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FAJITA: Stateful Packet Processing at 100 Million pps
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0002-0034-5098
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS, Network Systems Laboratory (NS Lab). NVIDIA, Stockholm, Sweden.ORCID iD: 0000-0001-5083-4052
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, Communication Systems, CoS.ORCID iD: 0000-0002-6066-746X
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2024 (English)In: Proceedings of the ACM on Networking, E-ISSN 2834-5509, Vol. 2, no CoNEXT3, p. 1-22Article in journal (Refereed) Published
Abstract [en]

Data centers increasingly utilize commodity servers to deploy low-latency Network Functions (NFs). However, the emergence of multi-hundred-gigabit-per-second network interface cards (NICs) has drastically increased the performance expected from commodity servers. Additionally, recently introduced systems that store packet payloads in temporary off-CPU locations (e.g., programmable switches, NICs, and RDMA servers) further increase the load on NF servers, making packet processing even more challenging. This paper demonstrates existing bottlenecks and challenges of state-of-the-art stateful packet processing frameworks and proposes a system, called FAJITA, to tackle these challenges & accelerate stateful packet processing on commodity hardware. FAJITA proposes an optimized processing pipeline for stateful network functions to minimize memory accesses and overcome the overheads of accessing shared data structures while ensuring efficient batch processing at every stage of the pipeline. Furthermore, FAJITA provides a performant architecture to deploy high-performance network functions service chains containing stateful elements with different state granularities. FAJITA improves the throughput and latency of high-speed stateful network functions by ~2.43x compared to the most performant state-of-the-art solutions, enabling commodity hardware to process up to ~178 Million 64-B packets per second (pps) using 16 cores.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2024. Vol. 2, no CoNEXT3, p. 1-22
Keywords [en]
packet processing frameworks, stateful network functions
National Category
Communication Systems Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-357087DOI: 10.1145/3676861OAI: oai:DiVA.org:kth-357087DiVA, id: diva2:1918345
Projects
ULTRA
Funder
EU, Horizon 2020, 770889Swedish Research Council, 2021-04212Vinnova, 2023-03003
Note

QC 20241206

Available from: 2024-12-04 Created: 2024-12-04 Last updated: 2024-12-06Bibliographically approved

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fajita(1357 kB)90 downloads
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Ghasemirahni, HamidFarshin, AlirezaScazzariello, MarianoMaguire Jr., Gerald Q.Kostic, DejanChiesa, Marco

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Ghasemirahni, HamidFarshin, AlirezaScazzariello, MarianoMaguire Jr., Gerald Q.Kostic, DejanChiesa, Marco
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