Abstract

Large-scale data centers leverage virtualization to achieve high
resource utilization, scalability, and availability. While ideally the
performance of an application running inside a virtual machine (VM)
shall be independent of co-located applications and VMs that share the
physical resources, current systems are yet to achieve this goal. In
this talk, I will describe our efforts in addressing a number of
challenges in order to achieve optimal I/O performance in such
virtualized systems. Specifically, TRACON constructs mathematical
models and scheduling algorithms to mitigate the VM interference;
Matrix leverages machine learning and optimization techniques to
allocate VM resource in a way that minimizes the cost while achieving
good application performance; and Mortar enhances the hypervisor to
pool together spare memory on each machine and expose it as a volatile
data cache to improve virtual I/O performance.

Speaker's Bio

Howie Huang is an Assistant Professor in Department of Electrical and
Computer Engineering at the George Washington University. His research
interests are in the areas of computer systems and architecture,
including cloud computing, big data computing, high-performance
computing and storage systems. His projects won the Best Poster Award
at PACT'11, ACM Undergraduate Student Research Competition at SC'12,
and a finalist for the Best Student Paper Award at SC'11. He was a
recipient of the NSF CAREER Award in 2014, NVIDIA Academic Partnership
Award in 2011, IBM Real Time Innovation Faculty Award in 2008, and
School of Engineering and Applied Science Outstanding Young Researcher
Award in 2014. He received his Ph.D. in Computer Science from the
University of Virginia in 2008.