Tuneful
Last updated on
Nov 28, 2020

Tuneful is an extension for Spark which optimizes workload configurations starting from a zero-knowledge setting. The more workloads a cluster executes, the better it becomes at executing them. In order to achieve this, we leverage Multi Task Gaussian Process, Similarity Analysis and Significance Analysis.

Thomas Pasquier
Lecturer (Assistant Professor) in Computer Science
My research interests include distributed robotics, mobile computing and programmable matter.
Publications
One of the key challenges for data analytics deployment is configuration tuning. The existing approaches for configuration tuning are …
A Fekry,
L Carata,
T Pasquier,
A Rice
This experimental study presents a number of issues that pose a challenge for practical configuration tuning and its deployment in data …
A Fekry,
L Carata,
T Pasquier,
A Rice,
A Hopper
The execution of distributed data processing workloads (such as those running on top of Hadoop or Spark) in cloud environments presents …
A Fekry,
L Carata,
T Pasquier,
Andrew Rice,
Andy Hopper