Alexander Dietmüller
PhD Student
I am a first year PhD student interested in the intersection of control theory and communication networks.
In my current research, I study the opportunities provided by recent advances in programmable networks, in particular using P4 for feedback-based learning and control in large-scale networks, such as the Internet. Furthermore, I am investigating how such smart networks can improve transport protocols, e.g. when it comes to congestion control and fairness.
Aside from that, I am working on programmable packet scheduling together with Albert.
I received my Bachelor and Master degrees in Electrical Engineering and Information Technology from ETH Zürich.
Recent Publications
SP-PIFO: Approximating Push-In First-Out Behaviors using Strict-Priority Queues
USENIX NSDI 2020. Santa Clara, California, USA (February 2020).
Push-In First-Out (PIFO) queues are hardware primitives which enable programmable packet scheduling by allowing to perfectly reorder packets at line rate. While promising, implementing PIFO queues in hardware and at scale is not easy: only hardware designs (not implementations) exist and they can only support about 1000 flows.
In this paper, we introduce SP-PIFO, a programmable packet scheduler which closely approximates the behavior of PIFO queues using strict-priority queues—at line rate, at scale, and on existing devices. The key insight behind SP-PIFO is to dynamically adapt the mapping between packet ranks and available queues to minimize the scheduling errors. We present a mathematical formulation of the problem and derive an adaptation technique which closely approximates the optimal queue mapping without any traffic knowledge.
We fully implement SP-PIFO in P4 and evaluate it on real workloads. We show that SP-PIFO: (i) closely matches ideal PIFO performance, with as little as 8 priority queues; (ii) arbitrarily scales to large amount of flows and ranks; and (iii) quickly adapts to traffic variations. We also show that SP-PIFO runs at line rate on existing programmable data planes.
Lectures
Communication Networks
Spring 2020
Advanced Topics in Communication Networks
Autumn 2019
Communication Networks
Spring 2019
Advanced Topics in Communication Networks
Autumn 2018
Supervised Theses
Leveraging Network Programmability for Machine Learning in the Data Plane (M)
- ABB Research Award 2019
