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Haiou Fang commented on YARN-1021: ---------------------------------- Hi, [~ywskycn] I am sorry to disturb you with such question and I have run the sls successful. “bin/slsrun.sh --input-sls=sls-file/sls-jobs.json --output-dir=output_sls --nodes=sls-file/sls-nodes.json ” thank you > 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. -- This message was sent by Atlassian JIRA (v6.2#6252)