JiankunLiu updated YARN-2381:

    Summary: CLONE  (was: CLONE - Yarn Scheduler Load Simulator)

> -----
>                 Key: YARN-2381
>                 URL: https://issues.apache.org/jira/browse/YARN-2381
>             Project: Hadoop YARN
>          Issue Type: New Feature
>            Reporter: JiankunLiu
> 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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