AI-DRIVEN ADAPTIVE REPLICA PLACEMENT AND REBALANCING IN DISTRIBUTED STORAGE SYSTEMS

Abstract

This article presents an innovative AI-driven framework for adaptive replica placement and rebalancing in distributed storage systems. The framework addresses critical challenges in maintaining optimal performance and reliability in dynamic storage environments through a sophisticated three-component architecture. By leveraging advanced machine learning techniques, including Deep Q-Networks and Proximal Policy Optimization algorithms, the system demonstrates superior capability in workload prediction, resource optimization, and automated decision-making. The framework incorporates knowledge-driven approaches for performance prediction, intelligent placement strategies for load balancing, and reinforcement learning mechanisms for continuous system improvement. Through comprehensive experimental validation across various deployment scenarios, the framework exhibits significant improvements in latency reduction, resource utilization, and system reliability compared to traditional approaches. The solution's ability to adapt to changing conditions while maintaining strict service level agreements represents a paradigm shift in distributed storage management, offering promising implications for future cloud and edge computing environments.