A Holistic View of AI-driven Network Incident Management
Name
3626111.3628176.pdf
Size
953.25 KB
Format
Adobe PDF
Checksum (MD5)
6a858151874c7b32c16ae98bd575c53e
Author(s)
Hamadanian, Pouya
Arzani, Behnaz
Fouladi, Sadjad
Kakarla, Siva Kesava Reddy
Fonseca, Rodrigo
Billor, Denizcan
Cheema, Ahmad
Nkposong, Edet
Chandra, Ranveer
Date Issued
November 28, 2023
Publisher
ACM|The 22nd ACM Workshop on Hot Topics in Networks
Citation
Hamadanian, Pouya, Arzani, Behnaz, Fouladi, Sadjad, Kakarla, Siva Kesava Reddy, Fonseca, Rodrigo et al. 2023. "A Holistic View of AI-driven Network Incident Management."
Version
Final published version
Abstract
We discuss the potential improvement large language models (LLM) can provide in incident management and how they can overhaul the ways operators conduct incident management today. We propose a holistic framework for building an AI helper for incident management and discuss the several avenues of future research needed to achieve it.
We thoroughly analyze the fundamental requirements the community should consider when designing such helpers. Our work is based on discussions with operators of a large public cloud provider and their prior experiences both in incident management and with attempts to improve the incident management experience through various forms of automation.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3626111.3628176
