2016 Poster Sessions : Canary: A Scheduling Architecture for High Performance Cloud Computing

Student Name : Hang Qu
Advisor : Philip Levis
Research Areas: Computer Systems
Abstract:
We present Canary, a scheduling architecture that allows high
performance analytics workloads to scale out to run on thousands of cores. Canary is motivated by the observation that a central scheduler is a bottleneck for high performance codes: a handful of multicore workers can execute tasks faster than a controller can schedule them.

The key insight in Canary is to reverse the responsibilities between controllers and workers. Rather than dispatch tasks to workers, which then fetch data as necessary, in Canary the controller assigns data
partitions to workers, which then spawn and schedule tasks locally.

Bio:
I am Hang Qu, a forth-year PhD student working with Professor Philip Levis. My research focuses on cloud computing, and specifically, how to make computation scalable and at the same time fast.