Wednesday, 23 July 2014

GSOC Week 8+9 : To be or not to be

These past two weeks, I've been busy since my college re-opened and I spent my past weekend coding away in an overnight hackathon. As instructed by my mentor, I spent this week testing my recent patch whether enabling I/O profiling always in io-stats really degrades io-performance or not.

For this, I performed two write tests, one with a 20 MB file and the other with a 730 MB file. Each file was written 20 times to the mounted volume after clearing the buffers on every iteration and the time taken measured with the time command. Since the values at different times for writing the same file are quite varied, I plotted a graph using the obtained values(Y-axis represents seconds). As you might see in these images, there is no clear pattern found in the variation of values obtained while writing.

So according to me, values in both the conditions are quite near to each other and equally capable of going quite high or low than the mean value and hence, there is no negative effect seen due to the change proposed. You can follow this discussion on the ML at

Monday, 7 July 2014

GSOC Week 7 : Back on track

It's time to get back on track. Passing the midterms with supposedly good flying colors was really great. I apologize for my tardiness during the last two weeks for unable to post any update regarding my progress, owing to the fact of me not feeling very well during this time.

The progress till now includes re-thinking of the previous patch and the methodology io-stats will use to dump the private info. As suggested by my mentor, I'm moving the job of speed calculation and other major work to the glusterfsiostat script rather than code it all in the glusterfs codebase. You can look at the new patch here :

Also, my project was accepted to be hosted on Gluster Forge at where you can track the progress for the python script and rest of the code base related to my project.

Recently, my mentor and me have started to track our progress with the help of Scrum model, by using trello. This helps us break the bigger jobs into smaller tasks and set the deadline on each of them to better estimate their supposed date of completion.