Analysis of Network Performance in Service Level Agreements (SLAs): Proactive Management and Advanced Troubleshooting Strategies in Modern Converged Environments

Abstract

In today's digital landscape, characterized by cloud infrastructures and software-defined networking (SDN), network performance management has evolved from a technical function to a strategic necessity to ensure business continuity and customer satisfaction. This paper provides a comprehensive analytical review of the critical importance of network performance management and troubleshooting techniques in modern complex environments. A paradigm shift from reactive to proactive management, based on establishing a robust “network performance baseline,” is essential for early detection of performance deviations and potential anomalies. This research paper systematically outlines the steps for establishing an effective baseline, from identifying key performance indicators (KPIs) and critical network devices to determining the optimal duration for data collection. Furthermore, multi-layered troubleshooting frameworks, such as the OSI and TCP/IP models, are examined as structured models for accurate problem diagnosis, with systematic approaches such as top-down, bottom-up, and divide-and-conquer discussed. The research culminates in a classification and evaluation of modern software and hardware tools, including Network Performance Monitoring and Diagnostics (NPMD) platforms and Artificial Intelligence for IT Operations (AIOps), that enable network engineers to enhance operational efficiency and meet the stringent requirements of Service Level Agreements (SLAs) in the era of converged digital solutions.