Our TNSM 2022 journal article is the final publication funded by this project. The full title is “Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming over HTTP (DASH)”. This is joint work done in collaboration with FBK between Rasoul Behravesh (FBK), Akhila Rao (RISE), Daniel F Perez-Ramirez (RISE), Davit Harutyunyan (Corporate … Continue reading “Final publication in our project: “Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming over HTTP (DASH)””
Congratulations on Alexandros for defending his licentiate thesis titled “Understanding the Capabilities of Route Collectors to Observe Stealthy Hijacks”! The supervisors are Marco Chiesa and Dejan Kostic. Thanks to the advanced reviewer Prof. Gerald Q. Maguire Jr., the special reviewer Prof. Alberto Dainotti, and the examiner Prof. Roberto Guanciale for their thorough work. You can … Continue reading “Alexandros’ licentiate defense”
In our PAM 2021 paper, we study the performance of (smart) Network Interface Cards (NICs) for widely deployed packet classification operations, focusing on four 100-200 GbE NICs from one of the largest NIC vendors worldwide. We show that the forwarding throughput of the tested NICs sharply degrades when i) the forwarding plane is updated and ii) packets match … Continue reading “Our PAM 2021 paper: “What you need to know about (Smart) Network Interface Cards””
ASPLOS ’21 will feature Alireza’s presentation of our paper titled “PacketMill: Toward Per-Core 100-Gbps Networking”. This is joint work with Alireza Farshin, Tom Barbette, Amir Roozbeh, Gerald Q. Maguire Jr., and Dejan Kostić. The full abstract (with the video and more resources below): We present PacketMill , a system for optimizing software packet processing, which (i) … Continue reading “Our ASPLOS ’21 Paper: “PacketMill: Toward Per-Core 100-Gbps Networking””
Effective use of networked resources requires the ability to solve complex large-scale optimization problems fast while accounting for many input variables and performance requirements, such as end-to-end latency. Advancing beyond heuristic approaches, we begin with surveying the current state of applied machine learning to solve complex combinatorial optimization problems over networks. In our IEEE Access … Continue reading “Timely survey on applying machine learning to solve complex combinatorial optimization problems over networks”
