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From TzuChiao Yeh <su3g4284zo...@gmail.com>
Subject Re: New scheduling algorithm proposal.
Date Thu, 07 Jun 2018 12:55:50 GMT
Hi Dominic,

I really like your proposal! Thanks for your awesome presentation and
materials, help me a lot.

I have some opinions and questions here about the proposal and previous
discussions:

1. About kafka utilization:

First of all, bypassing invokers is a great idea, though this will lead to
lots of hard work on various runtime. The possibility on utilizing "hot"
containers and parallelism also looks well.

I'm not a kafka expert at all, but there are some external references
talking about large number of partitions. [1]

One question here -

>From my own investigation, current implementation on messaging (consumer)
uses batch (offset) to enhance performance. Since your proposed algorithm
assign each topic to an action, more accurately speaking, each partition to
a running container. It might drop the capability on using batch and I'm
not sure that how much overhead will take? Or there might be some advanced
design for balancing number of partition and offset?

2. Controller side:

I pay more interests on the future enhancement for extending more states
(from invokers) for controllers. I.e. the more accurate resource-aware
scheduling (i.e. memory-aware that Markus proposed in previous dev list),
package aware scheduling for package caching [2] and control the running
container's reserved time that you've mentioned ---> warm/hot containers
utilization.

At the first time, I'm confused of the "autonomous" word maps to your
algorithm. I think the scheduling logic still sit in controller side (for
checking lags, limits and so on).
Seems like the proposed algorithm will not drop out the current sharding
loadbalancer, can you share more experience or plans on integrating these
in controller side?

[1]
https://www.confluent.io/blog/how-to-choose-the-number-of-topicspartitions-in-a-kafka-cluster/
[2] https://dl.acm.org/citation.cfm?id=3186294&dl=ACM&coll=DL

Thanks,
TzuChiao

Dominic Kim <style9595@gmail.com> 於 2018年6月7日 週四 上午1:08寫道:

