JoWUA

Abstract

This study presents an optimized model for data backup and recovery in multi-cloud environments, that specialize in improving performance, fee-effectiveness, and records integrity. The proposed model leverages multi-cloud architectures, deduplication, delta compression, and machine-getting-to-know-driven challenge optimization to reduce backup and healing times, decrease storage expenses, and beautify gadget resilience. Results from experiments display a 45% price discount for huge datasets, quicker restoration instances, and stepped-forward device availability through redundancy across multiple cloud carriers. However, the studies additionally highlight challenges which include the complexity of multi-cloud management, security worries associated with key control, and cost variability throughout providers. Future Paintings aims to cope with those challenges by way of developing simplified, scalable solutions, mainly for small and medium-sized firms (SMEs). The findings propose that the proposed model offers a strong and scalable solution for organizations seeking to optimize their backup and healing strategies in cloud environments.