Tuneful

Last updated on Nov 28, 2020
Tuneful

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.

Cloud Computing Big Data Spark Machine-Learning Bayesian Optimization
Thomas Pasquier
Thomas Pasquier
Lecturer (Assistant Professor) in Computer Science

My research interests include distributed robotics, mobile computing and programmable matter.

Publications

Accelerating the Configuration Tuning of Big Data Analytics with Similarity-aware Multitask Bayesian Optimization
One of the key challenges for data analytics deployment is configuration tuning. The existing approaches for configuration tuning are expensive and overlook the dynamic characteristics of the …
A Fekry, L Carata, T Pasquier, A Rice
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Accelerating the Configuration Tuning of Big Data Analytics with Similarity-aware Multitask Bayesian Optimization
To Tune or Not to Tune? In Search of Optimal Configurations for Data Analytics
This experimental study presents a number of issues that pose a challenge for practical configuration tuning and its deployment in data analytics frameworks. These issues include: 1) the assumption of …
A Fekry, L Carata, T Pasquier, A Rice, A Hopper
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To Tune or Not to Tune? In Search of Optimal Configurations for Data Analytics
Towards Seamless Configuration Tuning of Big Data Analytics
The execution of distributed data processing workloads (such as those running on top of Hadoop or Spark) in cloud environments presents a unique opportunity to explore multiple trade-offs between …
A Fekry, L Carata, T Pasquier, Andrew Rice, Andy Hopper
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