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https://issues.apache.org/jira/browse/YARN-569?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13650541#comment-13650541
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Carlo Curino commented on YARN-569:
-----------------------------------

Hi Bikas, I noticed that your patch and ours share a common architectural 
style, i.e., the preemption policy runs in a separate thread on a timer. 
Moreover, they also seem to mostly agree on the I/O to/from the policy, we both 
grab state from the CapacityScheduler (e.g., the root of the queues) as in 
input, and both trigger actions that affect the CapacityScheduler. In our 
design we tried to put the actions behind an event handler, but I think the 
ideas are very similar. 
In fact, I would guess that a good portion of your patch could be placed behind 
the ScheduleEditPolicy interface we defined. As I mentioned in some of our 
conversations, this is nice because the ScheduleEditPolicy API I think can be 
used also for other purposes (e.g., for a deadline-monitor, or an IO-starvation 
monitor, etc..). Basically to implement monitors that focus on specific (even 
orthogonal) properties of the schedule, and that can observe the cluster state 
through the CapacityScheduler viewpoint, and try to affect it somehow (via 
events in our design). As an example, imagine a deadline monitor trying to 
affect jobs' completion times, by tweaking capacity of the queues, or ordering 
of job in the queue etc. 
While I am not sure this API will see a broad public :-) it would be nice to 
agree on it. 

As for the specifics of what you do with all the enforcements stuff, I haven't 
read the code carefully enough to follow the details. Actually, if you have 
time to write a high-level summary of it and post it here, it would be useful 
to orient us through your patch. 

While I think it would be too convoluted to try to merge the two approaches, I 
would like to see whether, other than the SchedulerEditPolicy, there is more we 
can factor out to make your version of the policy easy to write. I know it 
wouldn't be hard to  evolve this later on, as the code is rather isolated, but 
if we can do something that make it easier now I think is worth considering.

                
> CapacityScheduler: support for preemption (using a capacity monitor)
> --------------------------------------------------------------------
>
>                 Key: YARN-569
>                 URL: https://issues.apache.org/jira/browse/YARN-569
>             Project: Hadoop YARN
>          Issue Type: Sub-task
>          Components: capacityscheduler
>            Reporter: Carlo Curino
>            Assignee: Carlo Curino
>         Attachments: 3queues.pdf, CapScheduler_with_preemption.pdf, 
> preemption.2.patch, YARN-569.patch, YARN-569.patch
>
>
> There is a tension between the fast-pace reactive role of the 
> CapacityScheduler, which needs to respond quickly to 
> applications resource requests, and node updates, and the more introspective, 
> time-based considerations 
> needed to observe and correct for capacity balance. To this purpose we opted 
> instead of hacking the delicate
> mechanisms of the CapacityScheduler directly to add support for preemption by 
> means of a "Capacity Monitor",
> which can be run optionally as a separate service (much like the 
> NMLivelinessMonitor).
> The capacity monitor (similarly to equivalent functionalities in the fairness 
> scheduler) operates running on intervals 
> (e.g., every 3 seconds), observe the state of the assignment of resources to 
> queues from the capacity scheduler, 
> performs off-line computation to determine if preemption is needed, and how 
> best to "edit" the current schedule to 
> improve capacity, and generates events that produce four possible actions:
> # Container de-reservations
> # Resource-based preemptions
> # Container-based preemptions
> # Container killing
> The actions listed above are progressively more costly, and it is up to the 
> policy to use them as desired to achieve the rebalancing goals. 
> Note that due to the "lag" in the effect of these actions the policy should 
> operate at the macroscopic level (e.g., preempt tens of containers
> from a queue) and not trying to tightly and consistently micromanage 
> container allocations. 
> ------------- Preemption policy  (ProportionalCapacityPreemptionPolicy): 
> ------------- 
> Preemption policies are by design pluggable, in the following we present an 
> initial policy (ProportionalCapacityPreemptionPolicy) we have been 
> experimenting with.  The ProportionalCapacityPreemptionPolicy behaves as 
> follows:
> # it gathers from the scheduler the state of the queues, in particular, their 
> current capacity, guaranteed capacity and pending requests (*)
> # if there are pending requests from queues that are under capacity it 
> computes a new ideal balanced state (**)
> # it computes the set of preemptions needed to repair the current schedule 
> and achieve capacity balance (accounting for natural completion rates, and 
> respecting bounds on the amount of preemption we allow for each round)
> # it selects which applications to preempt from each over-capacity queue (the 
> last one in the FIFO order)
> # it remove reservations from the most recently assigned app until the amount 
> of resource to reclaim is obtained, or until no more reservations exits
> # (if not enough) it issues preemptions for containers from the same 
> applications (reverse chronological order, last assigned container first) 
> again until necessary or until no containers except the AM container are left,
> # (if not enough) it moves onto unreserve and preempt from the next 
> application. 
> # containers that have been asked to preempt are tracked across executions. 
> If a containers is among the one to be preempted for more than a certain 
> time, the container is moved in a the list of containers to be forcibly 
> killed. 
> Notes:
> (*) at the moment, in order to avoid double-counting of the requests, we only 
> look at the "ANY" part of pending resource requests, which means we might not 
> preempt on behalf of AMs that ask only for specific locations but not any. 
> (**) The ideal balance state is one in which each queue has at least its 
> guaranteed capacity, and the spare capacity is distributed among queues (that 
> wants some) as a weighted fair share. Where the weighting is based on the 
> guaranteed capacity of a queue, and the function runs to a fix point.  
> Tunables of the ProportionalCapacityPreemptionPolicy:
> #     observe-only mode (i.e., log the actions it would take, but behave as 
> read-only)
> # how frequently to run the policy
> # how long to wait between preemption and kill of a container
> # which fraction of the containers I would like to obtain should I preempt 
> (has to do with the natural rate at which containers are returned)
> # deadzone size, i.e., what % of over-capacity should I ignore (if we are off 
> perfect balance by some small % we ignore it)
> # overall amount of preemption we can afford for each run of the policy (in 
> terms of total cluster capacity)
> In our current experiments this set of tunables seem to be a good start to 
> shape the preemption action properly. More sophisticated preemption policies 
> could take into account different type of applications running, job 
> priorities, cost of preemption, integral of capacity imbalance. This is very 
> much a control-theory kind of problem, and some of the lessons on designing 
> and tuning controllers are likely to apply.
> Generality:
> The monitor-based scheduler edit, and the preemption mechanisms we introduced 
> here are designed to be more general than enforcing capacity/fairness, in 
> fact, we are considering other monitors that leverage the same idea of 
> "schedule edits" to target different global properties (e.g., allocate enough 
> resources to guarantee deadlines for important jobs, or data-locality 
> optimizations, IO-balancing among nodes, etc...).
> Note that by default the preemption policy we describe is disabled in the 
> patch.
> Depends on YARN-45 and YARN-567, is related to YARN-568

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