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From Amila Jayasekara <thejaka.am...@gmail.com>
Subject Re: Work Stealing is not a good solution for Airavata.
Date Thu, 06 Oct 2016 18:50:07 GMT
On Thu, Oct 6, 2016 at 11:07 AM, Shameera Rathnayaka <shameerainfo@gmail.com
> wrote:

> Hi Amila,
>
> -- Please explain how you used "work stealing" in distributed system. That
>> would be interesting.
>>
>
> Airavata depends on work stealing + amqp for followings,
> Fault Tolerance - This is one of major distributed system problem which
> critical in Airavata, What ever the reason experiment request processing
> shouldn't get any effect  from internal node failure. Even with the node
> failures, Airavata should be capable enough to continue experiment request
> processing or hold it until at least one node appear and then continue. How
> this is handled in Ariavata is, worker only ack for messages only after it
> completely processed it. If the node goes down without sendings  ack for
> the messages it was processing,then rabbitmq put all these un-ack messages
> back to the queue and available to consume again.
>
> Resource Utilization- Another important goal of distributed system to
> effectively use available resources in the system, namely the memory and
> processors of components.  In Airavata this will decide the throughput and
> response time of experiments. Currently, at a given time workers only get
> messages up to a preconfigured limit (the limit is prefetch count) But most
> of these jobs are async jobs. That means after worker gets fixed amount of
> jobs, it won't get any other jobs even worker capable or handling more
> jobs, waste of worker resources.
>


You still did not answer my question. I want to know how you used "work
stealing" in your implementation. In other words how distributed work
stealing works in your implementation. The details  you gave above is
unrelated and does not answer my question.


>
>
>>
>> -- I dont see AMQP in the architecture diagram you attached above and I
>> dont understand why Airavata has to depend on it. One way to figure this
>> out is think about the architecture without AMQP and figure out what
>> actually should happend and look for a way to do that using AMQP.
>>
>
> Worker Queue is AMQP queue.
>

Does the worker queue needs to be an AMQP queue ? Sorry, I dont know much
about AMQP but it sounds like limitations you are explaining are because of
AMQP.


>
>
>> -- Does this mean that you have a waiting thread or process within
>> Airavata after submitting the job (for each work) ?
>>
>
> No, once the job is submitted to the remote resource, thread goes back to
> the thread pool.
>

Then, your previous explanation, (i.e., "The time needs for a worker to
finish the work is depend on the application run time (applications runs on
HPC machine). Theoretically, this can be from few sec to days or even
more."), invalidates. Correct ?


