AI-Powered Server Log Management for Automated Error Resolution

Abstract

Modern technology environments generate vast amounts of server logs, each potentially containing critical information about system errors. Traditional methods of resolving these errors typically involve time-consuming manual searches across multiple platforms—ranging from search engines like Google and Bing to various online forums—in hopes of finding the correct solution. This process often proves inefficient, as users must sift through extensive search results and compare inconsistent or irrelevant information, risking further errors and delays. In response, this research aims to develop an AI-powered server log management software that delivers accurate, automated solutions to errors by analyzing historical log data and corresponding resolutions. By consolidating server logs and training a predictive AI model, the proposed platform offers a one-stop solution capable of reducing the time, effort, and complexity currently associated with error resolution. Users simply input an error, and the system provides an intelligently derived, context-aware solution—eliminating the need for manual searches. In doing so, the platform streamlines workflows, reduces user frustration, and improves overall efficiency in managing complex technical issues in real-world environments.