Modern resource management frameworks for largescale analytics leave unresolved the
problematic tension between high cluster utilization and job's performance predictability …

Data analytics are an important class of data-intensive workloads on public cloud services.
However, selecting the right compute and storage configuration for these applications is …

In-memory cluster computing platforms have gained momentum in the last years, due to their
ability to analyse big amounts of data in parallel. These platforms are complex and difficult-to …

This paper presents a new MapReduce cloud service model, Cura, for provisioning cost-
effective MapReduce services in a cloud. In contrast to existing MapReduce cloud services …

The use of deep learning models for forecasting the resource consumption patterns of SQL
queries have recently been a popular area of study. While these models have demonstrated …

Cloud computing has become a popular platform for processing big data analysis jobs with
its advantages of high-availability, elasticity and cost-efficiency. Many big data analysis …

Any computational process from simple data analytics tasks to training a machine learning
model can be described by a workflow. Many workflow management systems (WMS) exist …

Abstract Machine learning algorithms are widely used today for analytical tasks such as data
cleaning, data categorization, or data filtering. At the same time, the rise of social media …

Size-based scheduling with aging has been recognized as an effective approach to
guarantee fairness and near-optimal system response times. We present HFSP, a scheduler …

Smart databases are adopting artificial intelligence (AI) technologies to achieve {\em
instance optimality}, and in the future, databases will come with prepackaged AI models …