Accelerating Performance Issue Detection in Distributed Systems Using Automated Latency Fingerprinting

Abstract

Managing modern distributed systems can be challenging due to their complexity and scale, making it difficult to quickly identify performance issues. Traditional monitoring often falls short, delaying responses to critical incidents. To tackle this, we propose Automated Latency Fingerprinting (ALF), an innovative approach that speeds up the diagnosis of performance issues by creating unique "latency signatures." ALF combines historical data analysis with real-time detection techniques to quickly pinpoint issues and recommend solutions. Our extensive tests show ALF significantly cuts down the time needed to detect and resolve problems, enhancing overall system reliability. By continuously learning from past incidents, ALF adapts dynamically, becoming increasingly effective in diverse operational environments. This document elaborates on the components, performance evaluations, real-world applications, challenges, solutions, and future research directions for ALF.