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 complete 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 latency 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 demonstrate that Sparrow performs within 14% of an ideal scheduler.
Title
Sparrow: Scalable Scheduling for Sub-Second Parallel Jobs
Published
2013-04-10
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2013-29
Type
Text
Extent
16 p
Archive
The Engineering Library
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