Ma, Xiaosong

Othmane, Bouhali

English

Texas A&M University

Xiaosong Ma 
Xiaosong Ma is currently a principal scientist at Qatar Computing 
Research Institute, HBKU. Previously, she was associate professor 
in the Department of Computer Science at North Carolina State 
University, as well as a joint faculty member at Oak Ridge National 
Laboratory. Her research interests are in the areas of storage systems, 
parallel I/O, high-performance parallel applications, cloud computing, 
and self-configurable performance optimization. She received the 
U.S. DOE Early Career Principal Investigator Award in 2005, the NSF 
CAREER Award in 2006, an IBM Faculty Award in 2009, and a NetApp 
Faculty Fellowship, an HPDC 20-Year Best Paper Award, as well as 
a University of Illinois Department of Computer Science Alumni 
Distinguished Educator Award in 2012. Xiaosong received her Ph.D. in 
computer science from the University of Illinois at Urbana-Champaign 
in 2003 and her B.S. in computer science from Peking University 
(China) in 1997.
Texas A&M at Qatar 
tasc
Server-side Log Data Analytics for 
Supercomputer I/O Workload Characterization
For more information on 
the lecture, please contact  
Dr. Othmane Bouhali.
othmane.bouhali@qatar.tamu.edu
RSVP
2 May, 1–2 p.m., LH143
Inter-application I/O contention and performance interference has 
been recognized as a severe problem. In this work, we demonstrate 
that high I/O variance co-exists with the fact that individual storage 
units remain under-utilized for the majority of time. This motivates 
us to propose IOAI, a system that performs automatic application I/O 
characterization and I/O-aware job scheduling. IOAI analyzes existing 
I/O traffic and batch job history logs, without any application-related 
prior knowledge or user/developer’s involvement. It identifies the small 
set of I/O-intensive candidates among parallel applications running 
on a supercomputer and subsequently mines their I/O patterns, 
using more detailed per-I/O-node traffic logs. Based on such auto-
extracted information, IOAI provides online I/O-aware scheduling 
recommendations to steer I/O-intensive applications away from heavy 
ongoing I/O activities.
We evaluate IOAI on the same supercomputer, using both real 
applications (with extracted I/O patterns validated by contacting users) 
and our own pseudo-applications. Our results confirm that IOAI is 
able to (1) identify I/O-intensive applications, plus their detailed I/O 
characteristics, and (2) significantly reduce these applications’ I/O 
performance degradation/variance by jointly evaluating outstanding 
applications’ I/O pattern and real-time system l/O load. 
Xiaosong Ma 
Principal Scientist, Qatar Computing Research Institute, HBKU


Server-side Log Data Analytics for Supercomputer I/O Workload Characterization

https://oaktrust.library.tamu.edu/bitstream/1969.1/187856/1/2May2017.pdf

Server-side Log Data Analytics for Supercomputer I/O Workload Characterization

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