Optimization of Virtual Machines in Cloud-Based Distributed Systems for Enhanced Performance and Cost Efficiency
DOI:
https://doi.org/10.53819/81018102t2326Abstract
The advent of cloud computing has revolutionized the management of computing resources within organizations, offering flexible access to scalable infrastructure on demand. This thesis delved into the realm of optimization techniques for Virtual Machines (VMs) in cloud-based distributed systems, with a focus on enhancing system performance and cost efficiency. The research objectives encompassed evaluating performance metrics, devising optimization strategies for workload balancing, dynamic provisioning, and fault tolerance, and scrutinizing the correlation between workload characteristics and resource utilization. The study employed a qualitative approach, conducting open-ended interviews with IT personnel at Bank and utilizing both primary and secondary data, with analysis performed using SPSS. The overarching goal was to furnish insights and recommendations for the adoption of efficient VM strategies. The conclusions underscored the significance of strategies aimed at maximizing resource utilization, integrating workload balancing and dynamic provisioning for Bank's data centers, and enhancing services such as core banking and agency banking. The recommendations put forward the deployment of the proposed algorithms and models, with due consideration for optimizing heat and cooling efficiency in data centers. Furthermore, future research should delve into exploring novel models, algorithms, and robust platforms, such as Microsoft Azure, to continually optimize virtual resources, thereby ensuring both cost efficiency and service performance.
Keywords: Optimization of Virtual Machines, Cloud-Based Distributed Systems, Enhanced Performance, Cost Efficiency.
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