Memory disaggregation architecture physically separates CPU and memory into
independent components, which are connected via high-speed RDMA networks, greatly …

Remote Direct Memory Access (RDMA) based network devices are increasingly being
deployed in modern data centers. RDMA brings significant performance improvements over …

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 …

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 …

In the presence of exponential growth of the data produced every day in volume, velocity,
and variety, online analytical processing (OLAP) is becoming increasingly challenging …

In this paper we present BatchDB, an in-memory database engine designed for hybrid OLTP
and OLAP workloads. BatchDB achieves good performance, provides a high level of data …

Sorting is a key computational kernel in many big data applications. Most sorting
implementations focus on a specific input size, record width, and hardware configuration …

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

Implementing parallel operators in multi-core machines often involves a data partitioning
step that divides the data into cache-size blocks and arranges them so to allow concurrent …

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 …