Accelerating Network Slice Placement with Multi-Agent Reinforcement Learning

Cellular networks are increasingly realized through software-based entities, with core functions deployed as Virtual Network Functions (VNFs) on Commercial-off-the-Shelf (COTS) hardware. Network slicing has emerged as a key enabler of 5G by providing logically isolated Quality of Service (QoS) guarantees for diverse applications. With the adoption of cloud-native infrastructures, the placement of network slices across heterogeneous multi-cloud environments poses new challenges due to variable resource capabilities and slice-specific requirements. This paper introduces a modular framework for autonomous and near-optimal VNF placement based on a disaggregated Multi-Agent Reinforcement Learning (MARL) approach. The framework incorporates real traffic profiles to estimate slice resource demands and employs a MARL-based scheduler to minimize deployment cost while meeting QoS constraints. Experimental evaluation on a multi-cloud testbed shows a 19x speed-up compared to combinatorial optimization, with deployment costs within 7.8% of the optimal. While the method incurs up to 2.42x more QoS violations under high load, the trade-off provides significantly faster decision-making and reduced computational complexity. These results suggest that MARL-based approaches offer a scalable and cost-efficient solution for real-time network slice placement in heterogeneous infrastructures.