Enhancing Large-Scale System Reliability Through Integrated Site Reliability Engineering and Evidence-Based Error Budget Management

Abstract

In contemporary organizational and technological landscapes, the management of complex systems requires an integrated approach that combines rigorous evidence-based practices with advanced reliability engineering frameworks. This research investigates the intersection of site reliability engineering (SRE) and evidence-based practice (EBP), focusing on error budget management as a strategic mechanism to enhance operational efficiency, system stability, and risk mitigation. By synthesizing insights from large-scale computing environments, nursing and healthcare evidence-based frameworks, and organizational behavior theory, the study establishes a multidisciplinary perspective on reliability optimization. This article critically examines the theoretical foundations of SRE, operationalization of error budgets, and the application of EBP principles in decision-making processes across heterogeneous systems. The methodological framework employs a qualitative synthesis of empirical studies, document analysis, and thematic analysis to interrogate the challenges, enablers, and barriers to effective implementation. Findings reveal that systematic monitoring, iterative feedback mechanisms, and structured policy interventions are pivotal in maintaining optimal system performance, minimizing downtime, and ensuring alignment between technical operations and organizational objectives. Furthermore, this research elucidates the complex interplay between human factors, procedural adherence, and technological constraints, emphasizing the necessity of cross-disciplinary integration to achieve sustainable system reliability. The discussion situates these findings within ongoing scholarly debates regarding the efficacy of EBP adoption, the limitations of conventional SRE metrics, and the contextual variability inherent in large-scale infrastructures. Ultimately, this article provides a comprehensive framework for practitioners, policymakers, and researchers aiming to enhance system dependability through the convergence of empirical evidence and engineering best practices.