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

Picking the right cloud configuration for recurring big data analytics jobs running in clouds is
hard, because there can be tens of possible VM instance types and even more cluster sizes …

While modern parallel computing systems offer high performance, utilizing these powerful
computing resources to the highest possible extent demands advanced knowledge of …

When tuning software configuration for better performance (eg, latency or throughput), an
important issue that many optimizers face is the presence of local optimum traps …

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 …

Configurable software systems can be tuned for better performance. Leveraging on some
Pareto optimizers, recent work has shifted from tuning for a single, time-related performance …

Automatically tuning software configuration for optimizing a single performance attribute (eg,
minimizing latency) is not trivial, due to the nature of the configuration systems (eg, complex …

There is a lot of interest today in building autonomous (or, self-driving) data processing
systems. An emerging school of thought is to leverage AI-driven" black box" algorithms for …

Datacenter-scale clusters are evolving toward heterogeneous hardware architectures due to
continuous server replacement. Meanwhile, datacenters are commonly shared by many …

Memory is a crucial resource for big data processing frameworks such as Spark and M3R,
where the memory is used both for computation and for caching intermediate storage data …