> Sorry.
> Let me share Kafka benchmark results again.
>
> | # of topics  |   Kafka TPS |
> |    50  |   34,488 |
> |  100  |  34,502 |
> |  200  |   31,781 |
> |  500  |   30,324 |
> | 1000  |  30,855 |
>
> Best regards
> Dominic
>
>
> 2018-06-07 2:04 GMT+09:00 Dominic Kim <style9595@gmail.com>:
>
> > Sorry for the late response Tyson.
> >
> > Let me first answer your second question.
> > Vuser is just the number of threads to send the requests.
> > Each Vusers randomly picked the namespace and send the request using REST
> > API.
> > So they are independent of the number of namespaces.
> >
> > And regarding performance degradation on the number of users, I think it
> > works a little bit differently.
> > Even though I have only 2 users(namespaces), if their homeInvoker is
> same,
> > TPS become very less.
> > So it is a matter of the number of actions whose homeInvoker are same
> > though more the number of users than the number of containers can harm
> the
> > performance.
> > This is because controller should send those actions to the same invoker
> > even though there are other idle invokers.
> > In my proposal, controllers can schedule activation to any invokers so it
> > does not happen.
> >
> >
> > And regarding the issue about the sheer number of Kafka topics, let me
> > share my idea.
> >
> > 1. Data size is not changed.
> >
> > If we have 1000 activation requests, they will be spread among invoker
> > topics. Let's say we have 10 invokers, then ideally each topic will have
> > 100 messages.
> > In my proposal, if I have 10 actions, each topic will have 100 messages
> as
> > well.
> > Surely there will be more number of actions than the number of invokers,
> > data will be spread to more topics, but data size is unchanged.
> >
> > 2. Data size depends on the number of active actions.
> >
> > For example, if we have one million actions, in turn, one million topics
> > in Kafka.
> > If only half of them are executed, then there will be data only for half
> > of them.
> > For rest half of topics, there will be no data and they won't affect the
> > performance.
> >
> > 3. Things to concern.
> >
> > Let me describe what happens if there are more number of Kafka topics.
> >
> > Let's say there are 3 invokers with 5 activations each in the current
> > implementation, then it would look like this.
> >
> > invoker0: 0 1 2 3 4 5 (5 messages) -> consumer0
> > invoker1: 0 1 2 3 4 5 -> consumer1
> > invoekr2: 0 1 2 3 4 5 -> consumer2
> >
> > Now If I have 15 actions with 15 topics in my proposal.
> >
> > action0: 0  -> consumer0
> > action1: 0  -> consumer1
> > action2: 0  -> consumer2
> > action3: 0  -> consumer3
> > .
> > .
> > .
> > action14: 0  -> consumer14
> >
> > Kafka utilizes page cache to maximize the performance.
> > Since the size of data is not changed, data kept in page cache is also
> not
> > changed.
> > But the number of parallel access to data is increased. I think it might
> > be some overhead.
> >
> > That's the reason why I performed benchmark with multiple topics.
> >
> > # of topics
> >
> > Kafka TPS
> >
> > 50
> >
> > 34,488
> >
> > 100
> >
> > 34,502
> >
> > 200
> >
> > 31,781
> >
> > 500
> >
> > 30,324
> >
> > 1,000
> >
> > 30,855
> >
> > As you can see there are some overheads from increased parallel access to
> > data.
> > Here we can see about 4,000 TPS degraded as the number of topics
> increased.
> >
> > But we can still support 30K TPS with 1000 topics using 3 Kafka nodes.
> > If we need more TPS we can just add more nodes.
> > Since Kafka can horizontally scale-out, if we add 3 more servers, I
> expect
> > we can get 60K TPS.
> >
> > Partitions in Kafka are evenly distributed among nodes in a cluster.
> > Each nodes becomes a leader for each partition. If we have 100 partitions
> > with 4 Kafka nodes, ideally each Kafka nodes will be a leader for 25
> > partitions.
> > Then consumers can directly read messages from different partition
> leaders.
> > This is why Kafka can horizontally scale-out.
> >
> > Even though the number of topics is increased, if we add more Kafka
> nodes,
> > the number of partitions which is managed by one Kafka node would be
> > unchanged.
> > So if we can support 30K TPS with 1000 topics using 3 nodes, then we can
> > still get 60K TPS with 2000 topics using 6 nodes.
> > Similarly, if we have enough Kafka nodes, the number of partitions in one
> > Kafka nodes will be same though we have one million concurrent
> invocations.
