Everything Matters in Programmable Packet Scheduling
August 01, 2023 Β· Declared Dead Β· π Symposium on Networked Systems Design and Implementation
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Albert Gran Alcoz, BalΓ‘zs Vass, GΓ‘bor RΓ©tvΓ‘ri, Laurent Vanbever
arXiv ID
2308.00797
Category
cs.NI: Networking & Internet
Citations
8
Venue
Symposium on Networked Systems Design and Implementation
Last Checked
3 months ago
Abstract
Programmable packet scheduling allows the deployment of scheduling algorithms into existing switches without need for hardware redesign. Scheduling algorithms are programmed by tagging packets with ranks, indicating their desired priority. Programmable schedulers then execute these algorithms by serving packets in the order described in their ranks. The ideal programmable scheduler is a Push-In First-Out (PIFO) queue, which achieves perfect packet sorting by pushing packets into arbitrary positions in the queue, while only draining packets from the head. Unfortunately, implementing PIFO queues in hardware is challenging due to the need to arbitrarily sort packets at line rate based on their ranks. In the last years, various techniques have been proposed, approximating PIFO behaviors using the available resources of existing data planes. While promising, approaches to date only approximate one of the characteristic behaviors of PIFO queues (i.e., its scheduling behavior, or its admission control). We propose PACKS, the first programmable scheduler that fully approximates PIFO queues on all their behaviors. PACKS does so by smartly using a set of strict-priority queues. It uses packet-rank information and queue-occupancy levels at enqueue to decide: whether to admit packets to the scheduler, and how to map admitted packets to the different queues. We fully implement PACKS in P4 and evaluate it on real workloads. We show that PACKS: better-approximates PIFO than state-of-the-art approaches and scales. We also show that PACKS runs at line rate on existing hardware (Intel Tofino).
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Networking & Internet
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
R.I.P.
π»
Ghosted
A Survey of Indoor Localization Systems and Technologies
R.I.P.
π»
Ghosted
Survey of Important Issues in UAV Communication Networks
R.I.P.
π»
Ghosted
Network Function Virtualization: State-of-the-art and Research Challenges
R.I.P.
π»
Ghosted
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted