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From "zhengchenyu (JIRA)" <j...@apache.org>
Subject [jira] [Issue Comment Deleted] (YARN-5936) when cpu strict mode is closed, yarn couldn't assure scheduling fairness between containers
Date Tue, 20 Dec 2016 02:43:58 GMT

     [ https://issues.apache.org/jira/browse/YARN-5936?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

zhengchenyu updated YARN-5936:
------------------------------
    Comment: was deleted

(was: But your "time" command is only related to it's own program, every program is single
thread. 
My question is below:
     two process that has different numbers of threads has different ability of schedule,
though they have the same cpu.share. 
I know the reason, but I don't have a proper suggestion to avoid this problem.)

> when cpu strict mode is closed, yarn couldn't assure scheduling fairness between containers
> -------------------------------------------------------------------------------------------
>
>                 Key: YARN-5936
>                 URL: https://issues.apache.org/jira/browse/YARN-5936
>             Project: Hadoop YARN
>          Issue Type: Bug
>          Components: nodemanager
>    Affects Versions: 2.7.1
>         Environment: CentOS7.1
>            Reporter: zhengchenyu
>            Priority: Critical
>             Fix For: 2.7.1
>
>   Original Estimate: 1m
>  Remaining Estimate: 1m
>
> When using LinuxContainer, the setting that "yarn.nodemanager.linux-container-executor.cgroups.strict-resource-usage"
is true could assure scheduling fairness with the cpu bandwith of cgroup. But the cpu bandwidth
of cgroup would lead to bad performance in our experience. 
>     Without cpu bandwidth of cgroup, cpu.share of cgroup is our only way to assure scheduling
fairness, but it is not completely effective. For example, There are two container that have
same vcore(means same cpu.share), one container is single-threaded, the other container is
multi-thread. the multi-thread will have more CPU time, It's unreasonable!
>     Here is my test case, I submit two distributedshell application. And two commmand
are below:
> {code}
> hadoop jar share/hadoop/yarn/hadoop-yarn-applications-distributedshell-2.7.1.jar org.apache.hadoop.yarn.applications.distributedshell.Client
-jar share/hadoop/yarn/hadoop-yarn-applications-distributedshell-2.7.1.jar -shell_script ./run.sh
 -shell_args 10 -num_containers 1 -container_memory 1024 -container_vcores 1 -master_memory
1024 -master_vcores 1 -priority 10
> hadoop jar share/hadoop/yarn/hadoop-yarn-applications-distributedshell-2.7.1.jar org.apache.hadoop.yarn.applications.distributedshell.Client
-jar share/hadoop/yarn/hadoop-yarn-applications-distributedshell-2.7.1.jar -shell_script ./run.sh
 -shell_args 1  -num_containers 1 -container_memory 1024 -container_vcores 1 -master_memory
1024 -master_vcores 1 -priority 10
> {code}
>      here show the cpu time of the two container:
> {code}
>   PID USER      PR  NI    VIRT    RES    SHR S  %CPU %MEM     TIME+ COMMAND
> 15448 yarn      20   0 9059592  28336   9180 S 998.7  0.1  24:09.30 java
> 15026 yarn      20   0 9050340  27480   9188 S 100.0  0.1   3:33.97 java
> 13767 yarn      20   0 1799816 381208  18528 S   4.6  1.2   0:30.55 java
>    77 root      rt   0       0      0      0 S   0.3  0.0   0:00.74 migration/1   
> {code}
>     We find the cpu time of Muliti-Thread are ten times than the cpu time of Single-Thread,
though the two container have same cpu.share.
> notes:
> run.sh
> {code} 
> 	java -cp /home/yarn/loop.jar:$CLASSPATH loop.loop $1	
> {code} 
> loop.java
> {code} 
> package loop;
> public class loop {
> 	public static void main(String[] args) {
> 		// TODO Auto-generated method stub
> 		int loop = 1;
> 		if(args.length>=1) {
> 			System.out.println(args[0]);
> 			loop = Integer.parseInt(args[0]);
> 		}
> 		for(int i=0;i<loop;i++){
> 			System.out.println("start thread " + i);
> 			new Thread(new Runnable() {
> 				@Override
> 				public void run() {
> 					// TODO Auto-generated method stub
> 					int j=0;
> 					while(true){j++;}
> 				}
> 			}).start();
> 		}
> 	}
> }
> {code}



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