AI-Powered Dynamic Resource Allocation in Edge-Cloud Environments for Improved Data Integrity

Abstract

In the era of data-intensive applications and real-time processing, managing and maintaining data
integrity across edge-cloud environments presents a significant challenge. This study explores the
application of AI-powered dynamic resource allocation strategies to enhance data integrity and
operational efficiency in edge-cloud architectures. We propose a novel framework that integrates
machine learning algorithms for real-time monitoring and adaptive resource management. The
framework is evaluated through a series of simulations and real-world deployments, demonstrating
its effectiveness in improving data integrity and system performance. Our findings indicate that
AI-driven resource allocation can significantly reduce data loss and corruption while optimizing
resource utilization across distributed systems. The proposed approach not only addresses
traditional challenges in resource management but also introduces new capabilities for handling
dynamic workloads and ensuring high availability. This paper provides a comprehensive analysis
of the framework’s impact on data integrity, resource efficiency, and overall system reliability.