PhD Defense - Li Yu
|Start:||7/23/2013 at 2:00PM|
|End:||7/23/2013 at 5:00PM|
|Location:||100 Stinson-Remick Hall Conference Room|
Li Yu, a Computer Science and Engineering PhD candidate, will present and defend his dissertation:
"Resource Management for Workflows on Clusters, Clouds, and Grids" on July 23rd at 2:00 p.m. in the 100 Stinson-Remick Hall Conference Room.
His advisor Dr. Douglas Thain, and committee members Dr. Frank Collins, Dr. Marina Blanton, Dr. Scott Emrich, and Dr. Jesus Izaguirre will be in attendance.
Students and faculty are welcome to attend the presentation portion of the defense. Light refreshments will be served.
Clouds have joined clusters and grids as powerful and scalable environments for large scale scientific computing. While these platforms together provide virtually unlimited computing resources, using more resources for an application does not always result in superior performance. The extra amount that does not contribute to any performance increase is obviously a waste, which directly translates to waste of money on public clouds. This dissertation seeks to answer the question of how many computing resources should be allocated for a given workload. Two categories of workloads – static and dynamic, are identified where viable solutions for this problem are found. For static workloads, we show that distributed abstractions allow for accurate performance modeling on distributed, multicore, and distributed multicore systems and thus can assist making resource allocation decisions. For dynamic workloads, we present dynamic capacity management as a solution to avoid resource waste without compromising on the application performance. We evaluate the effectiveness of this technique on a selection of workload patterns, ranging from highly homogeneous to completely random, and observe that the system is able to significantly reduce wasted resources with minimal impact on performance. Finally, we show that both solutions have been successfully applied in real world scientific applications in areas such as bioinformatics, economics, and molecular modeling.