Modern cloud networks are a fundamental pillar of data-intensive applications. They provide
high-speed transaction (packet) rates and low overhead, enabling, for instance, truly …

Serverless computing is renowned for its computation elasticity, yet its full potential is often
constrained by the requirement for functions to operate within local and dedicated …

Existing general purpose frameworks for gigantic model training, ie, dense models with
billions of parameters, cannot scale efficiently on cloud environment with various networking …

Businesses are increasingly demanding real-time analytics on up-to-date data. However,
current solutions fail to efficiently combine transactional and analytical processing in a single …

Many distributed cloud OLTP databases have settled on a sharedstorage design coupled
with a single-writer. This design choice is remarkable since conventional wisdom promotes …

This paper explores the intersection of operating systems and database systems, focusing
on the potential of specialized kernels for cloud-native database systems. Although the idea …

Abstract In recent years, Deep Learning models have shown great potential in many areas,
including Computer Vision, Speech Recognition, Information Retrieval, etc. This results in a …

Memory is one of the key components that affects reliability and performance of datacenter
servers. Memory in today's servers is organized and shared in several ways to provide the …

Online Transaction Processing (OLTP) underpins real-time data processing in many mission-
critical applications, from banking to e-commerce. These applications typically issue short …

Over the past two decades, distributed stream processing engines (SPEs) have become a
prominent component in the big data management tool-chain to support real-time, stateful …