Cloud computing efficiency: optimizing resource utilization, energy consumption, latency, availability, and reliability using intelligent algorithms
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Date
2024
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Publisher
The Universit of Western Australia
Abstract
Cloud 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.
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Keywords
Cloud Scheduling, Microservices, Cloud Computing, Reinforcement Learning, Deep Q-Networks, Multi-objective Optimization, Resource Management, Latency Reduction, Container Technology, Machine Learning, Deep learning