Real Time Autotuning for MapReduce on Hadoop/YARN

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.


Pospelova, Maria




Humanity entered the Big Data era passing through the zettabite mark at an exponential rate in just a few years. The Apache Hadoop is the most popular Big Data processing framework. However, Hadoop performance is highly dependent on chosen settings. An automatic tuner is a desirable solution. Existent off-line tuning approaches require multiple repetitive executions of the job in order to find optimal tuning settings and, hence, are not applicable to use in most cases.

The presented work introduces a novel real time autotuning approach. The resource management parameters are tuned between execution waves by a modular autotuner connected to Hadoop architecture through YARN. The developed autotuner effectively intercepts a resource request, modifies it according to a tuning algorithm and passes it to YARN Scheduler. Such an approach not only carries high practical and theoretical value, but also opens a new horizon in the Hadoop/YARN automatic tuning development.


Computer Science




Carleton University

Thesis Degree Name: 

Master of Computer Science: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Computer Science

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).