Optimized Recovery Point Selection for Distributed Systems Using AI-Enhanced Heuristic Search

Abstract

In today's digital world, organizations rely on distributed storage systems to manage vast amounts of data across multiple servers. Each server, or host, is responsible for storing a specific portion of the data and takes backups at different time intervals to ensure reliability and disaster recovery. However, these backups are not always synchronized, meaning that when a system failure occurs, restoring data from different recovery points can lead to inconsistencies. This can cause issues like missing or outdated information, transactional mismatches, and operational disruptions.  To solve this challenge, we propose an intelligent recovery point selection method that ensures the most consistent restoration of data. Our algorithm, inspired by the A* search technique, systematically evaluates all possible backup combinations and selects the set that minimizes the time difference across all hosts. By using an optimized heap-based selection process, it efficiently finds the most synchronized recovery points, reducing data inconsistency and improving reliability.  Unlike traditional recovery methods that rely on manual selection or simple rules, our approach is automated, scalable, and computationally efficient. It can be applied in industries such as cloud computing, finance, healthcare, and e-commerce, where maintaining accurate and consistent data is critical. In the future, our solution can be further enhanced with machine learning to predict failures and optimize recovery strategies.