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View all- Ghasemirahni HFarshin AScazzariello MMaguire GKostić DChiesa M(2024)FAJITA: Stateful Packet Processing at 100 Million ppsProceedings of the ACM on Networking10.1145/36768612:CoNEXT3(1-22)Online publication date: 21-Aug-2024
Deploying Stateful Network Functions Efficiently using Large Language Models
Authors: 
Hamid Ghasemirahni, 
Alireza Farshin, 
Mariano Scazzariello, 
Marco Chiesa, 
Dejan KostićAuthors Info & Claims
Pages 28 - 38
Abstract
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 improvement (up to 11×) in comparison to the default configuration of the system.
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- Deploying Stateful Network Functions Efficiently using Large Language Models
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April 2024
218 pages
ISBN:9798400705410
DOI:10.1145/3642970
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View all- Ghasemirahni HFarshin AScazzariello MMaguire GKostić DChiesa M(2024)FAJITA: Stateful Packet Processing at 100 Million ppsProceedings of the ACM on Networking10.1145/36768612:CoNEXT3(1-22)Online publication date: 21-Aug-2024
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