithm and resilience assessment model for work zone scenarios on two-way two-lane highways. The cost optimization process assesses the trade-offs between agency, user, accident, and emission costs. This study enhances several assumptions and limitations of previously developed models by accounting for hourly demand variations, heavy vehicle presence, and work zone buffer areas. Additionally, this study utilizes the latest models for crash risk predictions as illustrated in the Highway Safety Manual (HSM) and emission rate simulator developed by the Environmental Protection Agency (EPA). The results of the optimization models serve as framework for comparison of potential scheduling schemes by exploring the effects of traffic demand variations, work zone lengths, and project starting times, while taking into consideration scheduling restraints, accident risks, and emission standards.17 0Item Restricted Optimising IDS configurations for IoT Networks Using AI approaches(Saudi Digital Library, 2023) Alshahrani, Abdulmonem; John A. ClarkThe number of internet-connected smart objects, known as the Internet of Things (IoT), has increased significantly in recent years. The low cost of manufacturing has enabled a proliferation of smart devices across many tasks and domains. Such devices, however, are typically resource constrained. This has led to the emergence of Low-Power and Lossy Networks (LLNs) which require efficient communication protocols. The Routing Protocol for Low-Power and Lossy Networks (RPL) has been designed for such a purpose. The RPL is the de-facto standard routing protocol for the IoT. Nevertheless, RPL-enabled networks are susceptible to many attacks as these devices are unattended, resource-constrained, and connected via unreliable networks. Deploying Intrusion Detection Systems (IDSs) in such a large and resource-constrained environment is a challenging task. The resource-constrained nature of many devices and nodes restricts what tasks those nodes can realistically expect to perform. There may be a great many choices as to what detection functionality is allocated and where. There are cost/benefit trade-offs between them and inappropriately favouring one over the another may cause an ineffective IDS deployment. In this research, we investigate the use of a metaheuristic- based optimisation method, namely a Genetic Algorithm (GA), to discover optimal IDS placements and configurations for the Low Power and Lossy Networks (LLNs). To the best of our knowledge, this is the first attempt to optimise IDS configurations for emerging and constrained networks while incorporating a wider set of aspects than currently considered. Our approach seeks to optimise and balance detection performance (either detection rate or F1 score), coverage (nodes are monitored by an appropriate number of probes), feasibility cost (nodes host detection functionality within their capability), and deployment cost (seeking to reduce the number of probes deployed). We propose a framework that makes trades-offs between these functional and non-functional constraints. A genetic algorithm-based optimisation approach is developed to address the IDS optimisation task. However, the fitness function is evaluated in part via a computationally expensive simulation. We show how a neural network can be used as a surrogate fitness function evaluation, providing better results more cheaply. Experimental results show that the proposed function approximation is more computationally efficient. Our approximation-based GA system is 1.6 times faster than the corresponding simulation-based GA system. It also gives better results. Furthermore, when used repeatedly to generate candidate placements and configurations the resource costs per generation reduce drastically. The surrogate model is valuable as it significantly reduces the evaluation time and computation. However, generality is still a limitation. Therefore, we propose a transfer-learning Deep Neural Networks (DNNs) approach, that harnesses the experience of previously trained neural networks, to develop a general proxy model for evaluating IDS configurations of variant newly-presented networks more accurately.27 0