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https://issues.apache.org/jira/browse/MAPREDUCE-7208?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16852022#comment-16852022
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Ahmed Hussein commented on MAPREDUCE-7208:
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[~jeagles], [~tgraves], [~vinodkv], [~nroberts]
I had some issues using {{ExponentiallySmoothedTaskRuntimeEstimator}}. I made
some investigation and implemented a new estimator that addresses some issues
with the existing smoothing factor estimator. Do you mind taking a look at the
suggested fixes and implementations?
*{{SimpleExponentialTaskRuntimeEstimator}} (new) Vs
{{ExponentiallySmoothedTaskRuntimeEstimator}} (old)*
# New estimator follows Basic Exponential Smooth.
# New estimator does not return an estimate for the first few cycles. This
increases the accuracy of estimation; especially for long running tasks
# New Estimator detects tasks that are slowing down. Old estimator fails to
detect such scenarios.
# New Estimator detects stalled tasks. Old estimator will not launch any
speculative attempts when an attempt has a sharp slow down.
*Is the default speculator affected?*
* The speculator is still using the {{LegacyTaskRuntimeEstimator}} by default.
* The existing implementation uses the statistics.mean to get an
{{estimatedNewAttemptRuntime()}}. This causes frequent speculation as the
smallest difference between the {{estimatedRuntime}} and the mean will create a
new speculativeAttempt. I changed the implementation of
{{estimatedNewAttemptRuntime()}} so that it uses (mean + a small delta)
* I created a n JUnit {{TestSpeculativeExecOnCluster}} that verifies the
speculator running on {{MiniMRYarnCluster}}. The test case can be used for the
old estimators.
*Tuning parameters:*
* {{job.task.estimator.simple.exponential.smooth.lambda-ms}}: The lambda value
in the smoothing function of the task estimator
* {{job.task.estimator.simple.exponential.smooth.stagnated-ms}}: The window
length in the simple exponential smoothing that considers the task attempt is
stagnated. This allows the speculator to detect stalled progress.
* {{job.task.estimator.simple.exponential.smooth.skip-initials}}: The number
of initial readings that the estimator ignores before giving a prediction. A
simple smoothing needs several iterations before adjusting and returning good
estimates. The skip-initials parameter instructs the estimator to return
"no-information" progress updates did not reach that value.
> 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, 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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