Research
Wallenberg Scholar Project: ChatGPT for every child
The goal of my Wallenberg Scholar Project is to build a substantially more energy efficient platform for AI inferencing. The project will run from 2025 until 2029, and is titled "Sustainable and Adaptive Inferencing for Democratizing AI (project blog link)". An interview about the project is also available.
ML for systems, systems for ML
My recent focus is on using machine learning for systems, and building systems for machine learning. One key example is our Swedish Research Council (VR) project on Scalable Federated Learning (SFL). This project aims to develop a highly scalable, flexible, extensible, distributed federated machine learning approach that can directly benefit public health and wellness. In our recent CFD paper we demonstrate our ability to perform dropout on image recognition models using coding theory (e.g., using Gold codes), resulting in more than 2X network bandwidth savings when used for Federated Learning. Our FMM-head work shows the benefit of taking the underlying model of the heart (via Frequency Modulated Möbius waves) into account when performing ECG anomaly detection. Our model achieves up to 0.31 increase in area under the ROC curve (AUROC) when compared to the state of the art models. Moreover, the processing time of our model is four orders of magnitude lower than solving an optimization problem to obtain the same parameters, thus making it suitable for real-time ECG parameter extraction and anomaly detection. Our paper at STACOM demonstrates a potential for removing a large number of leads (more than half!) during ECG Imaging.
In the domain of optimization, our DeepGANTT IPSN '23 paper demonstrates our ability to apply transformers to Graph Neural Networks (GNNs) for scaling up an IoT scheduling problem 10X beyond what a constraint optimization solver can solve in a reasonable time.
ERC Consolidator Project: 10x more efficient Internet Services
In my 2018-2024 ERC Consolidator Project called ULTRA, we wanted to dramatically change the way Internet Services are constructed.
In our SoCC 2018 paper, we present Kurma, our fast and accurate load balancer for geo-distributed storage systems. We have recently collaborated with Ericsson on our CacheDirector work published at EuroSys 2019.
The work on Metron (NFV service chains at the true speed of the underlying hardware) has led to, and has inspired this project. Our most recent CoNEXT 2019 paper called RSS++shows how load-balancing with Receive Side Scaling (RSS) can be improved for large increases in efficiency. In our NSDI 2020 paper we have introduced Cheetah, a new load balancer for solving the difficult challenge of remembering which server is serving which connection, without the tradeoff between uniform load balancing and efficiency. In our ASPLOS 2021 paper, we have shown that vertically integrating a network protocol stack enables a single CPU core to forward packets faster than 100 Gbps. More details are available at the PacketMill project page. The Metron journal article is now also available, and it shows how high performance NFV service chaining can be done even in the presence of blackboxes. Our NSDI 2022 paper Packet Order Matters shows a surprising result: deliberately delaying packets can improve the performance of backend servers by up to a factor of 2 (e.g., those used for Network Function Virtualization)! We show three different scenarios in which our Reframer can be deployed. Our final work in this project is FAJITA, which shows that a commodity server running a chain of stateful network functions can process more than 170 M packets per second (equivalent of 1.4 Tbps if payloads are stored in a disaggregated fashion as in our earlier Ribosome work [NSDI ’23]!).
In our OSDI 2020 paper on Assise, we provide a blueprint of how a next generation distributed file system should be built on top of NVRAM that is becoming widely available, and achieve large performance gains. Our LineFS paper (Best Paper award at SOSP '21) builds upon this work by offloading CPU-intensive tasks to a SmartNIC (BlueField-1 in our case) for about 80% performance improvement across the board. Our RedN NSDI 2022 paper shows another suprising result, namely that Remote Direct Memory Access (RDMA), as implemented in widely deployed RDMA Network Interface Cards, is Turing Complete! We leverage this finding to reduce the tail latency of services running on busy servers by 35x!
SSF Framework project: Time-Critical Clouds
A major focus area for my
research group was on Time-Critical Clouds,
a 2016-2022 project supported by SSF
(the Swedish Foundation for Strategic Research) with 27 M SEK (~2.7 M EUR). This was a
joint effort with the
Connected Intelligence group of RISE AB. Our
first major contribution is a Eurosys 2017 paper (link to video and paper available
here). In this work we reduce the tail latency in
key-value stores by up to 1.9x by scheduling multiget requests
more efficiently. Most recently we have shown how to run NFV service chains at the true speed of the underlying hardware in our NSDI '18 paper.
In our EuroSys 2019 paper we have unlocked a performance-enhancing feature that existed in Intel processors for almost a decade. In our USENIX ATC 2020 paper, we are reexamining Direct Cache Access (DCA) to optimize I/O intensive applications for multi-hundred-gigabit networks.
In our PAM 2021 paper, we show that the forwarding throughput of the widely-deployed programmable Network Interface Cards (NICs) sharply degrades when i) the forwarding plane is updated and ii) packets match multiple forwarding tables in the NIC.
ERC Starting Grant: simplifying the way networked systems are developed and deployed
We have also concluded the work on the PROPHET
ERC project (2010-2016), in which we aimed to dramatically change the
way networked
systems
are developed and deployed. For example, we improved
the performance of geo-replicated storage systems using GeoPerf
[SOCC '15]. We have successfully
applied software verification techniques to increase the
reliability
of Software-Defined
Networks (SDN). Some of our key contributions
to testing of OpenFlow networks are NICE
[NSDI'12] and SOFT
[CoNEXT'12]. We have
identified serious issues in the interplay
between the control and data planes in OpenFlow switches [PAM '15],
and
proposed an approach
for verifying rule installation [CoNEXT '14] as well as fine-grained
dynamic monitoring of switch dataplanes [CoNEXT '15]. Extended versions of these contributions are now available as IEEE/ACM TON and Elsevier Computer Networks journal publications. My work in Wallenberg AI, Autonomous Systems and Software Program (WASP) as a WASP faculty (advising an industrial PhD student at Ericsson, Amir Roozbeh) is complementary to these efforts.
We have wrapped up our work in the BEhavioral-BAsed
Forwarding (BEBA) Horizon2020 project (2014-2017) that aimed
to reshape
Software-Defined Networks. Our contributions are described in George
Katsikas' licentiate thesis, and involve deep
understanding and performance optimization of
Network Functions Virtualization (NFV) service chains.
Moreover, our recent work on Synthesized
Network Functions, demonstrates
high throughput with low predictable latency on a single
commodity
server thanks to its highly synthesized code and request dispatching. The overall project was recently highlighted by the EU comission.