>
> Thanks,
> Shameera.
>
>
>>
>> It takes more time for me to digest following right now. I will try to
>> give more feedback when I properly understand them.
>>
>> Thanks
>> -Amila
>>
>>
>>
>> That means, If a worker can read N(=prefetch count) number of messages
>> from a queue without sending acknowledgment then that is the limit one
>> worker can handle for given time. But most of this long-running jobs are
>> asynchronous. Worker resources are free to handle more works than N. Hence
>> Airavata underutilized worker resources. In the case of small jobs (small
>> in runtime), this won't be a big problem.
>>
>> Apache Zookeeper[2] provide a way to manage distributed system components
>> and most of  the latest distributed systems have been used Zookeeper to
>> address all the common distributed system problems like HA, FT, Leader
>> Election etc ... But in Airavata is trying to replace Rabbitmq with
>> Zookeeper to achieve the same outcomes, I haven't seen any framework have
>> done it. How Airavata tries to do is using Work Stealing Queues. Anyway,
>> Airavata hasn't move zookeeper out of its architecture yet as it uses
>> zookeeper to handle cancel requests.
>>
>>
>>
>> Regarding 3 => Well.. email monitoring was alway problematic. More
>> precisely monitoring is problematic IMO. To handle monitoring I think we
>> should be able to get better feedback from job schedulers but as per my
>> experience, even those job schedulers are unreliable. Until we have a
>> better way to get feedback from job scheduler, monitoring is going to be
>> challenging. However, I don't understand why you have "serious scalability"
>> issues in GFac because of this.
>>
>>
>> Let me explain more about this, I have used some terms comes with latest
>> AMQP spec here. Let's say we have two GFac Workers in the system. and both
>> submit jobs to Computer Resources and waiting for status update via emails
>> (We finally decided to depend on emails for a while come up with more
>> robust monitoring solution) when emails come, there is email monitoring
>> server which reads this emails and put it to a rabbitmq exchange[3].  Then
>> each gfac worker has subscribed to this exchange to get all email updates.
>> Because Framework doesn't know which worker handle particular jobs, it
>> sends this email content to all workers who subscribe to that exchange.
>> There are few issues in this way.
>>
>> 1. what if one of the workers goes offline for a moment? It should
>> receive the messages from where he left the queue. To do that we need to
>> use persistence queue which doesn't remove when consumer disconnect. And
>> this queue should receive all email updates messages as well during the
>> consumer down time. To create a persistent queue and reconnect again to the
>> same queue, the consumer should know about the queue name. Ok, let's say
>> worker create this queue  with a name in the very first time it joins to
>> email monitoring exchange. Now this problem is solved see the second issue.
>>
>> 2. What if one worker node goes down and we start a different node ( runs
>> in different Machine/VM) . Now there is no way this new worker knows the
>> queue name created by the previous worker unless we configure it which is
>> not a very good solution where we have a pool of workers and this pool
>> getting changes time to time. Now the real problem is all the jobs handled
>> by down node is getting to this new node but there is no way it gets
>> previous email monitoring messages. which make these jobs hanging on it
>> previous state forever. Even the previously down worker comes up this might
>> not get previous jobs instead it retrieves a new set of jobs. This means we
>> can't scale Gfac workers independently. Hope this will explain the issue.
>>
>>
>>
>> In summary, to me, none of these are concerns for the architecture at the
>> moment. Also, you cannot just naively complain an architecture is "not
>> good". Architecture has to be compared with another design and evaluate and
>> pros/cons for both. I suggest we first try to improve the existing design
>> to handle issues you are pointing.
>>
>>
>> It would be great to get some solution for above issues. IMHO, we have
>> overcomplicated Airavata design with Work Stealing approach, which is not
>> suitable for Airavata use cases and requirement.
>>
>> Thank you for your feedbacks, hope I answered to all of your concerns,
>>
>>
>> [1] http://www.rabbitmq.com/consumer-prefetch.html
>> [2] https://zookeeper.apache.org
>> [3] https://www.rabbitmq.com/tutorials/tutorial-two-python.html
>>
>> Thanks,
>> Shameera.
>>
>> [2] https://en.wikipedia.org/wiki/Work_stealing
>>
>> Thanks
>> -Amila
>>
>>
>> On Tue, Oct 4, 2016 at 1:07 PM, Shameera Rathnayaka <
>> shameerainfo@gmail.com> wrote:
>>
>> Hi Devs,
>>
>> Airavata has adopted to work stealing design pattern lately and use work
>> queue approach to distributing works among consumers. There are two work
>> queues in current Airavata architecture. One in middle of API Server and
>> Orchestrator and the second one in between Orchestrator and Gfac, Following
>> is very high-level Airavata architecture.
>>
>>
>> [image: Highleve Arachitecture- Airavata.png]
>> Here are the issues we have with above architecture.
>>
>> 1. Low resource utilization in Workers (Gfac/Orchestrator).
>> We have used AMPQ prefetch count to limit the number of requests served
>> by a Worker, which is not a good way to load balance in the
>> heterogeneous environment where different workers have different level of
>> resources. And it is recommended to keep this prefetch count minimum [1]
>> and this is valid for work stealing too. If we only have one worker and we
>> have M ( > N) number of long running jobs, and our prefetch count is N
>> then, only N jobs will in active mode. As we are run this long-running job
>> in the async way, we can handle more long running jobs than N. Therefore
>> workers resources are underutilized.
>>
>> 2. Even though we can easily deal with recovery requirement with work
>> stealing, it is not easy to handle cancel experiments. When this cancel
>> experiment comes the worker who works on this experiment should act
>> immediately. To add this behavior we need to introduce priority queues and
>> no need say this will add extra layer of complexity. Currently, we use
>> zookeeper to trigger cancel requests ( Another downside, we are using both
>> zookeeper and rabbitmq to solve different parts of Distributed systems
>> issues. Almost all latest Distributed system frameworks have being used
>> zookeeper to manage distributed system problems, we need to strongly
>> consider using zookeeper  as a way of managing our components and share the
>> load according to the resource available in workers)
>>
>> 3. Putting email to a queue is not a good solution with commodity servers
>> where system failures are expected. This email queue is critical, if we
>> missed one of the statuses of a job then this job can go to the unknown
>> state or hang in the old status forever. Due to this, we have serious
>> scalability issue with GFac at the moment due to a bottleneck of email
>> monitoring.
>>
>> I think we need to re-evaluate Airavata architecture and find a good yet
>> simple solution based on requirements. The new architecture should handle
>> all existing issues and able to extend future requirement.
>>
>>
>> [1] http://www.mariuszwojcik.com/blog/How-to-choose-
>> prefetch-count-value-for-RabbitMQ
>> --
>> Shameera Rathnayaka
>>
>>
>> --
>> Shameera Rathnayaka
>>
>> --
> Shameera Rathnayaka
>

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