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From Supun Nakandala <supun.nakand...@gmail.com>
Subject Re: [#Spring17-Airavata-Courses] : Distributed Workload Management for Airavata
Date Tue, 07 Feb 2017 22:47:26 GMT
Hi Gourav,I agree with your idea of using one “workflow micro-service”
which would basically be the mediator/orchestrator for deciding which
micro-service should be executed next. But I think these components do not
necessarily have to be micro-services but rather conforms to the
master-worker paradigm in some sense. But the trick here is how can we
implement a scalable, fault tolerant system to do distributed workload
management and from CAP theorem what is the property that we are going to
compromise.

I think you are heading in the right direction. But I would like to add
more details to your solution. Please note that I haven't evaluated these
ideas 100%. Perhaps we can talk more about this in the next class.

As you have done, I think we should centralize the state information into
one component (orchestrator in our case). From my experience, it is very
hard to achieve consistency in a distributed state setting in the events of
failure.

Second, to maintain generalizability in Airavata I think we should treat
each application/use-cases as a DAG of execution. For example, HPC job and
a cloud job will have two different DAGs which consists of tasks (data
staging, job submission, out staging etc). These tasks should be short
tasks and should roughly have the same execution time. And having
idempotent tasks is preferable.

Orchestrator is responsible for executing the DAG and assign tasks to the
workers(how? will follow) based on the control dependencies in the DAG
tasks. In addition to the dependencies generated from tasks I see, there
can be other dependencies to things like monitoring and scheduling which
the orchestrator has to make into account when executing the DAG.

The next question is how we distribute jobs from Orchestrator to workers. I
think here it is ok to compromise availability in favor of consistency. I
suggest that we use the request/response messaging pattern which uses a
persistent message broker (critical service). In this architecture, we can
safely allow orchestrator or workers to fail without losing consistency
(because of the persistent queue). But if the orchestrator fails then the
availability will go down. One way to overcome this would be to come
up with an orchestrator quorum.Attached figure summarizes my idea.

I think we can also evaluate this solution with the concerns that Shameera
pointed out such as can we enable cancel?. Once again it's just my idea and
is open for argument and debate.



[image: Inline image 2]

Thanks
-Supun



On Tue, Feb 7, 2017 at 10:54 AM, Shenoy, Gourav Ganesh <goshenoy@indiana.edu
> wrote:

