Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape
of cloud computing has been undergoing remarkable changes with the emergence of many …

Distributed dataflow systems enable data-parallel processing of large datasets on clusters.
Public cloud providers offer a large variety and quantity of resources that can be used for …

Distributed dataflow systems enable the use of clusters for scalable data analytics. However,
selecting appropriate cluster resources for a processing job is often not straightforward …

Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data
analytics. While runtime prediction models can be used to initially select appropriate cluster …

Analyzing large datasets with distributed dataflow systems requires the use of clusters.
Public cloud providers offer a large variety and quantity of resources that can be used for …

In order to overcome the famous von Neumann bottleneck, FPGAs employ a dataflow model
that processes data through a pipeline of operator modules, akin to an assembly line for …

In highly distributed environments such as cloud, edge and fog computing, the application of
machine learning for automating and optimizing processes is on the rise. Machine learning …

For decades, database research has focused on optimizing performance under fixed
resources. As more and more database applications move to the public cloud, we argue that …

In the rapidly expanding cloud continuum, efficient resource management presents a critical
challenge, particularly in multi-tenant environments where applications share resources …

Predicting the runtime of distributed iterative jobs can help reduce the deployment cost of
clusters and optimize their resource allocation and scheduling strategies, but the runtime …