Automated SLO Threshold Optimization Using Historical Monitoring Data

Abstract

Service Level Objectives (SLOs) are a cornerstone of modern reliability engineering, providing measurable targets that ensure system performance aligns with user expectations and contractual Service Level Agreements (SLAs). Traditionally, SLO thresholds are set manually, often relying on expert judgment or static performance benchmarks, which can lead to either overly conservative or excessively lenient targets. This research introduces a novel automated framework for SLO threshold optimization that leverages historical monitoring data from large-scale distributed systems. Using advanced machine learning algorithms and time-series statistical models, the framework dynamically recalibrates SLO thresholds based on evolving workload patterns, system dependencies, and incident history.

The study employs real-world datasets from production-grade observability platforms, covering over 1.2 billion metric records from 2019 to 2025 across sectors including cloud computing, financial services, and e-commerce. Experimental results indicate that the proposed system improves SLA compliance rates by an average of 12.7% while reducing false positive alerts by 34.5%. The optimization process also achieves an estimated annual operational cost reduction of 18%, primarily by minimizing unnecessary incident escalations and aligning resources with true service degradation risks. However, challenges remain in data quality, model interpretability, and integration with heterogeneous monitoring architectures. This research provides a practical blueprint for engineering teams seeking to modernize their SLO management through data-driven automation.