In comparison with cloud computing, edge computing offers processing at locations closer to
end devices and reduces the user experienced latency. The new recent paradigm of in …

Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …

Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …

Distributed deep neural network training (DT) systems are widely deployed in clusters where
the network is shared across multiple tenants, ie, multiple DT jobs. Each DT job computes …

It is commonly believed that datacenter networking software must sacrifice generality to
attain high performance. The popularity of specialized distributed systems designed …

Distributed Denial-of-Service (DDoS) attacks have become a critical threat to the Internet.
Due to the increasing number of vulnerable Internet of Things (IoT) devices, attackers can …

Traditionally, the data plane has been designed with fixed functions to forward packets using
a small set of protocols. This closed-design paradigm has limited the capability of the …

Machine learning is currently driving a technological and societal revolution. While
programmable switches have been proven to be useful for in-network computing, machine …

Programmable packet scheduling enables scheduling algorithms to be programmed into the
data plane without changing the hardware. Existing proposals either have no hardware …

Programmable switches have been touted as an attractive alternative for deploying network
functions (NFs) such as network address translators (NATs), load balancers, and firewalls …