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Large Language Models meet Network Slicing Management and Orchestration

Abdulhalim Dandoush, Viswanath Kumarskandpriya, Mueen Uddin, Usman Khalil

arXiv:2403.13721v1cs.NIcs.AI

TL;DR

Multi-domain slicing management must handle varied service requirements across administrative boundaries, while users may express needs only as high-level intent. The paper proposes collaborative LLM agents to support end-to-end slice management; it presents this as a framework vision and identifies implementation challenges.

  • Problem

    Existing orchestration approaches face difficulty handling complex service demands across administrative domains, where users may provide high-level requests rather than technical requirements.

    Source: §I · pp. 1–2

  • Method

    The paper proposes LLM agents that collaborate across domains, interpret user intent, share abstracted network information, and support mapping requirements to network functions and configurations.

    Source: §I · p. 2, §IV · p. 5, 2) Network Operator point of view · p. 10

  • Results

    The proposed framework maps agents to ETSI and 3GPP visions and describes LLM assistance from request initiation through slice deployment and monitoring.

    Source: §IV · p. 5, §VI · p. 11

  • Takeaways & Limitations

    The paper presents collaborative LLM-agent management as a future approach to end-to-end slicing across heterogeneous administrative domains.

    Source: §IV · p. 5

    Slice-profile diversity can make lifecycle management and adaptation to changing service or resource requirements difficult.

    Source: A. Limitations and Challenges in… · p. 4

Abstract

from arXiv · show

Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them.

I. INTRODUCTION

Network slicing offers customizable virtual networks, but existing orchestration struggles with diverse service needs across administrative domains. The paper presents LLM-based multi-agent management as a vision for translating user intent and coordinating slice operations.

I. INTRODUCTION

  • Network slices create isolated virtual networks on shared infrastructure, with dedicated connectivity, storage, or computing resources tailored to applications.Examples include low-latency support for autonomous vehicles and high bandwidth for virtual reality.Source: §I · p. 1
  • Current orchestration approaches struggle with complex QoS demands across administrative domains, while users may express slice needs only as high-level human requests.The introduction notes that separate domain systems and protocols complicate end-to-end creation, management, and resource allocation.Source: §I · pp. 1–2
  • LLM agents are proposed to translate user intent into technical requirements, share abstracted topology information across domains, and coordinate resource allocation and network-function mapping.The agents may span access, transport, core, and cloud networks and support configuration generation and monitoring.Source: §I · p. 2
  • The authors describe this as the first work to explore LLMs for Self-Driving Network Slices.They frame the work as exploration of the technology’s potential.Source: §I · p. 2

II. LARGE LANGUAGE MODELS OVERVIEW

  • LLMs use transformer architectures with attention mechanisms, and prior work has explored natural-language-to-code generation for network management and virtual assistants for customer support.The overview also describes training on large text and code datasets.Source: §II · pp. 2–3

III. BASIC E2E NETWORK SLICING ARCHITECTURE

The section introduces an end-to-end slicing architecture in which tenants express service needs, providers map them to abstracted infrastructure, and domain agents deploy connected segments.

III. BASIC E2E NETWORK SLICING ARCHITECTURE

  • The architecture distinguishes tenants, network-slice providers, and network-slice agents, with agents controlling individual network domains.The ETSI reference architecture is presented alongside the complementary 3GPP view.Source: §III · p. 3
  • A tenant service profile specifies a service graph and attributes, while a provider resource database aggregates abstracted topology information from agents across domains.The graph represents compute, storage, service-instance types, and constraints such as link capacity.Source: §III · p. 3
  • The provider maps a service profile to abstracted infrastructure, then delegates network segments to agents for end-to-end deployment.A segment can include paths and nodes such as a load balancer or firewall function.Source: §III · p. 3
  • In the 3GPP view, the CSMF translates customer requirements into technical network-slice requirements, the NSMF manages slice instances, and the NSSMF manages subnet operations.Tenants can also request runtime resource augmentation, including changes to latency constraints.Source: §III · p. 3

A. Limitations and Challenges in Applying Network Slicing Framework

Multi-domain slicing faces coordination, interoperability, security, and lifecycle-management challenges. The proposed LLM multi-agent approach shares abstracted domain information to support collaborative end-to-end management.

