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 …

Modern web services use in-memory caching extensively to increase throughput and reduce
latency. There have been several workload analyses of production systems that have fueled …

Commodity network devices support adding in-band telemetry measurements into data
packets, enabling a wide range of applications, including network troubleshooting …

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

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 …

The emergence of programmable switches offers a new opportunity to revisit ISP-scale
defenses for volumetric DDoS attacks. In theory, these can offer better cost vs. performance …

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

Emerging Multicore SoC SmartNICs, enclosing rich computing resources (eg, a multicore
processor, onboard DRAM, accelerators, programmable DMA engines), hold the potential to …

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 …