A goal of cloud service management is to design self-adaptable auto-scaler to react to
workload fluctuations and changing the resources assigned. The key problem is how and …

In this paper, we present an overview of the Myria stack for big data management and
analytics that we developed in the database group at the University of Washington and that …

We implement and evaluate deep learning for cardinality estimation by studying the
accuracy, space and time trade-offs across several architectures. We find that simple deep …

OLTP database systems are a critical part of the operation of many enterprises. Such
systems are often configured statically with sufficient capacity for peak load. For many OLTP …

Distributed data management systems often operate on" elastic''clusters that can scale up or
down on demand. These systems face numerous challenges, including data fragmentation …

Modern large-scale computing deployments consist of complex applications running over
machine clusters. An important issue in these is the offering of elasticity, ie, the dynamic …

In this extended abstract, we propose a new technique for query scheduling with the explicit
goal of reducing disk reads and thus implicitly increasing query performance. We introduce …

The onset of cloud computing has brought about computing power that can be provisioned
and released on-demand. This capability has drastically increased the complexity of …

Several programming languages use garbage collectors (GCs) to automatically manage
memory for the programmer. Such collectors must decide when to look for unreachable …

Modern data processing applications execute increasingly sophisticated analysis that
requires operations beyond traditional relational algebra. As a result, operators in query …