Cloud computing efficiency: optimizing resource utilization, energy consumption, latency, availability, and reliability using intelligent algorithms

dc.contributor.advisorDatta, Amitava
dc.contributor.advisorGhulam, Mubasher Hassan
dc.contributor.authorAlelyani, Abdullah Hamed A
dc.date.accessioned2025-04-16T05:59:00Z
dc.date.issued2024
dc.description.abstractCloud 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.
dc.format.extent217
dc.identifier.urihttps://hdl.handle.net/20.500.14154/75202
dc.language.isoen
dc.publisherThe Universit of Western Australia
dc.subjectCloud Scheduling
dc.subjectMicroservices
dc.subjectCloud Computing
dc.subjectReinforcement Learning
dc.subjectDeep Q-Networks
dc.subjectMulti-objective Optimization
dc.subjectResource Management
dc.subjectLatency Reduction
dc.subjectContainer Technology
dc.subjectMachine Learning
dc.subjectDeep learning
dc.titleCloud computing efficiency: optimizing resource utilization, energy consumption, latency, availability, and reliability using intelligent algorithms
dc.typeThesis
sdl.degree.departmentSoftware System and Computer Science
sdl.degree.disciplineSoftware System and Computer Science
sdl.degree.grantorThe Universit of Western Australia
sdl.degree.nameDoctor of Philosophy
sdl.thesis.sourceSACM - Australia

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