The allreduce operation is an essential building block for many distributed applications, ranging from the training of deep learning models to scientific computing. In an allreduce operation, data from multiple hosts is aggregated together and then broadcasted to each host participating in the operation. Allreduce performance can be improved by a factor of two by aggregating the data directly in the network. Switches aggregate data coming from multiple ports before forwarding the partially aggregated result to the next hop. In all existing solutions, each switch needs to know the ports from which it will receive the data to aggregate. However, this forces packets to traverse a predefined set of switches, making these solutions prone to congestion. For this reason, we design Canary, the first congestion-aware in-network allreduce algorithm. Canary uses load balancing algorithms to forward packets on the least congested paths. Because switches do not know from which ports they will receive the data to aggregate, they use timeouts to aggregate the data in a best-effort way. We develop a P4 Canary prototype and evaluate it on a Tofino switch. We then validate Canary through simulations on large networks, showing performance improvements up to 40% compared to the state-of-the-art.

Avoiding packet loss is crucial for ISPs. Unfortunately, malfunctioning hardware at ISPs can cause long-lasting packet drops, also known as gray failures, which are undetectable by existing monitoring tools.

One design principle of modern network architecture seems to be set in stone: a software-based control plane drives a hardware- or software-based data plane. We argue that it is time to revisit this principle after the advent of programmable switch ASICs which can run complex logic at line rate.

We investigate the applicability of IEEE 802.11ac for entertainment low-latency show control systems (to orchestrate free-riding vehicles in a theme-park environment) by experimentally characterizing the indoor throughput and jitter performance of 802.11ac using statistical analysis. 802.11ac is the successor of 802.11n that provides higher throughput by incorporating wider channels, more spatial streams and denser modulation. We show that multiple linear regression provides valuable insight in the influence of 802.11ac's independent features and their combinations on performance, for various links and interference scenarios. Finally, we show that 802.11ac could be used for not only delivering high throughput for multimedia streaming but also supports applications requiring minimal jitter variance in the setup investigated.