Exp-PIFO: Scalable and Efficient Programmable Packet Scheduling

Programmable packet schedulers provide great flexibility in the ordering of packet transmission. They allow network operators to optimize crucial performance metrics, such as Flow Completion Time (FCT), using strategies that adapt to changes in the characteristics of incoming traffic. A challenge in programming these devices is that they are severely limited in registers, memory, and control flow operations. The popular scheduling strategy Push-In First-Out (PIFO), for example, cannot be straightforwardly implemented because it relies on sorting packets, which is difficult to implement at line speed on current hardware. Fixed-priority approximations of PIFO, like SP-PIFO and AIFO, do have hardware implementations but generally still do not scale well in, for example, the number of memory cells being used. This paper introduces a new PIFO approximation strategy, Exp-PIFO, which prioritizes packets based on adaptive exponential prioritization criteria, called exponential bins. Exp-PIFO approximates the behavior of PIFO using only two memory cells to keep track of its state and uses a lookup table to avoid a complex control flow. We initially expected our improvement in memory and computation to come at a cost w.r.t. FCT performance compared to PIFO and its existing approximations. However, our empirical evaluation shows that Exp-PIFO sometimes even outperforms strict PIFO. We provide an explanation for this behavior and demonstrate the practical feasibility of Exp-PIFO through a proof-of-concept implementation on an Intel Tofino switch, which uses significantly less memory than comparable implementations of SP-PIFO and AIFO.

Authors

Institutions

Publication Details

Journal
Proceedings of the ACM on Networking
Published
2025-09-03
DOI
https://doi.org/10.1145/3749217
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exp-PIFO: Scalable and Efficient Programmable Packet Scheduling

Gábor Rétvári, Pjl Pieter Cuijpers, Habib Mostafaei, Stefan Schmid et al.
Proceedings of the ACM on Networking
Software-Defined Networks and 5G
article

Exp-PIFO: Scalable and Efficient Programmable Packet Scheduling

Gábor Rétvári, Pjl Pieter Cuijpers, Habib Mostafaei, Stefan Schmid, Sem Borst, Mohammad Ezzati
article en

Abstract

Programmable packet schedulers provide great flexibility in the ordering of packet transmission. They allow network operators to optimize crucial performance metrics, such as Flow Completion Time (FCT), using strategies that adapt to changes in the characteristics of incoming traffic. A challenge in programming these devices is that they are severely limited in registers, memory, and control flow operations. The popular scheduling strategy Push-In First-Out (PIFO), for example, cannot be straightforwardly implemented because it relies on sorting packets, which is difficult to implement at line speed on current hardware. Fixed-priority approximations of PIFO, like SP-PIFO and AIFO, do have hardware implementations but generally still do not scale well in, for example, the number of memory cells being used. This paper introduces a new PIFO approximation strategy, Exp-PIFO, which prioritizes packets based on adaptive exponential prioritization criteria, called exponential bins. Exp-PIFO approximates the behavior of PIFO using only two memory cells to keep track of its state and uses a lookup table to avoid a complex control flow. We initially expected our improvement in memory and computation to come at a cost w.r.t. FCT performance compared to PIFO and its existing approximations. However, our empirical evaluation shows that Exp-PIFO sometimes even outperforms strict PIFO. We provide an explanation for this behavior and demonstrate the practical feasibility of Exp-PIFO through a proof-of-concept implementation on an Intel Tofino switch, which uses significantly less memory than comparable implementations of SP-PIFO and AIFO.

Proceedings of the ACM on NetworkingVol. 3(CoNEXT3)
Budapest University of Technology and Economics (HU), Fraunhofer Institute for Secure Information Technology (DE), Technische Universität Berlin (DE), Eindhoven University of Technology (NL)
Deutsche Forschungsgemeinschaft, Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Openalex Percentile: Top 73%
Software-Defined Networks and 5G
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.