Choosing the right setting for big data frameworks is an important yet difficult task. These
frameworks come with a complex set of parameters that need to be tuned to achieve the best …

Choosing the right setting for big data frameworks is an important yet difficult task. These
frameworks come with a complex set of parameters that need to be tuned to achieve the best …

Spark tuning with its dozens of parameters for performance improvement is both a challenge
and time consuming effort. Current techniques rely on trial-and-error or best guess utilizing …

Configuration tuning is vital to optimize the performance of big data analysis platforms like
Spark. Existing methods (eg auto-tuning relational databases) are not effective for tuning …

Tuning configurations of Spark jobs is not a trivial task. State-of-the-art auto-tuning systems
are based on iteratively running workloads with different configurations. During the …

We propose a novel approach to address one aspect of the non-stationarity problem in multi-
agent reinforcement learning (RL), where the other agents may alter their policies due to …

The distributed data analytic system--Spark is a common choice for processing massive
volumes of heterogeneous data, while it is challenging to tune its parameters to achieve …

In the field of reinforcement learning for continuous control, deep off-policy actor-critic
algorithms have become a popular approach due to their ability to address function …

Java is the backbone of widely used big data frameworks, such as Apache Spark, due to its
productivity, portability from JVM-based execution, and support for a rich set of libraries …

In recent years, deep off-policy actor-critic algorithms have become a dominant approach to
reinforcement learning for continuous control. One of the primary drivers of this improved …