Constrained Network Slice Assignment via Large Language Models

• URL: http://arxiv.org/abs/2512.00040v1
• Date: Fri, 14 Nov 2025 07:47:42 GMT
• Title: Constrained Network Slice Assignment via Large Language Models
• Authors: Sagar Sudhakara, Pankaj Rajak,
• Abstract summary: We explore the use of Large Language Models (LLMs) to tackle radio resource allocation for network slicing.<n>We show that an LLM, even with zero-shot prompting, can produce a reasonable first draft of slice assignments.<n>We then incorporate the LLM's understanding of service requirements into an optimization solver to generate an improved allocation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern networks support network slicing, which partitions physical infrastructure into virtual slices tailored to different service requirements (for example, high bandwidth or low latency). Optimally allocating users to slices is a constrained optimization problem that traditionally requires complex algorithms. In this paper, we explore the use of Large Language Models (LLMs) to tackle radio resource allocation for network slicing. We focus on two approaches: (1) using an LLM in a zero-shot setting to directly assign user service requests to slices, and (2) formulating an integer programming model where the LLM provides semantic insight by estimating similarity between requests. Our experiments show that an LLM, even with zero-shot prompting, can produce a reasonable first draft of slice assignments, although it may violate some capacity or latency constraints. We then incorporate the LLM's understanding of service requirements into an optimization solver to generate an improved allocation. The results demonstrate that LLM-guided grouping of requests, based on minimal textual input, achieves performance comparable to traditional methods that use detailed numerical data, in terms of resource utilization and slice isolation. While the LLM alone does not perfectly satisfy all constraints, it significantly reduces the search space and, when combined with exact solvers, provides a promising approach for efficient 5G network slicing resource allocation.

Related papers

• Cluster Topology-Driven Placement of Experts Reduces Network Traffic in MoE Inference [49.141930185079325]
  We propose an integer linear program (ILP) that determines the optimal placement of experts, minimizing the expected number of transmissions.<n>We demonstrate that ILP-based placement strategy yields lower network traffic than competitors for small-scale (DeepSeekMoE16B) and large-scale (DeepSeek-R1671B) models.
  arXiv  Detail & Related papers  (2025-08-12T07:08:48Z)
• PickLLM: Context-Aware RL-Assisted Large Language Model Routing [0.5325390073522079]
  PickLLM is a lightweight framework that relies on Reinforcement Learning (RL) to route on-the-fly queries to available models.<n>We demonstrate the speed of convergence for different learning rates and improvement in hard metrics such as cost per querying session and overall response latency.
  arXiv  Detail & Related papers  (2024-12-12T06:27:12Z)
• ALISE: Accelerating Large Language Model Serving with Speculative Scheduling [7.367068885621016]
  Large Language Models (LLMs) represent a revolutionary advancement in the contemporary landscape of artificial general intelligence (AGI) In this paper, we propose a new efficient LLM inference serving framework, named ALISE. We show that ALISE improves the throughput of inference serving by up to 1.8x and 2.1x under the same latency constraint on the Alpaca and ShareGPT datasets, respectively.
  arXiv  Detail & Related papers  (2024-10-31T00:58:11Z)
• SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization [70.11167263638562]
  Social relation reasoning aims to identify relation categories such as friends, spouses, and colleagues from images. We first present a simple yet well-crafted framework named name, which combines the perception capability of Vision Foundation Models (VFMs) and the reasoning capability of Large Language Models (LLMs) within a modular framework.
  arXiv  Detail & Related papers  (2024-10-28T18:10:26Z)
• SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration [10.970637831760136]
  Speculative decoding (SD) has emerged as a widely used paradigm to accelerate LLM inference without compromising quality.<n>We introduce SWIFT, an on-the-fly self-speculative decoding algorithm that adaptively selects intermediate layers of LLMs to skip during inference.<n>Our experiments demonstrate that SWIFT can achieve over a 1.3x-1.6x speedup while preserving the original distribution of the generated text.
  arXiv  Detail & Related papers  (2024-10-09T14:15:30Z)
• Preble: Efficient Distributed Prompt Scheduling for LLM Serving [8.706905652975554]
  This paper proposes Preble, the first distributed LLM serving platform that targets and optimize for prompt sharing. We designed a distributed scheduling system that co-optimizes KV state reuse and computation load-balancing with a new scheduling algorithm and a hierarchical scheduling mechanism. Our evaluation of Preble with real workloads and request arrival patterns on two open-source LLMs shows that Preble outperforms the SOTA serving systems by 1.5X to 14.5X on average latency and 2X to 10X on p99 latency.
  arXiv  Detail & Related papers  (2024-05-08T06:30:58Z)
• BiLLM: Pushing the Limit of Post-Training Quantization for LLMs [53.31402059062365]
  BiLLM is a groundbreaking 1-bit post-training quantization scheme tailored for pretrained large language models. It achieves for the first time high-accuracy inference (e.g. 8.41 perplexity on LLaMA2-70B) with only 1.08-bit weights across various LLMs families.
  arXiv  Detail & Related papers  (2024-02-06T09:26:34Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)
• LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying [71.86163159193327]
  Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. We introduce LaGR, which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent.
  arXiv  Detail & Related papers  (2023-08-21T02:07:35Z)
• LLM-Pruner: On the Structural Pruning of Large Language Models [65.02607075556742]
  Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. We tackle the compression of LLMs within the bound of two constraints: being task-agnostic and minimizing the reliance on the original training dataset. Our method, named LLM-Pruner, adopts structural pruning that selectively removes non-critical coupled structures.
  arXiv  Detail & Related papers  (2023-05-19T12:10:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.