> >
> > This is what I am thinking.
> > If I miss anything, kindly let me know.
> >
> > Thanks
> > Regards
> > Dominic.
> >
> >
> >
> >
> >
> > 2018-05-26 13:22 GMT+09:00 Tyson Norris <tnorris@adobe.com.invalid>:
> >
> >> Hi Dominic -
> >>
> >> Thanks for the detailed presentation! It is helpful to go through to
> >> understand your points - well done.
> >>
> >>
> >> A couple of comments:
> >>
> >> - I'm not sure how unbounded topic (and partition) growth will be
> >> handled, realistically. AFAIK, these are not free of resource
> consumption
> >> at the client or.
> >>
> >> - In your test it looks like you have 990 vusers example (pdf page 121)
> -
> >> are these using different namespaces? I ask because I think the current
> >> impl isolates the container per namespace, so if you are limited to 180
> >> containers, I can see how there will be container removal/restarts in
> the
> >> case where the number of users greatly outnumbers the number of
> containers
> >> - I'm not sure if the test behaves this way, or your "New
> implementation"
> >> behaves similar? (does a container get reused for many different
> >> namespaces?)
> >>
> >>
> >> I'm interested to know if there are any kafka experts here that can
> >> provide more comments on the topics/partition handling question? I will
> >> also ask for some additional feedback from other colleagues.
> >>
> >>
> >> I will gather some more comments to share, but wanted to start off with
> >> these. Will continue next week after the long (US holiday) weekend.
> >>
> >>
> >> Thanks
> >>
> >> Tyson
> >>
> >>
> >> ________________________________
> >> From: Dominic Kim <style9595@gmail.com>
> >> Sent: Friday, May 25, 2018 1:58:55 AM
> >> To: dev@openwhisk.apache.org
> >> Subject: Re: New scheduling algorithm proposal.
> >>
> >> Dear Whiskers.
> >>
> >> I uploaded the material that I used to give a speech at last biweekly
> >> meeting.
> >>
> >> https://na01.safelinks.protection.outlook.com/?url=https%3A%
> >> 2F%2Fcwiki.apache.org%2Fconfluence%2Fdisplay%2FOPENW
> >> HISK%2FAutonomous%2BContainer%2BScheduling&data=02%7C01%
> >> 7Ctnorris%40adobe.com%7C0c84bff555fb4990142708d5c21dc5e8%7Cf
> >> a7b1b5a7b34438794aed2c178decee1%7C0%7C0%7C636628355491109416
> >> &sdata=qppUpTRm%2BkTR5EueiLg7Ix6xiGWlzk5WDn3DxJv032w%3D&reserved=0
> >>
> >> This document mainly describes following things:
> >>
> >> 1. Current implementation details
> >> 2. Potential issues with current implementation
> >> 3. New scheduling algorithm proposal: Autonomous Container Scheduling
> >> 4. Review previous issues with new scheduling algorithm
> >> 5. Pros and cons of new scheduling algorithm
> >> 6. Performance evaluation with prototyping
> >>
> >>
> >> I hope this is helpful for many Whiskers to understand the issues and
> new
> >> proposal.
> >>
> >> Thanks
> >> Best regards
> >> Dominic
> >>
> >>
> >> 2018-05-18 18:44 GMT+09:00 Dominic Kim <style9595@gmail.com>:
> >>
> >> > Hi Tyson
> >> > Thank you for comments.
> >> >
> >> > First, total data size in Kafka would not be changed, since the number
> >> of
> >> > activation requests will be same though we activate more topics.
> >> > Same amount of data will be just split into different topics, so there
> >> > would be no need for more disk space in Kafka.
> >> > But it will increase parallel processing at Kafka layer, more number
> of
> >> > consumers will access to Kafka at the same time.
> >> >
> >> > The reason why active/inactive is meaningful in the scene is, the size
> >> of
> >> > parallel processing will be dependent on it.
> >> > Though we have 100k and more topics(actions), if only 10 actions are
> >> being
> >> > invoked, there will be only 10 parallel processing.
> >> > The number of inactive topics does not affect the performance of
> active
> >> > consumers.
> >> > If we really need to support 100k parallel action invocation, surely
> we
> >> > need more nodes to handle them not only for just Kafka.
> >> > Kafka can horizontally scale out and number of active topics at some
> >> point
> >> > will always be lesser than the total number of topics,  Based on my
> >> > benchmark results, I expect it is enough to take with scale-out of
> >> Kafka.
> >> >
> >> > Regarding topic cleanup, we don't need to clean them up by ourselves,
> >> > Kafka will clean up expired data based on retention configuration.
> >> > So if the topic is no more activated(the action is no more invoked),
> >> there
