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 …

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 …

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 …

Memory disaggregation promises transparent elasticity, high resource utilization and
hardware heterogeneity in data centers by physically separating memory and compute into …

Performance of in-memory key-value store (KVS) continues to be of great importance as
modern KVS goes beyond the traditional object-caching workload and becomes a key …

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

Load balancing is critical for distributed storage to meet strict service-level objectives
(SLOs). It has been shown that a fast cache can guarantee load balancing for a clustered …

Developing server applications that offload computation and data to a NIC accelerator is
laborious because one has to explore the design space of decisions about data placement …