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From Amila Jayasekara <thejaka.am...@gmail.com>
Subject Re: [#Spring17-Airavata-Courses] : Distributed Workload Management for Airavata
Date Fri, 10 Feb 2017 19:24:27 GMT
I did not understand scheduling dependency very well, but why monitoring
dependency needs to be part of the DAG ?

Thanks
-Thejaka

On Fri, Feb 10, 2017 at 2:00 PM, Supun Nakandala <supun.nakandala@gmail.com>
wrote:

> Hi Amila,
>
> By monitoring and scheduling dependencies I meant the following.
>
> Monitoring dependencies: Eg. After a job is submitted to the remote host,
> the DAG execution has to wait until the job completes before proceeding to
> the next task. Currently, we handle this by monitoring emails. A separate
> daemon is checking for emails. So (I think) we can consider waiting for
> this email as having a monitoring dependency to the next task that has to
> be executed.
>
> Scheduling dependency: This is something that we currently don't have a
> use case but which I think soon become as a requirement. For example, when
> submitting jobs to Jetstream(which gives preference to interactive users)
> we have to wait until the system becomes vacant. Thus even though a user
> submits a job that job will have to wait until it is scheduled by an
> external system/call. So my idea was to consider these things as external
> scheduling dependencies. One might argue that scheduling sub-system also
> has to be part of Airavata. But I think we can separate scheduling
> sub-system and execution sub-system by having these scheduling dependencies.
>
> Hope this clarifies your question.
>
> On Fri, Feb 10, 2017 at 1:25 PM, Amila Jayasekara <thejaka.amila@gmail.com
> > wrote:
>
>> What are monitoring dependencies and scheduling dependencies in the
>> execution DAG ?
>>
>> Thanks
>> -Thejaka
>>
>> On Tue, Feb 7, 2017 at 5:47 PM, Supun Nakandala <
>> supun.nakandala@gmail.com> wrote:
>>
>>> 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
>>>
>>
>>
>
>
> --
> Thank you
> Supun Nakandala
> Dept. Computer Science and Engineering
> University of Moratuwa
>

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