> >> > would be no actual data though the topic exists.
> >> > And as I said, even if data exists for some topics, they won't affect
> >> > other active consumers if they are not activated.
> >> >
> >> > Regarding concurrent activation PR, it is very worthy change. And I
> >> > recognize it is orthogonal to my PR.
> >> > It will not only alleviate current performance issue but can be used
> >> with
> >> > my changes as well.
> >> >
> >> > In current logics, controller schedule activations based on hash, your
> >> PR
> >> > would be much effective if some changes are made on scheduling logic.
> >> >
> >> > Regarding bypassing kafka, I am inclined to use Kafka because it can
> act
> >> > as a kind of buffer.
> >> > If some activations are not processed due to some reasons such as lack
> >> of
> >> > resources or invoker failure and so on, Kafka can keep them up for
> some
> >> > times and guarantee `at most once` invocation though invocation might
> >> be a
> >> > bit delayed.
> >> >
> >> > With regard to combined approach, I think that is great idea.
> >> > For that, container states should be shared among invokers and they
> can
> >> > send activation request to any containers(local, or remote).
> >> > As so invokers will utilize warmed resources which are not located in
> >> its
> >> > local.
> >> >
> >> > But it will also introduce some synchronization issue among
> controllers
> >> > and invokers or it needs segregation between resources based
> scheduling
> >> at
> >> > controller and real invocation.
> >> > In the earlier case, since controller will schedule activations based
> on
> >> > resources status, it is required to synchronize them in realtime.
> >> > Invokers can send requests to any remote containers, there will be
> >> > mismatch in resource status between controllers and invokers.
> >> >
> >> > In the later case, controller should be able to send requests to any
> >> > invokers then invoker will schedule the activations.
> >> > In this case also, invokers need to synchronize their container status
> >> > among them.
> >> >
> >> > Under the situation all invokers have same resources status, if two
> >> > invokers received same action invocation requests, it's not easy to
> >> control
> >> > the traffic among them, because they will schedule requests to same
> >> > containers. And if we take similar approach with what you suggested,
> to
> >> > send intent to use the containers first, it will introduce increasing
> >> > latency overhead as more and more invokers joined the cluster.
> >> > I couldn't find any good way to handle this yet. And this is why I
> >> > proposed autonomous containerProxy to enable location free scheduling.
> >> >
> >> > Finally regarding SPI, yes you are correct, ContainerProxy is highly
> >> > dependent on ContainerPool, I will update my PR as you guided.
> >> >
> >> > Thanks
> >> > Regards
> >> > Dominic.
> >> >
> >> >
> >> > 2018-05-18 2:22 GMT+09:00 Tyson Norris <tnorris@adobe.com.invalid>:
> >> >
> >> >> Hi Dominic -
> >> >>
> >> >> I share similar concerns about an unbounded number of topics, despite
> >> >> testing with 10k topics. I’m not sure a topic being considered active
> >> vs
> >> >> inactive makes a difference from broker/consumer perspective? I think
> >> there
> >> >> would minimally have to be some topic cleanup that happens, and I’m
> not
> >> >> sure the impact of deleting topics in bulk will have on the system
> >> either.
> >> >>
> >> >> A couple of tangent notes related to container reuse to improve
> >> >> performance:
> >> >> - I’m putting together the concurrent activation PR[1] (to allow
> reuse
> >> of
> >> >> an warm container for multiple activations concurrently); this can
> >> improve
> >> >> throughput for those actions that can tolerate it (FYI per-action
> >> config is
> >> >> not implemented yet). It suffers from similar inaccuracy of kafka
> >> message
> >> >> ingestion at invoker “how many messages should I read”? But I think
> we
> >> can
> >> >> tune this a bit by adding some intelligence to Invoker/MessageFeed
> >> like “if
> >> >> I never see ContainerPool indicate it is busy, read more next time”
-
> >> that
> >> >> is, allow ContainerPool to backpressure MessageFeed based on ability
> to
> >> >> consume, and not (as today) strictly on consume+process.
> >> >>
> >> >> - Another variant we are investigating is putting a ContainerPool
> into
> >> >> Controller. This will prevent container reuse across controllers
