[YARN-1969] Fair Scheduler: Add policy for Earliest Endtime First

• Type: Improvement
• Status: Resolved
• Priority: Major
• Resolution: Not A Problem
• Component/s: fairscheduler
• Assignee: Maysam Yabandeh
• Reporter: Maysam Yabandeh
• Created: 21/Apr/14 17:49
• Updated: 24/May/15 23:49
• Resolved: 24/May/15 23:49

Description

What we are observing is that some big jobs with many allocated containers are waiting for a few containers to finish. Under fair-share scheduling however they have a low priority since there are other jobs (usually much smaller, new comers) that are using resources way below their fair share, hence new released containers are not offered to the big, yet close-to-be-finished job. Nevertheless, everybody would benefit from an "unfair" scheduling that offers the resource to the big job since the sooner the big job finishes, the sooner it releases its "many" allocated resources to be used by other jobs.In other words, we need a relaxed version of Earliest Endtime First scheduling, that takes into account the number of already-allocated resources and estimated time to finish.

For example, if a job is using MEM GB of memory and is expected to finish in TIME minutes, the priority in scheduling would be a function p of (MEM, TIME). The expected time to finish can be estimated by the AppMaster using TaskRuntimeEstimator#estimatedRuntime and be supplied to RM in the resource request messages. To be less susceptible to the issue of apps gaming the system, we can have this scheduling limited to leaf queues which have applications.

Issue Links

• is blocked by: MAPREDUCE-5871 Estimate Job Endtime