Abstract
Bloom: Cloud Optimization tool" endeavors to harness the power of the Ant Colony Optimization (ACO) algorithm to address the challenge of organizing unstructured input data in cloud storage systems. The primary objective is to optimize cloud storage utilization and minimize associated billing expenses. Through the utilization of ACO, the solution aims to transform unorganized and unstructured data into a structured format, facilitating efficient storage allocation and enhancing data management practices. By leveraging predictive insights derived from the structured data, the solution empowers both cloud service providers and users to make informed decisions regarding data storage optimization. Ultimately, this approach not only streamlines data organization processes but also aids in maximizing cost-effectiveness by ensuring that cloud resources are allocated optimally based on usage patterns and predicted future demands. The integration of ACO into the solution offers a promising avenue for addressing the complexities inherent in managing vast amounts of unstructured data in cloud environments, thereby paving the way for improved efficiency and cost savings.
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