> Hi Supun,
>
>
>
> I agree, but may be for the example I mentioned, multiple micro-services
> might not sound necessary. I was trying to generalize towards a scenario
> where we have multiple independent micro-services (not necessarily for task
> execution). Again, I am not certain if this is the right architecture but
> yours (and other’s) inputs, will definitely help us narrow down on the
> different scenarios we need to exactly focus on. Do let me know if I make
> sense.
>
>
>
> Thanks and Regards,
>
> Gourav Shenoy
>
>
>
> *From: *Supun Nakandala <supun.nakandala@gmail.com>
> *Reply-To: *"dev@airavata.apache.org" <dev@airavata.apache.org>
> *Date: *Monday, February 6, 2017 at 12:15 PM
> *To: *dev <dev@airavata.apache.org>
>
> *Subject: *Re: [#Spring17-Airavata-Courses] : Distributed Workload
> Management for Airavata
>
>
>
> Hi Gourav,
>
>
>
> It is my belief that we don't need a separate microservice to each task. I
> favor a single micro service which can execute all tasks (or in other words
> a generic task execution micro service). Of course, we can have many of
> them when we want to scale. WDYT?
>
>
>
> On Sun, Feb 5, 2017 at 3:07 PM, Shenoy, Gourav Ganesh <
> goshenoy@indiana.edu> wrote:
>
> Hi dev,
>
>
>
> We were brainstorming some potential designs that might help us with this
> problem. One possible option would be to have a “workflow micro-service”
> which would basically be the mediator/orchestrator for deciding which
> micro-service should be executed next – based on the type of the job. The
> motive is to make micro-services independent of the workflow; i.e. a
> micro-service implementation should be not be aware of which micro-service
> will be executed next and we should have a central control of deciding this
> pattern.
>
> Eg: For job type X, the pattern could be A -> B -> C -> D. Whereas for job
> type Y, the pattern could be A -> C -> D; and so on.
>
>
>
> An initial design with this idea looks like follows:
>
>
>
>
>
> We would have a common messaging framework (implementation has not been
> decided yet). The database associated with the workflow micro-service could
> be a graph database (maybe?) – again the implementation/technology has not
> been decided yet.
>
>
>
> This is just a proposed design, and I would love to hear your thoughts on
> this and any suggestions/comments if any. If there is anything that we are
> missing or should consider, please do let us know.
>
>
>
> Thanks and Regards,
>
> Gourav Shenoy
>
>
>
> *From: *"Christie, Marcus Aaron" <machrist@iu.edu>
> *Reply-To: *"dev@airavata.apache.org" <dev@airavata.apache.org>
> *Date: *Friday, February 3, 2017 at 9:21 AM
>
>
> *To: *"dev@airavata.apache.org" <dev@airavata.apache.org>
> *Subject: *Re: [#Spring17-Airavata-Courses] : Distributed Workload
> Management for Airavata
>
>
>
> Vidya,
>
>
>
> I’m not sure how relevant it is, but it occurs to me that a microservice
> that executes jobs on a cloud requires very little in terms of resources to
> submit and monitor that job on the cloud. It doesn’t really matter if the
> job is a “big” or a “small” job.  So I’m not sure what heuristic makes
> sense regarding distributing work to these job execution microservices.
> Maybe a simple round robin approach would be sufficient.
>
>
>
> I think a job scheduling algorithm does make sense, however, for a higher
> level component, some sort of metascheduler that understands what resources
> are available on the cloud resources on which the jobs will be running.
> The metascheduler could create work for the job exection microservices to
> run on particular cloud resources in a way that optimizes for some metric
> (e.g., throughput).
>
>
>
> Thanks,
>
>
>
> Marcus
>
>
>
> On Feb 3, 2017, at 3:19 AM, Vidya Sagar Kalvakunta <vkalvaku@umail.iu.edu>
> wrote:
>
>
>
> Ajinkya,
>
>
>
> My scenario is for workload distribution among multiple instances of the
> same microservice.
>
>
>
> If a message broker needs to distribute the available jobs among multiple
> workers, the common approach would be to use round robin or a similar
> algorithm. This approach works best when all the workers are similar and
> the jobs are equal.
>
>
>
> So I think that a genetic or heuristic job scheduling algorithm, which is
> also aware of each of the worker's current state (CPU, RAM, No of Jobs
> processing) can more efficiently distribute the jobs. The workers can
> periodically ping the message broker with their current state info.
>
>
>
> The other advantage of using a customized algorithm is that it can
> be tweaked to use embedded routing, priority or other information in the
> job metadata to resolve all of the concerns raised by Amrutha viz message
> grouping, ordering, repeated messages, etc.
>
>
>
> We can even ensure data privacy, i.e if the workers are spread across
> multiple compute clusters say AWS and IU Big Red and we want to restrict
> certain sensitive jobs to be run only on Big Red.
>
>
>
> Some distributed job scheduling algorithms for cloud computing.
>
>    - http://www.ijimai.org/journal/sites/default/files/files/2013
>    /03/ijimai20132_18_pdf_62825.pdf
>    <http://www.ijimai.org/journal/sites/default/files/files/2013/03/ijimai20132_18_pdf_62825.pdf>
>    - https://arxiv.org/pdf/1404.5528.pdf
>
>
>
>
>
> Regards
>
> Vidya Sagar
>
>
>
> On Fri, Feb 3, 2017 at 1:38 AM, Kamat, Amruta Ravalnath <
> arkamat@indiana.edu> wrote:
>
> Hello all,
>
>
>
> Adding more information to the message based approach. Messaging is a key
> strategy employed in many distributed environments. Message queuing is
> ideally suited to performing asynchronous operations. A sender can post a
> message to a queue, but it does not have to wait while the message is
> retrieved and processed. A sender and receiver do not even have to be
