Evolving Towards Optimal Cloud Resource Allocation and Cost Management: An In-depth Analysis
Sourabh Pal, Krishna Kant Agrawal, Baskar Kasi · 2024
This research paper explores the field of cloud computing optimisation, with the goal of tackling the two-fold difficulty of efficiently allocating resources and effectively managing costs. The primary objective is to achieve a balance between optimising the allocation of resources to enhance performance and minimising operational expenses in cloud systems. Our approach incorporates a systematic methodology that includes a thorough examination of existing literature, the development of theoretical models, and the execution of substantial experiments. The suggested analysis indicates that dynamic allocation, load balancing, and auto-scaling techniques have significant potential in reaching this balance. Dynamic allocation adjusts resources in real-time to accommodate varying workloads, while load balancing maintains equitable distribution of resources. Auto-scaling stands out as the top performer, with a significant 28% reduction in costs and a noteworthy 30% boost in performance. The proposed research expands upon previous studies by offering a comprehensive evaluation of resource allocation options within the existing approaches. It highlights the feasibility of obtaining cost reductions while concurrently improving performance in cloud systems. However, the suggested study recognises specific constraints, including the utilisation of synthetic workload data and simplified cloud infrastructures. The research has significant ramifications as it provides guidance to organisations on how to optimise their allocation of cloud resources and manage costs. This ultimately promotes efficient and cost-effective cloud operations.