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May 6, 20260 citationsOpen Access

Experimental Evaluation of AI-Driven Sustainable Intelligent Cloud Optimization Framework

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PPPriyanka PurohitSCShambhuraj ChavanOPOmkar Patil

Key Points

  • To evaluate an AI-driven framework for optimizing personal cloud storage through intelligent management strategies.
  • Developed an integrated multi-modal AI framework for cloud environments.
  • Utilized techniques for intelligent file prioritization and redundant storage reduction.
  • Implemented AI-driven categorization and vision-language processing for image title suggestion.
  • Demonstrated effective file prioritization and improved video summarization.
  • Achieved high summarization compression efficiency and enhanced retrieval accuracy in personal cloud settings.

Abstract

This paper presents an integrated multi-modal Artificial Intelligence (AI) framework that uses intelligent file prioritization, redundant storage reduction, and automated content understanding to improve personal cloud environments. The system makes use of AI-driven categorization, vision-language processing for image title suggestion, and large language models (LLMs) for hierarchical document and video summarization. The suggested framework integrates multiple AI services into a single architecture, in contrast to traditional storage platforms like Dropbox and Google Drive. Effective file prioritization, high summarization compression efficiency, and enhanced retrieval accuracy are demonstrated by experimental evaluation across several modules. The outcomes confirm that creating an intelligent, context-aware digital workspace from traditional cloud storage is feasible.

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Cite This Study

Purohit et al. (2026) studied this question.

synapsesocial.com/papers/69faa2e204f884e66b53365chttps://doi.org/10.5281/zenodo.20022365
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