A systematic review on resolving service level agreement violation in IT Services

Abstract

Information Technology (IT) services are crucial for modern organizations, making Service Level Agreements (SLAs) essential for ensuring quality and performance. However, SLA violations remain an unavoidable and significant challenge in IT service management, often leading to financial losses and reputational damage. The increasing complexity of IT landscapes, driven by cloud computing, IoT, and AI, complicates both root cause identification and resolution. This systematic literature review (SLR) identifies, evaluates, and synthesizes existing research on SLA violation resolution, with a particular focus on machine learning (ML) and deep learning (DL) methods. The study aims to identify relevant technology contexts, examine main problems in SLA violations, and determine how ML/DL methods are effectively used in their resolution. Findings indicate a strong research focus on cloud computing, with emerging attention to edge computing and IoT, and highlight the diverse application of ML/DL in mitigating SLA breaches.