Big data processing systems (eg, Hadoop, Spark, Storm) contain a vast number of
configuration parameters controlling parallelism, I/O behavior, memory settings, and …

In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …

Serving ML prediction pipelines spanning multiple models and hardware accelerators is a
key challenge in production machine learning. Optimally configuring these pipelines to meet …

Finding good configurations of a software system is often challenging since the number of
configuration options can be large. Software engineers often make poor choices about …

Modern software systems provide many configuration options which significantly influence
their non-functional properties. To understand and predict the effect of configuration options …

Finding the optimally performing configuration of a software system for a given setting is
often challenging. Recent approaches address this challenge by learning performance …

Many software systems today are configurable, offering customization of functionality by
feature selection. Understanding how performance varies in terms of feature selection is key …

With the advent of big data applications, which tend to have longer execution time, choosing
the right cloud VM has significant performance and economic implications. For example, in …

Performance-influence models can help stakeholders understand how and where
configuration options and their interactions influence the performance of a system. With this …

Modern software systems are built to be used in dynamic environments using configuration
capabilities to adapt to changes and external uncertainties. In a self-adaptation context, we …