AI-Driven QoS Optimization in Multi-Cloud Environments: Investigating the Use of AI Techniques to Optimize QoS Parameters Dynamically Across Multiple Cloud Providers

Abstract

As businesses increasingly move towards multi-cloud environments for unique benefits of different cloud service providers (CSPs), ensuring optimal Quality of Service (QoS) becomes critical. QoS in multi-cloud environments involves balancing numerous parameters such as latency, throughput, availability, and resource allocation across multiple platforms. This paper explores the use of machine learning (ML) and deep learning (DL), for the dynamic optimization of QoS in multi-cloud environments. AI offers assistance to manage large-scale datasets, adapt to changing conditions, and learn from previous performance data to make intelligent decisions. The study focuses on how these AI techniques can minimize Service Level Agreement (SLA) violations, optimize resource usage, and enhance service reliability. The study investigates AI-driven approaches, such as reinforcement learning, neural networks, and predictive analytics to look into how automation in multi-cloud management can result in better resource efficiency, improved QoS, and reduced operational costs. This paper also discusses the challenges inherent in AI-driven multi-cloud management, such as data heterogeneity, system scalability, and security concerns. The application of AI to assist multi-cloud environments through real-time decision-making and predictive modeling is emphasized, showing how these technologies can transform the future of cloud computing infrastructure.