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https://issues.apache.org/jira/browse/MAPREDUCE-728?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12758621#action_12758621
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Hadoop QA commented on MAPREDUCE-728:
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-1 overall. Here are the results of testing the latest attachment
http://issues.apache.org/jira/secure/attachment/12420122/mapreduce-728-20090918-6.patch
against trunk revision 817740.
+1 @author. The patch does not contain any @author tags.
+1 tests included. The patch appears to include 30 new or modified tests.
+1 javadoc. The javadoc tool did not generate any warning messages.
+1 javac. The applied patch does not increase the total number of javac
compiler warnings.
+1 findbugs. The patch does not introduce any new Findbugs warnings.
+1 release audit. The applied patch does not increase the total number of
release audit warnings.
-1 core tests. The patch failed core unit tests.
-1 contrib tests. The patch failed contrib unit tests.
Test results:
http://hudson.zones.apache.org/hudson/job/Mapreduce-Patch-h6.grid.sp2.yahoo.net/124/testReport/
Findbugs warnings:
http://hudson.zones.apache.org/hudson/job/Mapreduce-Patch-h6.grid.sp2.yahoo.net/124/artifact/trunk/build/test/findbugs/newPatchFindbugsWarnings.html
Checkstyle results:
http://hudson.zones.apache.org/hudson/job/Mapreduce-Patch-h6.grid.sp2.yahoo.net/124/artifact/trunk/build/test/checkstyle-errors.html
Console output:
http://hudson.zones.apache.org/hudson/job/Mapreduce-Patch-h6.grid.sp2.yahoo.net/124/console
This message is automatically generated.
> Mumak: Map-Reduce Simulator
> ---------------------------
>
> Key: MAPREDUCE-728
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-728
> Project: Hadoop Map/Reduce
> Issue Type: New Feature
> Affects Versions: 0.21.0
> Reporter: Arun C Murthy
> Assignee: Hong Tang
> Fix For: 0.21.0, 0.22.0
>
> Attachments: 19-jobs.topology.json.gz, 19-jobs.trace.json.gz,
> mapreduce-728-20090917-3.patch, mapreduce-728-20090917-4.patch,
> mapreduce-728-20090917.patch, mapreduce-728-20090918-2.patch,
> mapreduce-728-20090918-3.patch, mapreduce-728-20090918-5.patch,
> mapreduce-728-20090918-6.patch, mapreduce-728-20090918.patch, mumak.png
>
>
> h3. Vision:
> We want to build a Simulator to simulate large-scale Hadoop clusters,
> applications and workloads. This would be invaluable in furthering Hadoop by
> providing a tool for researchers and developers to prototype features (e.g.
> pluggable block-placement for HDFS, Map-Reduce schedulers etc.) and predict
> their behaviour and performance with reasonable amount of confidence,
> there-by aiding rapid innovation.
> ----
> h3. First Cut: Simulator for the Map-Reduce Scheduler
> The Map-Reduce Scheduler is a fertile area of interest with at least four
> schedulers, each with their own set of features, currently in existence:
> Default Scheduler, Capacity Scheduler, Fairshare Scheduler & Priority
> Scheduler.
> Each scheduler's scheduling decisions are driven by many factors, such as
> fairness, capacity guarantee, resource availability, data-locality etc.
> Given that, it is non-trivial to accurately choose a single scheduler or even
> a set of desired features to predict the right scheduler (or features) for a
> given workload. Hence a simulator which can predict how well a particular
> scheduler works for some specific workload by quickly iterating over
> schedulers and/or scheduler features would be quite useful.
> So, the first cut is to implement a simulator for the Map-Reduce scheduler
> which take as input a job trace derived from production workload and a
> cluster definition, and simulates the execution of the jobs in as defined in
> the trace in this virtual cluster. As output, the detailed job execution
> trace (recorded in relation to virtual simulated time) could then be analyzed
> to understand various traits of individual schedulers (individual jobs turn
> around time, throughput, faireness, capacity guarantee, etc). To support
> this, we would need a simulator which could accurately model the conditions
> of the actual system which would affect a schedulers decisions. These include
> very large-scale clusters (thousands of nodes), the detailed characteristics
> of the workload thrown at the clusters, job or task failures, data locality,
> and cluster hardware (cpu, memory, disk i/o, network i/o, network topology)
> etc.
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