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From "Ahmed Hussein (JIRA)" <j...@apache.org>
Subject [jira] [Created] (MAPREDUCE-7208) Tuning TaskRuntimeEstimator
Date Wed, 29 May 2019 20:09:00 GMT
Ahmed Hussein created MAPREDUCE-7208:
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             Summary: Tuning TaskRuntimeEstimator 
                 Key: MAPREDUCE-7208
                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-7208
             Project: Hadoop Map/Reduce
          Issue Type: Improvement
            Reporter: Ahmed Hussein
            Assignee: Ahmed Hussein
         Attachments: smoothing-exponential.md

By default, MR uses LegacyTaskRuntimeEstimator to get an estimate of the runtime.  The estimator
does not adjust dynamically to the progress rate of the tasks. On the other hand, the existing
alternative "ExponentiallySmoothedTaskRuntimeEstimator" behavior in unpredictable.

 

There are several dimensions to improve the exponential implementation:
 # Exponential shooting needs a warmup period. Otherwise, the estimate will be affected by
the initial values.
 # Using a single smoothing factor (Lambda) does not work well for all the tasks. To increase
the level of smoothing across the majority of tasks, we need to give a range of flexibility
to dynamically adjust the smoothing factor based on the history of the task progress.
 # Design wise, it is better to separate between the statistical model and the MR interface. We
need to have a way to evaluate estimators statistically, without the need to run MR. For example,
an estimator can be evaluated as a black box by using a stream of raw data as input and testing
the accuracy of the generated stream of estimates.
 # The exponential estimator speculates frequently and fails to detect slowing tasks. It does
not detect slowing tasks. As a result, a taskAttempt that does not do any progress won't trigger
a new speculation.

 

The file [^smoothing-exponential.md] describes how Simple Exponential smoothing factor works.

 

 



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