AI for Service-Level Agreement (SLA) Optimization in Microservices-Based Cloud Infrastructures

Service-Level Agreements (SLAs) act as a backbone for reliable performance of microservices-based cloud infrastructure. But due to fluctuating workloads, limited resources, and manual management of SLAs, maintaining SLA adherence has always been a challenge. This paper addresses how AI can be used for SLA optimization through predictive analytics, reinforcement learning (RL), and self-healing mechanisms for improving service availability and efficiency. In this research, we showcase a framework where AI helps in the integration of ML models for anomaly detection, RL-based resource allocation, and deep learning for fault prediction and recovery. With the experimental results, we show that AI-driven SLA management decreases latency by 52%, SLA violations by 67%, and downtime by 75% compared to the conventional rule-based methods. The proposed approach provides an avenue for proactive SLA compliance through real-time monitoring, dynamic resource provisioning, and automated fault handling. Our findings assert the advantage of AI for enhancing cloud scalability, optimizing microservices orchestration, and minimizing operational costs. AI-based method advantages over traditional ones include autonomously made SLA decisions, real-time adaptation to changing scenarios, and ability to optimize alongside multi-objectives essential for cutting-edge cloud architectures. The study evaluates how revolutionary AI can be in helping the SLA business and creates research pathways for federated learning, hybrid AI methodologies, and edge-cloud symbiosis.