Large-scale data analytics frameworks are shifting towards shorter task durations and larger degrees of parallelism to provide low latency. However, scheduling highly parallel jobs that com- plete in hundreds of milliseconds poses a major challenge for cluster schedulers, which will need to place millions of tasks per second on appropriate nodes while offering millisecond-level la- tency and high availability. We demonstrate that a decentralized, randomized sampling approach provides near-optimal performance while avoiding the throughput and availability limitations of a centralized design. We implement and deploy our scheduler, Sparrow, on a real cluster and demon- strate that Sparrow performs within 14% of an ideal scheduler.
Title
Scalable Scheduling for Sub-Second Parallel Jobs
Published
2013-05-16
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2013-79
Type
Text
Extent
30 p
Archive
The Engineering Library
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