> >> (bad!),
> >> >> but will bypass kafka(good!). I think this will be plausible for
> >> actions
> >> >> that support concurrency, and may be useful for anything that runs
as
> >> >> blocking to improve a few ms of latency, but I’m not sure of all
the
> >> >> ramifications yet.
> >> >>
> >> >>
> >> >> Another (more far out) approach combines some of these is changing
> the
> >> >> “scheduling” concept to be more resource reservation and garbage
> >> >> collection. Specifically that the ContainerPool could be a
> combination
> >> of
> >> >> self-managed resources AND remote managed resources. If no proper
> >> (warm)
> >> >> container exists locally or remotely, a self-managed one is created,
> >> and
> >> >> advertised. Other ContainerPool instances can leverage the remote
> >> resources
> >> >> (containers). To pause or remove a container requires advertising
> >> intent to
> >> >> change state, and giving clients time to veto. So there is some added
> >> >> complication in the start/reserver/pause/rm container lifecycle, but
> >> the
> >> >> case for reuse is maximized in best case scenario (concurrency
> tolerant
> >> >> actions) and concurrency intolerant actions have a chance to
> leverage a
> >> >> broader pool of containers (iff the ability to reserve a shared
> >> available
> >> >> container is fast enough, compared to starting a new cold one). There
> >> is a
> >> >> lot wrapped in there (how are resources advertised, what are the new
> >> states
> >> >> of lifecycle, etc), so take this idea with a grain of salt.
> >> >>
> >> >>
> >> >> Specific to your PR: do you need an SPI for ContainerProxy? Or can
it
> >> >> just be designated by the ContainerPool impl to use a specific
> >> >> ContainerProxy variant? I think these are now and will continue to
be
> >> tied
> >> >> closely together, so would manage them as a single SPI.
> >> >>
> >> >> Thanks
> >> >> Tyson
> >> >>
> >> >> [1] https://na01.safelinks.protection.outlook.com/?url=https%3A%
> >> 2F%2Fgithub.com%2Fapache%2Fincubator-openwhisk%2Fpull%
> >> 2F2795&data=02%7C01%7Ctnorris%40adobe.com%7C0c84bff555fb4990
> >> 142708d5c21dc5e8%7Cfa7b1b5a7b34438794aed2c178decee1%7C0%7C0%
> >> 7C636628355491109416&sdata=WsPtzIMCpQsGQDML8n1Jm%2BbsBj8BWhw
> >> IJ%2B9wiQjkQOE%3D&reserved=0<http
> >> >> s://github.com/apache/incubator-openwhisk/pull/2795#pullrequ
> >> >> estreview-115830270>
> >> >>
> >> >> On May 16, 2018, at 10:42 PM, Dominic Kim <style9595@gmail.com
> <mailto:
> >> st
> >> >> yle9595@gmail.com>> wrote:
> >> >>
> >> >> Dear all.
> >> >>
> >> >> Does anyone have any comments on this?
> >> >> Any comments or opinions would be greatly welcome : )
> >> >>
> >> >> I think we need around following changes to take this in.
> >> >>
> >> >> 1. SPI supports for ContainerProxy and ContainerPool ( I already
> >> opened PR
> >> >> for this:
> https://na01.safelinks.protection.outlook.com/?url=https%3A%
> >> >> 2F%2Fgithub.com%2Fapache%2Fincubator-openwhisk%2Fpull%
> >> >> 2F3663&data=02%7C01%7Ctnorris%40adobe.com%7Cb2dd7c5686bc466f
> >> >> 92f708d5bbb8f43d%7Cfa7b1b5a7b34438794aed2c178decee1%7C0%7C0%
> >> >> 7C636621325405375234&sdata=KYHyOGosc%2BqMIFLVVRO2gYCYZRegfQL
> >> >> l%2BfGjSvtGI9k%3D&reserved=0)
> >> >> 2. SPI supports for Throttling/Entitlement logics
> >> >> 3. New loadbalancer
> >> >> 4. New ContainerPool and ContainerProxy
> >> >> 5. New notions for limit and logics to hanlde more fine-grained
> >> limit.(It
> >> >> should be able to coexist with existing limit)
> >> >>
> >> >> If there's no any comments, I will open a PR based on it one by one.
> >> >> Then it would be better for us to discuss on this because we can
> >> directly
> >> >> discuss over basic implementation.
> >> >>
> >> >> Thanks
> >> >> Regards
> >> >> Dominic.
> >> >>
> >> >>
> >> >>
> >> >> 2018-05-09 11:42 GMT+09:00 Dominic Kim <style9595@gmail.com<mailto:
> st
> >> >> yle9595@gmail.com>>:
> >> >>
> >> >> One more thing to clarify is invoker parallelism.
> >> >>
> >> >> ## Invoker coordinates all requests.
> >> >>
> >> >> I tend to disagree with the "cannot take advantage of parallel
> >> >> processing" bit. Everything in the invoker is parallelized after
> >> updating
> >> >> its central state (which should take a **very** short amount of time
> >> >> relative to actual action runtime). It is not really optimized to
> >> scale to
> >> >> a lot of containers *yet*.
> >> >>
> >> >> More precisely, I think it is related to Kafka consumer rather than
