Alexander Dietmüller
PhD Student
I am a third year PhD student interested in the intersection of control theory, machine learning, 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
P2GO: P4 Profile-Guided Optimizations
ACM HotNets 2020. Chicago, Illinois, USA (November 2020).
Programmable devices allow the operator to specify the data-plane behavior of a network device in a high-level language such as P4. The compiler then maps the P4 program to the hardware after applying a set of optimizations to minimize resource utilization. Yet, the lack of context restricts the compiler to conservatively account for all possible inputs -- including unrealistic or infrequent ones -- leading to sub-optimal use of the resources or even compilation failures. To address this inefficiency, we propose that the compiler leverages insights from actual traffic traces, effectively unlocking a broader spectrum of possible optimizations.
We present a system working alongside the compiler that uses traffic-awareness to reduce the allocated resources of a P4 program by: (i) removing dependencies that do not manifest; (ii) adjusting table and register sizes to reduce the pipeline length; and (iii) offloading parts of the program that are rarely used to the controller. Our prototype implementation on the Tofino switch automatically profiles the P4 program, detects opportunities and performs optimizations to improve the pipeline efficiency.
Our work showcases the potential benefit of applying profiling techniques used to compile general-purpose languages to compiling P4 programs.
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
Advanced Topics in Communication Networks
Autumn 2021
Seminar in Communication Networks
Spring 2021
Advanced Topics in Communication Networks
Autumn 2020
Communication Networks
Spring 2020
Advanced Topics in Communication Networks
Autumn 2019
Communication Networks
Spring 2019
Advanced Topics in Communication Networks
Autumn 2018
Supervised Theses
Profile-Guided Network Optimizations (M)
Mailing List Analysis (S)
In Search of Network Shifts (B)
Traffic-Aware Compilation
Improving Performance with Network-aware Scheduling Algorithms (S)
Leveraging Network Programmability for Machine Learning in the Data Plane (M)
- ABB Research Award 2019
