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Deploying Stateful Network Functions Efficiently using Large Language Models
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-0034-5098
NVIDIA Stockholm, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-9780-873X
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-9675-9729
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2024 (English)In: EuroMLSys 2024 - Proceedings of the 2024 4th Workshop on Machine Learning and Systems, Association for Computing Machinery (ACM) , 2024, p. 28-38Conference paper, Published paper (Refereed)
Abstract [en]

Stateful network functions are increasingly used in data centers. However, their scalability remains a significant challenge since parallelizing packet processing across multiple cores requires careful configuration t o avoid compromising the application’s semantics or performance. This challenge is particularly important when deploying multiple stateful functions on multi-core servers. This paper proposes FlowMage, a system that leverages Large Language Models (LLMs) to perform code analysis and extract essential information from stateful network functions (NFs) prior to their deployment on a server. FlowMage uses this data to find an efficient configuration of an NF chain that maximizes performance while preserving the semantics of the NF chain. Our evaluation shows that, utilizing GPT-4, FlowMage is able to find and apply optimized configuration when deploying stateful NFs chain on a server, resulting in significant p erformance i mprovement (up t o 1 1×) in comparison to the default configuration of the system.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2024. p. 28-38
Keywords [en]
Intra-Server Load Balancing, LLMs, RSS Configuration, Stateful Network Functions, Static Code Analysis
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-346539DOI: 10.1145/3642970.3655836Scopus ID: 2-s2.0-85192276579OAI: oai:DiVA.org:kth-346539DiVA, id: diva2:1858455
Conference
4th Workshop on Machine Learning and Systems, EuroMLSys 2024, held in conjunction with ACM EuroSys 2024, Athens, Greece, Apr 22 2024
Note

QC 20240520

Part of ISBN 979-840070541-0

Available from: 2024-05-16 Created: 2024-05-16 Last updated: 2024-05-20Bibliographically approved

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Scazzariello, MarianoChiesa, MarcoKostic, Dejan

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  • apa
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