Very recently, the unexpected combination of data structures and machine learning has led
to the development of a new area of research, called learned data structures. Their …

The past decades witnessed the fast and wide deployment of Internet. The Internet has bred
the ubiquitous computing environment that is spanning the cloud, edge, mobile devices, and …

Host-based intrusion detection systems (HIDS) can automatically identify adversarial
applications by learning models from system events that represent normal system behaviors …

Today's computing systems pay a heavy" OS tax", as kernel execution accounts for a
significant amount of resource footprint. This is not least because today's kernels abound …

A learned system uses machine learning (ML) internally to improve performance. We can
expect such systems to be vulnerable to some adversarial-ML attacks. Often, the learned …

Sample-efficient machine learning (SEML) has been widely applied to find optimal latency
and power tradeoffs for configurable computer systems. Instead of randomly sampling from …

Designing and maintaining distributed systems remains highly challenging: there is a high-
dimensional design space of potential ways to distribute a system's sub-components over a …

The complexity of modern operating systems (OSes), rapid diversification of hardware, and
steady evolution of machine learning (ML) motivate us to explore the potential of ML to …

Modern computer systems need to execute under strict safety constraints (eg, a power limit),
but doing so often conflicts with their ability to deliver high performance (ie minimal latency) …

Persistent Memory (PM) and Remote Direct Memory Access (RDMA) technologies have
significantly improved the storage and network performance in data centers and spawned a …