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

Recent workload trends indicate rapid growth in the deployment of machine learning,
genomics and scientific workloads on cloud computing infrastructure. However, efficiently …

MapReduce and Spark are two very popular open source cluster computing frameworks for
large scale data analytics. These frameworks hide the complexity of task parallelism and …

Workflow technology is rapidly evolving and, rather than being limited to modeling the
control flow in business processes, is becoming a key mechanism to perform advanced data …

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 …

Database and big data analytics systems such as Hadoop and Spark have a large number
of configuration parameters that control memory distribution, I/O optimization, parallelism …

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 …

Spark has become one of the main options for large-scale analytics running on top of shared-
nothing clusters. This work aims to make a deep dive into the parallelism configuration and …

Declarative large-scale machine learning (ML) aims at flexible specification of ML algorithms
and automatic generation of hybrid runtime plans ranging from single node, in-memory …

Big data analytics frameworks (BDAFs) have been widely used for data processing
applications. These frameworks provide a large number of configuration parameters to …