Abstract

This systematic review is aimed towards the state-of-the-art reinforcement learning (RL) approaches towards the next-generation multi-cloud traffic engineering, through the existing 15 academic papers from 2021 to 2025. The study performs a critical review of the application of Multi-Agent Reinforcement Learning (MARLs), Multi-Agent Reinforcement Learning (GNNs), and hybrid optimization approaches to transform traffic management on distributed clouds. The review exposes notable advances in large-scale distributed decision-making, flexibility of routing under uncertainty, and cross-domain resource optimization. Despite the positive outcomes, the analysis highlights decades-old questions regarding safety guarantees, heterogenous infrastructure unification, and real-world deployment struggles. The research identifies future research challenges in transfer learning capabilities, explainability demands, and cross-layer optimization. This review aims to synthesize existing knowledge to inform future research on the design of fault-tolerant, efficient, and adaptive traffic engineering techniques for complex multi-cloud systems.

References

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