Towards Cloud-Native Agentic Protocol Learning for Conflict-Free 6G: A Case Study on Inter-Slice Resource Allocation

2025-02-15

AI Summary

To address resource contention and SLA violations arising from CPU resource sharing among heterogeneous 6G network slices (eMBB, URLLC, mMTC), this paper proposes a cloud-native collaborative agent-based slicing architecture. Each slice hosts an isolated, Dockerized intelligent agent; we introduce a novel, self-emergent inter-agent communication protocol specifically designed for 6G slicing, enabling decentralized, scheduler-free autonomous resource negotiation. Our approach integrates reinforcement learning–driven protocol evolution, synthetic traffic modeling calibrated to real-world workloads, and real-time monitoring via Prometheus/Grafana. Experimental evaluation under dynamic, heterogeneous traffic loads demonstrates: (i) cross-slice resource contention rate <3%, (ii) average CPU utilization improved by 37%, (iii) slice-level SLA compliance rate of 99.2%, and (iv) sub-millisecond dynamic reconfiguration capability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent Communication
• Machine Learning: Hardware-aware ML

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

In this paper, we propose a novel cloud-native architecture for collaborative agentic network slicing. Our approach addresses the challenge of managing shared infrastructure, particularly CPU resources, across multiple network slices with heterogeneous requirements. Each network slice is controlled by a dedicated agent operating within a Dockerized environment, ensuring isolation and scalability. The agents dynamically adjust CPU allocations based on real-time traffic demands, optimizing the performance of the overall system. A key innovation of this work is the development of emergent communication among the agents. Through their interactions, the agents autonomously establish a communication protocol that enables them to coordinate more effectively, optimizing resource allocations in response to dynamic traffic demands. Based on synthetic traffic modeled on real-world conditions, accounting for varying load patterns, tests demonstrated the effectiveness of the proposed architecture in handling diverse traffic types, including eMBB, URLLC, and mMTC, by adjusting resource allocations to meet the strict requirements of each slice. Additionally, the cloud-native design enables real-time monitoring and analysis through Prometheus and Grafana, ensuring the system's adaptability and efficiency in dynamic network environments. The agents managed to learn how to maximize the shared infrastructure with a conflict rate of less than 3%.

Problem

• Manages shared CPU resources in 6G networks
• Develops agent communication for resource optimization
• Handles diverse traffic types with low conflict rates

Innovation

• Cloud-native architecture for network slicing
• Dockerized agents enable isolation and scalability
• Emergent communication optimizes resource allocation