Datacenter systems and I/O devices now run at single-digit microsecond latencies, requiring
ns-scale operating systems. Traditional kernel-based operating systems impose an …

Big data applications often incur large costs in I/O, data transfer and copying overhead,
especially when operating in cloud environments. Since most such computations are …

In this paper, we propose ScaleStore, a novel distributed storage engine that exploits DRAM
caching, NVMe storage, and RDMA networking to achieve high performance, cost-efficiency …

Cloud deployments disaggregate storage from compute, providing more flexibility to both the
storage and compute layers. In this paper, we explore disaggregation by taking it one step …

The Internet of Things (IoT) presents a novel computing architecture for data management: a
distributed, highly dynamic, and heterogeneous environment of massive scale. Applications …

While hardware and software improvements greatly accelerated modern database systems'
internal operations, the decades-old stream-based Socket API for external communication is …

The commoditization of high-performance networking has sparked research interest in the
RDMA capability of this hardware. One-sided RDMA primitives, in particular, have generated …

In this paper, we propose the Data Flow Interface (DFI) as a way to make it easier for data
processing systems to exploit high-speed networks without the need to deal with the …

Researchers have long predicted the demise of the operating system [21, 26, 41]. As
datacenter servers increasingly incorporate I/O devices that let applications bypass the OS …

Concurrency control is a cornerstone of distributed database engines and storage systems.
In pursuit of scalability, a common assumption is that Two-Phase Locking (2PL) and Two …