Enhancing Network Management Using Code Generated by Large Language
Models
- URL: http://arxiv.org/abs/2308.06261v1
- Date: Fri, 11 Aug 2023 17:49:15 GMT
- Title: Enhancing Network Management Using Code Generated by Large Language
Models
- Authors: Sathiya Kumaran Mani, Yajie Zhou, Kevin Hsieh, Santiago Segarra,
Ranveer Chandra, and Srikanth Kandula
- Abstract summary: We introduce a novel approach to facilitate a natural-language-based network management experience, utilizing large language models (LLMs) to generate task-specific code from natural language queries.
This method tackles the challenges of explainability, scalability, and privacy by allowing network operators to inspect the generated code.
We design and evaluate a prototype system using benchmark applications, showcasing high accuracy, cost-effectiveness, and the potential for further enhancements.
- Score: 15.557254786007325
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Analyzing network topologies and communication graphs plays a crucial role in
contemporary network management. However, the absence of a cohesive approach
leads to a challenging learning curve, heightened errors, and inefficiencies.
In this paper, we introduce a novel approach to facilitate a
natural-language-based network management experience, utilizing large language
models (LLMs) to generate task-specific code from natural language queries.
This method tackles the challenges of explainability, scalability, and privacy
by allowing network operators to inspect the generated code, eliminating the
need to share network data with LLMs, and concentrating on application-specific
requests combined with general program synthesis techniques. We design and
evaluate a prototype system using benchmark applications, showcasing high
accuracy, cost-effectiveness, and the potential for further enhancements using
complementary program synthesis techniques.
Related papers
- Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestration [10.981422497762837]
Large language models (LLMs) are rapidly emerging in Artificial Intelligence (AI) applications.
This paper presents semantic routing to achieve enhanced performance in intent-based management and orchestration of 5G core networks.
arXiv Detail & Related papers (2024-04-24T13:34:20Z) - Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences.
It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations.
Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
arXiv Detail & Related papers (2024-02-20T15:23:24Z) - Unsupervised Graph Attention Autoencoder for Attributed Networks using
K-means Loss [0.0]
We introduce a simple, efficient, and clustering-oriented model based on unsupervised textbfGraph Attention textbfAutotextbfEncoder for community detection in attributed networks.
The proposed model adeptly learns representations from both the network's topology and attribute information, simultaneously addressing dual objectives: reconstruction and community discovery.
arXiv Detail & Related papers (2023-11-21T20:45:55Z) - COOL: A Constraint Object-Oriented Logic Programming Language and its
Neural-Symbolic Compilation System [0.0]
We introduce the COOL programming language, which seamlessly combines logical reasoning with neural network technologies.
COOL is engineered to autonomously handle data collection, mitigating the need for user-supplied initial data.
It incorporates user prompts into the coding process to reduce the risks of undertraining and enhances the interaction among models throughout their lifecycle.
arXiv Detail & Related papers (2023-11-07T06:29:59Z) - netFound: Foundation Model for Network Security [12.062547301932966]
We develop netFound, a foundational model for network security.
Our experiments demonstrate netFound's superiority over existing state-of-the-art ML-based solutions.
arXiv Detail & Related papers (2023-10-25T22:04:57Z) - Learning State-Augmented Policies for Information Routing in
Communication Networks [92.59624401684083]
We develop a novel State Augmentation (SA) strategy to maximize the aggregate information at source nodes using graph neural network (GNN) architectures.
We leverage an unsupervised learning procedure to convert the output of the GNN architecture to optimal information routing strategies.
In the experiments, we perform the evaluation on real-time network topologies to validate our algorithms.
arXiv Detail & Related papers (2023-09-30T04:34:25Z) - CodeRL: Mastering Code Generation through Pretrained Models and Deep
Reinforcement Learning [92.36705236706678]
"CodeRL" is a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning.
During inference, we introduce a new generation procedure with a critical sampling strategy.
For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives.
arXiv Detail & Related papers (2022-07-05T02:42:15Z) - A Tutorial on Ultra-Reliable and Low-Latency Communications in 6G:
Integrating Domain Knowledge into Deep Learning [115.75967665222635]
Ultra-reliable and low-latency communications (URLLC) will be central for the development of various emerging mission-critical applications.
Deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLC in future 6G networks.
This tutorial illustrates how domain knowledge can be integrated into different kinds of deep learning algorithms for URLLC.
arXiv Detail & Related papers (2020-09-13T14:53:01Z) - Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G
Networks [84.2155885234293]
We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC.
To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
arXiv Detail & Related papers (2020-02-22T14:38:11Z) - Synthetic Datasets for Neural Program Synthesis [66.20924952964117]
We propose a new methodology for controlling and evaluating the bias of synthetic data distributions over both programs and specifications.
We demonstrate, using the Karel DSL and a small Calculator DSL, that training deep networks on these distributions leads to improved cross-distribution generalization performance.
arXiv Detail & Related papers (2019-12-27T21:28:10Z)
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.