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https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ahmed Hussein updated MAPREDUCE-7208:
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Attachment: MAPREDUCE-7208.003.patch
> 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
> Priority: Minor
> Attachments: MAPREDUCE-7208.001.patch, MAPREDUCE-7208.002.patch,
> MAPREDUCE-7208.003.patch, 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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