Workflows are an application model that enables the automated execution of multiple
interdependent and interconnected tasks. They are widely used by the scientific community …

In this survey, we discuss the challenges of executing scientific workflows as well as existing
Machine Learning (ML) techniques to alleviate those challenges. We provide the context …

Machine learning (ML) is being applied in a number of everyday contexts from image
recognition, to natural language processing, to autonomous vehicles, to product …

Technological advances and the emergence of the Internet of Things have lead to the
collection of vast amounts of scientific data from increasingly powerful scientific instruments …

Modern scientific workflows are data-driven and are often executed on distributed,
heterogeneous, high-performance computing infrastructures. Anomalies and failures in the …

Identifying and addressing anomalies in complex, distributed systems can be challenging for
reliable execution of scientific workflows. We model these workflows as directed acyclic …

Virtualization as a key IT technology has developed to a predominant model in data centers
in recent years. The flexibility regarding scaling-out and migration of virtual machines for …

Research integrity is crucial to ensuring the trustworthiness of scientific discoveries. This
work is aimed at detecting misbehaviors targeting scientific workflows, which are computing …

Large-scale scientific workflows rely heavily on high-performance file transfers. These
transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or …

Cloud computing has become increasingly popular, which has led many individuals and
organizations towards cloud storage systems. This move is motivated by benefits such as …