Realizing High-Performance Stateful Network Function Chains on Commodity Hardware
Improving Packet Processing Frameworks by Minimizing Memory Access Overheads
Time: Mon 2024-11-18 15.00
Location: Sal C (Sven-Olof Öhrvik), seminar, Kistagången 16, plan 2, Kista
Video link: https://kth-se.zoom.us/j/65526555288
Language: English
Subject area: Information and Communication Technology Computer Science
Doctoral student: Hamid Ghasemirahni , Programvaruteknik och datorsystem, SCS
Opponent: Senior Research Fellow Gabor Retvari, Budapest University of Technology and Economics
Supervisor: Dejan Kostic, Programvaruteknik och datorsystem, SCS; Marco Chiesa, Programvaruteknik och datorsystem, SCS
QC 20241018
Abstract
Data centers increasingly deploy commodity servers with high-speed network interface cards to enable network services with low latency and high throughput capabilities. However, existing packet processing systems still suffer from high I/O and memory access overheads, especially when deploying stateful network functions, resulting in degraded performance.
This doctoral dissertation describes our attempts to improve the performance of stateful network functions deployed on commodity servers by carefully studying traffic properties in heterogeneous data centers, low-level analysis of existing systems' bottlenecks, and finally proposing solutions to optimize stateful packet processing and alleviating systems' memory and I/O overheads.
The first contribution of this dissertation studies the impact of temporal and spatial traffic locality on the performance of commodity servers. Accordingly, we propose Reframer as a system that deliberately delays packets and reorders them to increase traffic locality. By deploying Reframer in front of a network function chain the system achieves up to 84% improvement in throughput and reduces the flow completion time of a web server by 11%.
The second contribution of this dissertation focuses on optimizing packet processing frameworks when deploying a chain of stateful network functions with various flow definitions. We identify three commonly practiced principles that are essential to achieve high performance. We propose FAJITA as a cache-friendly stateful packet processing framework that improves the performance of stateful network function service chains compared to existing state-of-the-art solutions, by at least 2.4× & 1.5× when using shared-nothing & shared architectures, respectively.
The third contribution of this dissertation takes one step further in optimizing packet processing frameworks by automatically configuring RSS before deploying a stateful network function chain on a commodity server. We propose FlowMage, a system that leverages Large Language Models (LLMs) to perform code analysis and extract essential information from stateful network functions. FlowMage uses this data to find an efficient configuration of a network function chain while preserving the semantics of the NF chain. FlowMage achieves a significant performance improvement (up to 11×) in comparison to the default configuration of the system.