> running concurrently.
>
>
>
> With message queuing there can be 2 possible scenarios:
>
>    1. ​Sending and receiving messages using a * single message queue.*
>    2. ​*Sharing a message queue* between many senders and receivers
>
> ​When a message is retrieved, it is removed from the queue. A message
> queue may also support message peeking. This mechanism can be useful if
> several receivers are retrieving messages from the same queue, but each
> receiver only wishes to handle specific messages. The receiver can examine
> the message it has peeked, and decide whether to retrieve the message
> (which removes it from the queue) or leave it on the queue for another
> receiver to handle.
>
>
>
> A few basic message queuing patterns are:
>
>    1. *One-way messaging*: The sender simply posts a message to the queue
>    in the expectation that a receiver will retrieve it and process it at some
>    point.
>    2. *Request/response messaging*: In this pattern a sender posts a
>    message to a queue and expects a response from the receiver. The sender can
>    resend if the message is not delivered. This pattern typically requires
>    some form of correlation to enable the sender to determine which response
>    message corresponds to which request sent to the receiver.
>    3. *Broadcast messaging*: In this pattern a sender posts a message to
>    a queue, and multiple receivers can read a copy of the message. This
>    pattern depends on the message queue being able to disseminate the same
>    message to multiple receivers. There is a queue to which the senders can
>    post messages that include metadata in the form of attributes. Each
>    receiver can create a subscription to the queue, specifying a filter that
>    examines the values of message attributes. Any messages posted to the
>    queue with attribute values that match the filter are automatically
>    forwarded to that subscription.
>
> A solution based on asynchronous messaging might need to address a number
> of concerns:
>
>
>
> *Message ordering, Message grouping: *Process messages either in the
> order they are posted or in a specific order based on priority. Also, there
> may be occasions when it is difficult to eliminate dependencies, and it may
> be necessary to group messages together so that they are all handled by the
> same receiver.
> *Idempotency: *Ideally the message processing logic in a receiver should
> be idempotent so that, if the work performed is repeated, this repetition
> does not change the state of the system.
> *Repeated messages: *Some message queuing systems implement duplicate
> message detection and removal based on message IDs
> *Poison messages: *A poison message is a message that cannot be handled,
> often because it is malformed or contains unexpected information.
> *Message expiration: *A message might have a limited lifetime, and if it
> is not processed within this period it might no longer be relevant and
> should be discarded.
> *Message scheduling: *A message might be temporarily embargoed and should
> not be processed until a specific date and time. The message should not be
> available to a receiver until this time.
>
>
> Thanks
>
> Amruta Kamat
>
> ------------------------------
>
> *From:* Shenoy, Gourav Ganesh <goshenoy@indiana.edu>
> *Sent:* Thursday, February 2, 2017 7:57 PM
> *To:* dev@airavata.apache.org
>
>
> *Subject:* Re: [#Spring17-Airavata-Courses] : Distributed Workload
> Management for Airavata
>
>
>
> Hello all,
>
>
>
> Amila, Sagar, thank you for the response and raising those concerns; and
> apologies because my email resonated the topic of workload management in
> terms of how micro-services communicate. As Ajinkya rightly mentioned,
> there exists some sort of correlation between micro-services communication
> and it’s impact on how that micro-service performs the work under those
> circumstances. The goal is to make sure we have maximum independence
> between micro-services, and investigate the workflow pattern in which these
> micro-services will operate such that we can find the right balance between
> availability & consistency. Again, from our preliminary analysis we can
> assert that these solutions may not be generic and the specific use-case
> will have a big decisive role.
>
>
>
> For starters, we are focusing on the following example – and I think this
> will clarify the doubts on what we are exactly trying to investigate about.
>
>
>
> *Our test example *
>
> Say we have the following 4 micro-services, which each perform a specific
> task as mentioned in the box.
>
>
>
> <image001.png>
>
>
>
>
>
> *A state-full pattern to distribute work*
>
> <image002.png>
>
>
>
> Here each communication between micro-services could be via RPC or
> Messaging (eg: RabbitMQ). Obvious disadvantage is that if any micro-service
> is down, then the system availability is at stake. In this test example, we
> can see that Microservice-A coordinates the work and maintains the state
> information.
>
>
>
> *A state-less pattern to distribute work*
>
>
>
> <image003.png>
>
>
>
> Another purely asynchronous approach would be to associate message-queues
> with each micro-service, where each micro-service performs it’s task,
> submits a request (message on bus) to the next micro-service, and continues
> to process more requests. This ensures more availability, and perhaps we
> might need to handle corner cases for failures such as message broker down,
> or message loss, etc.
>
>
>
> As mentioned, these are just a few proposals that we are planning to
> investigate via a prototype project. Inject corner cases/failures and try
> and find ways to handle these cases. I would love to hear more
> thoughts/questions/suggestions.
>
>
>
> Thanks and Regards,
>
> Gourav Shenoy
>
>
>
