Datta, AmitavaGhulam, Mubasher HassanAlelyani, Abdullah Hamed A2025-04-162024https://hdl.handle.net/20.500.14154/75202Cloud 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.217enCloud SchedulingMicroservicesCloud ComputingReinforcement LearningDeep Q-NetworksMulti-objective OptimizationResource ManagementLatency ReductionContainer TechnologyMachine LearningDeep learningThesis