A. Limitations and Challenges in Applying Network Slicing Framework

  • Service heterogeneity can make real-time orchestration difficult and may delay responses to changing demands; diverse slice profiles also complicate adaptation as requirements change.Continuous monitoring is identified as necessary for compliance with differing regulatory frameworks.Source: A. Limitations and Challenges in… · p. 4
  • Diverse service profiles and administrative domains complicate SLA alignment, interoperability, security, resource coordination, policy alignment, dynamic orchestration, lifecycle management, and regulatory compliance.The section identifies standardization and collaboration among standards bodies, operators, and service providers as relevant to seamless deployment.Source: A. Limitations and Challenges in… · pp. 3–4

IV. PROPOSED LLM-BASED SLICING MANAGEMENT APPROACH

  • The proposed system maps agents to administrative domains or SDN controllers, while orchestrator agents communicate abstracted topology and capacity to agents in other domains.This is presented as a collaborative approach to end-to-end slicing management.Source: §IV · pp. 4–5
  • The envisioned LLM-agent role spans assistance with high-level user requests through deployment and monitoring of slice functions across access, edge, core, and cloud.The paper describes collaboration for both network operators and consumers.Source: §IV · p. 5

A. From Customer request to auto deployment

The proposed workflow connects natural-language slice requests to technical profiles, then uses LLM agents to model and deploy slices under consumer or operator goals. It spans user interaction, infrastructure-aware planning, and resource management across administrative domains.

1) Service Consumer point of view:

  • LLMs translate consumer requests into standardized technical requirements and can negotiate relaxed constraints for user approval when network capacity is insufficient.The requirements include latency, throughput, and reliability; the requested relaxation is proposed by an agent and requires user approval.Source: 1) Service Consumer point of view · p. 5
  • For telemedicine, a service may use separate high-throughput video and ultra-low-latency command slices, while an LLM can support requests beyond a predefined service catalogue.The catalogue's predefined function chains cannot cover every possible request, so a chatbot agent can help form new requests and suggest function chains.Source: 1) Service Consumer point of view · p. 5

2) Network Operator point of view:

  • Network operators express slice goals alongside infrastructure information, and agents model candidate topologies against constraints such as average slice utilization above 80 percent.The operator reviews topology recommendations and may select one or provide feedback to fine-tune the modeling task.Source: 2) Network Operator point of view · pp. 7–8
  • After topology selection, deployment and resource-management agents generate standards-based descriptors and interact with network-slice management systems and NFV-Os.The described outputs include SliceProfile, Network Service Descriptor, and Network Function Descriptor.Source: 2) Network Operator point of view · p. 8
  • LLMs map requirements to network functions and placement, then adjust computing, memory, storage, and bandwidth using collected infrastructure statistics.The proposed resource management includes scaling resources up or down while respecting customer SLAs.Source: 2) Network Operator point of view · pp. 9–10

A. Current Challenges

The paper describes practical challenges to integrating LLMs into end-to-end network slicing, proposes measures to address them, and outlines a framework spanning slice deployment and ongoing management.

A. Current Challenges

  • LLM-assisted slice management faces dataset, compute, security, trust, validation, interoperability, and privacy challenges.Synthetic data may help with dataset scarcity, but its authenticity and representativeness matter; the paper also calls for testing, validation, and common interfaces.Source: A. Current Challenges · p. 10

B. Future Directions and Solutions

  • Proposed responses include collaborative and federated learning, explainability, continuous auditing, ethical safeguards, user-centric design, and cross-sector collaboration.Federated learning is proposed for training across distributed network environments while preserving data privacy and security.Source: B. Future Directions and Solutions · pp. 10–11

VI. CONCLUSION

  • The proposed framework maps agents to ETSI and 3GPP visions and supports user requests, deployment, resource monitoring, auto-scaling, and allocation optimization.The conclusion also notes challenges including data availability, computing power, security backdoors, trust and validation, interoperability, and privacy.Source: §VI · p. 11

APPENDIX

The appendix describes LLM-assisted agents and tools for modeling, simulating, deploying, and managing network slices and their resources.

Slice modeling and simulation

  • The Slice Modelling Agent runs experiments across network topologies to recommend slice profiles and topologies under given constraints.The slicenet tool takes YAML experiment files and returns topology recommendations and slice profiles.Source: APPENDIX · p. 13

Agent workflow

  • The on-device agent breaks a design or deployment goal into tasks, assigns agents, supplies inputs, and chains outputs between tasks.Its prompt directs it to collect each task’s output and process it before passing it to subsequent tasks when needed.Source: APPENDIX · p. 13

Slice modeling and simulation

  • The slicenet-preparer generates YAML experiment files from modeling requirements, constraints, and network infrastructure.These files describe the experiment goal and are used as input to the slicenet simulation tool.Source: APPENDIX · p. 13

Deployment

  • The NSI preparer generates slice and subnet templates as ETSI TOSCA deployment descriptors; the deployer uses descriptors to create or discover NSIs and return instance information.The listed output includes network slice instance information such as a Slice ID and other deployment information.Source: APPENDIX · p. 13

Resource management

  • The Resource Management Agent handles virtual or physical infrastructure resources and VIM-layer allocation; resource-manager actions include start, stop, scale, restart, and delete.The resource-manager tool returns network-function monitoring information.Source: APPENDIX · pp. 13–14
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