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https://issues.apache.org/jira/browse/YARN-1021?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13746666#comment-13746666
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Carlo Curino commented on YARN-1021:
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The key idea behind the discrete event simulation is that you completely
decouple yourself from any real-world (or slow-down/fasten) clock. This allows
you to step through the execution, set debug points, and definitely get a
deterministic rexec. Overall this is really nice for debugging, and as a rig
for tests.
On the other end, in this approach you tend to pick an event order and execute
multiple threads in some deterministic order, which doesn't exercise all
possible code interleavings. As such it is complementary to what you have,
which by being less deterministic might allow you to spot timing and
concurrency bugs.
Ideally we would want a simulator that can provide both, and even more ideally
a simulator that logs a non-det execution, and than it can replay it
deterministically. I think this would have cut down our development of
preemption related code by a good 30%.
My 2 cents.
(BTW I am about to travel abroad, so expect major latency and packet drops for
the next 3 weeks :-))
> 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
> 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.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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