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Switcharoo accepted at CoNEXT 2023

Today’s network functions require keeping state at the granularity of each individual flow. Storing such state on network devices is highly challenging due to the complexity of the involved data structures. As such, the state is often stored on inefficient CPU-based servers as opposed to high-speed ASIC network switches. In our newly accepted CoNEXT paper, we demonstrate the possibility to perform tens of millions of low-latency flow state insertions on ASIC switches, showing our implementation achieves 75x memory requirements compared to existing probabilistic data structures in a common datacenter scenario. A PDF of the paper will soon be available. This was joint work between Mariano Scazzariello, Tommaso Caiazzi (from Roma Tre University), and Marco Chiesa.