> *From: *Ajinkya Dhamnaskar <adhamnas@umail.iu.edu>
> *Reply-To: *"dev@airavata.apache.org" <dev@airavata.apache.org>
> *Date: *Thursday, February 2, 2017 at 2:22 AM
> *To: *"dev@airavata.apache.org" <dev@airavata.apache.org>
> *Subject: *Re: [#Spring17-Airavata-Courses] : Distributed Workload
> Management for Airavata
>
>
>
> Hello all,
>
>
>
> Just a heads up. Here the name Distributed workload management does not
> necessarily mean having different instances of a microservice and then
> distributing work among these instances.
>
>
>
> Apparently, the problem is how to make each microservice work
> independently with concrete distributed communication infrastructure. So,
> think of it as a workflow where each microservice does its part of work and
> communicates (how? yet to be decided) output. The next underlying
> microservice identifies and picks up that output and takes it further
> towards the final outcome, having said that, the crux here is, none of the
> miscoservices need to worry about other miscoservices in a pipeline.
>
>
>
> Vidya Sagar,
>
> I completely second your opinion of having stateless miscoservices, in
> fact that is the key. With stateless miscroservices it is difficult to
> guarantee consistency in a system but it solves the availability problem to
> some extent. I would be interested to understand what do you mean by "an
> intelligent job scheduling algorithm, which receives real-time updates from
> the microservices with their current state information".
>
>
>
> On Wed, Feb 1, 2017 at 11:48 PM, Vidya Sagar Kalvakunta <
> vkalvaku@umail.iu.edu> wrote:
>
>
>
> On Wed, Feb 1, 2017 at 2:37 PM, Amila Jayasekara <thejaka.amila@gmail.com>
> wrote:
>
> Hi Gourav,
>
>
>
> Sorry, I did not understand your question. Specifically I am having
> trouble relating "work load management" to options you suggest (RPC,
> message based etc.).
>
> So what exactly you mean by "workload management" ?
>
> What is work in this context ?
>
>
>
> Also, I did not understand what you meant by "the most efficient way".
> Efficient interms of what ? Are you looking at speed ?
>
>
>
> As per your suggestions, it seems you are trying to find a way to
> communicate between micro services. RPC might be troublesome if you need to
> communicate with processes separated from a firewall.
>
>
>
> Thanks
>
> -Thejaka
>
>
>
>
>
> On Wed, Feb 1, 2017 at 12:52 PM, Shenoy, Gourav Ganesh <
> goshenoy@indiana.edu> wrote:
>
> Hello dev, arch,
>
>
>
> As part of this Spring’17 Advanced Science Gateway Architecture course, we
> are working on trying to debate and find possible solutions to the issue of
> managing distributed workloads in Apache Airavata. This leads to the
> discussion of finding the most efficient way that different Airavata
> micro-services should communicate and distribute work, in such a way that:
>
> 1.       We maintain the ability to scale these micro-services whenever
> needed (autoscale perhaps?).
>
> 2.       Achieve fault tolerance.
>
> 3.       We can deploy these micro-services independently, or better in a
> containerized manner – keeping in mind the ability to use devops for
> deployment.
>
>
>
> As of now the options we are exploring are:
>
> 1.       RPC based communication
>
> 2.       Message based – either master-worker, or work-queue, etc
>
> 3.       A combination of both these approaches
>
>
>
> I am more inclined towards exploring the message based approach, but again
> there arises the possibility of handling limitations/corner cases of
> message broker such as downtimes (may be more). In my opinion, having
> asynchronous communication will help us achieve most of the above-mentioned
> points. Another debatable issue is making the micro-services implementation
> stateless, such that we do not have to pass the state information between
> micro-services.
>
>
>
> I would love to hear any thoughts/suggestions/comments on this topic and
> open up a discussion via this mail thread. If there is anything that I have
> missed which is relevant to this issue, please let me know.
>
>
>
> Thanks and Regards,
>
> Gourav Shenoy
>
>
>
>
>
> Hi Gourav,
>
>
>
> Correct me if I'm wrong, but I think this is a case of the job shop
> scheduling problem, as we may have 'n' jobs of varying processing times
> and memory requirements, and we have 'm' microservices with possibly
> different computing and memory capacities, and we are trying to minimize
> the makespan <https://en.wikipedia.org/wiki/Makespan>.
>
>
>
> For this use-case, I'm in favor a highly available and consistent message
> broker with an intelligent job scheduling algorithm, which receives
> real-time updates from the microservices with their current state
> information.
>
>
>
> As for the state vs stateless implementation, I think that question
> depends on the functionality of a particular microservice. In a broad
> sense, the stateless implementation should be preferred as it will scale
> better horizontally.
>
>
>
>
>
> Regards,
>
> Vidya Sagar
>
>
>
>
> --
>
> Vidya Sagar Kalvakunta | Graduate MS CS Student | IU School of Informatics
> and Computing | Indiana University Bloomington | (812) 691-5002
> <8126915002> | vkalvaku@iu.edu
>
>
>
>
>
> --
>
> Thanks and regards,
>
>
>
> Ajinkya Dhamnaskar
>
> Student ID : 0003469679
>
> Masters (CS)
>
> +1 (812) 369- 5416 <(812)%20369-5416>
>
>
>
>
>
> --
>
> Vidya Sagar Kalvakunta | Graduate MS CS Student | IU School of Informatics
> and Computing | Indiana University Bloomington | (812) 691-5002
> <8126915002> | vkalvaku@iu.edu
>
>
>
>
>
>
>
> --
>
> Thank you
> Supun Nakandala
> Dept. Computer Science and Engineering
> University of Moratuwa
>



-- 
Thank you
Supun Nakandala
Dept. Computer Science and Engineering
University of Moratuwa

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