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https://issues.apache.org/jira/browse/YARN-1021?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14010234#comment-14010234
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Wei Yan commented on YARN-1021:
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[~cristiana.voicu], the SLS directly supports rumen traces. In general, you
need to have some existing workload traces (i.e., from some production
clusters), and then use Rumen to generate workload traces. Then let the SLS
load these traces. Or you can generate some traces randomly (random # of jobs,
requests, lifetime, etc).
Sorry that I don't have the traces used in that page right now.
> Yarn Scheduler Load Simulator
> -----------------------------
>
> Key: YARN-1021
> URL: https://issues.apache.org/jira/browse/YARN-1021
> Project: Hadoop YARN
> Issue Type: New Feature
> Components: scheduler
> Reporter: Wei Yan
> Assignee: Wei Yan
> Fix For: 2.3.0
>
> Attachments: YARN-1021-demo.tar.gz, YARN-1021-images.tar.gz,
> YARN-1021.patch, YARN-1021.patch, YARN-1021.patch, YARN-1021.patch,
> YARN-1021.patch, YARN-1021.patch, YARN-1021.patch, YARN-1021.patch,
> YARN-1021.patch, YARN-1021.patch, YARN-1021.patch, YARN-1021.patch,
> YARN-1021.patch, YARN-1021.patch, YARN-1021.patch, YARN-1021.pdf
>
>
> The Yarn Scheduler is a fertile area of interest with different
> implementations, e.g., Fifo, Capacity and Fair schedulers. Meanwhile,
> several optimizations are also made to improve scheduler performance for
> different scenarios and workload. Each scheduler algorithm has its own set of
> features, and drives scheduling decisions by many factors, such as fairness,
> capacity guarantee, resource availability, etc. It is very important to
> evaluate a scheduler algorithm very well before we deploy it in a production
> cluster. Unfortunately, currently it is non-trivial to evaluate a scheduling
> algorithm. Evaluating in a real cluster is always time and cost consuming,
> and it is also very hard to find a large-enough cluster. Hence, a simulator
> which can predict how well a scheduler algorithm for some specific workload
> would be quite useful.
> We want to build a Scheduler Load Simulator to simulate large-scale Yarn
> clusters and application loads in a single machine. This would be invaluable
> in furthering Yarn by providing a tool for researchers and developers to
> prototype new scheduler features and predict their behavior and performance
> with reasonable amount of confidence, there-by aiding rapid innovation.
> The simulator will exercise the real Yarn ResourceManager removing the
> network factor by simulating NodeManagers and ApplicationMasters via handling
> and dispatching NM/AMs heartbeat events from within the same JVM.
> To keep tracking of scheduler behavior and performance, a scheduler wrapper
> will wrap the real scheduler.
> The simulator will produce real time metrics while executing, including:
> * Resource usages for whole cluster and each queue, which can be utilized to
> configure cluster and queue's capacity.
> * The detailed application execution trace (recorded in relation to simulated
> time), which can be analyzed to understand/validate the scheduler behavior
> (individual jobs turn around time, throughput, fairness, capacity guarantee,
> etc).
> * Several key metrics of scheduler algorithm, such as time cost of each
> scheduler operation (allocate, handle, etc), which can be utilized by Hadoop
> developers to find the code spots and scalability limits.
> The simulator will provide real time charts showing the behavior of the
> scheduler and its performance.
> A short demo is available http://www.youtube.com/watch?v=6thLi8q0qLE, showing
> how to use simulator to simulate Fair Scheduler and Capacity Scheduler.
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