> >> >> invoker. Invoker logic can run in parallel. But `MessageFeed` seems
> >> not.
> >> >> Once outstanding message size reaches max size, it will wait for
> >> messages
> >> >> processed. If few activation messages for an action are not properly
> >> >> handled, `MessageFeed` does not consume more message from Kafka(until
> >> any
> >> >> messages are processed).
> >> >> So subsequent messages for other actions cannot be fetched or delayed
> >> due
> >> >> to unprocessed messages. This is why I mentioned invoker
> parallelism. I
> >> >> think I should rephrase it as `MessageFeed` parallelism.
> >> >>
> >> >> As you know partition is unit of parallelism in Kafka. If we have
> >> multiple
> >> >> partitions for activation topic, we can setup multiple consumers and
> it
> >> >> will enable parallel processing for Kafka messages as well.
> >> >> Since logics in invoker can already run in parallel, with this
> change,
> >> we
> >> >> can process messages entirely in parallel.
> >> >>
> >> >> In my proposal, I split activation messages from container
> coordination
> >> >> message(action parallelism), assign more partition for activation
> >> >> messages(in-topic parallelism) and enable parallel processing with
> >> >> multiple
> >> >> consumers(containers).
> >> >>
> >> >>
> >> >> Thanks
> >> >> Regards
> >> >> Dominic.
> >> >>
> >> >>
> >> >>
> >> >>
> >> >>
> >> >> 2018-05-08 19:34 GMT+09:00 Dominic Kim <style9595@gmail.com<mailto:
> st
> >> >> yle9595@gmail.com>>:
> >> >>
> >> >> Thank you for the response Markus and Christian.
> >> >>
> >> >> Yes I agree that we need to discuss this proposal in abstract way
> >> instead
> >> >> in conjunction it with any specific technology because we can take
> >> better
> >> >> software stack if possible.
> >> >> Let me answer your questions line by line.
> >> >>
> >> >>
> >> >> ## Does not wait for previous run.
> >> >>
> >> >> Yes it is valid thoughts. If we keep cumulating requests in the
> queue,
> >> >> latency can be spiky especially in case execution time of action is
> >> huge.
> >> >> So if we want to take this in, we need to find proper way to balance
> >> >> creating more containers for latency and making existing containers
> >> handle
> >> >> requests.
> >> >>
> >> >>
> >> >> ## Not able to accurately control concurrent invocation.
> >> >>
> >> >> Ok I originally thought this is related to concurrent containers
> rather
> >> >> than concurrent activations.
> >> >> But I am still inclined to concurrent containers approach.
> >> >> In current logic, it is dependent on factors other than real
> concurrent
> >> >> invocations.
> >> >> If RTT between controllers and invokers becomes high for some
> reasons,
> >> >> controller will reject new requests though invokers are actually
> idle.
> >> >>
> >> >> ## TPS is not deterministic.
> >> >>
> >> >> I meant not deterministic TPS for just one user rather I meant
> >> >> system-wide deterministic TPS.
> >> >> Surely TPS can vary when heterogenous actions(which have different
> >> >> execution time) are invoked.
> >> >> But currently it's not easy to figure out what the TPS is with only
1
> >> >> kind of action because it is changed based on not only heterogeneity
> of
> >> >> actions but the number of users and namespaces.
> >> >>
> >> >> I think at least we need to be able to have this kind of official
> spec:
> >> >> In case actions with 20 ms execution time are invoked, our system TPS
> >> is
> >> >> 20,000 TPS(no matter how many users or namespaces are used).
> >> >>
> >> >>
> >> >> Your understanding about my proposal is perfectly correct.
> >> >> Small thing to add is, controller sends `ContainerCreation` request
> >> based
> >> >> on processing speed of containers rather than availability of
> existing
> >> >> containers.
> >> >>
> >> >> BTW, regarding your concern about Kafka topic, I think we may be fine
> >> >> because,
> >> >> the number of topics will be unbounded, but the number of active
> topics
> >> >> will be bounded.
> >> >>
> >> >> If we take this approach, it is mandatory to limit retention bytes
> and
> >> >> duration for each topics.
> >> >> So the number of active topics is limited and actual data in them are
> >> >> also limited, so I think that would be fine.
> >> >>
> >> >> But it is necessary to have optimal configurations for retention and
> >> many
> >> >> benchmark to confirm this.
> >> >>
> >> >>
> >> >> And I didn't get the meaning of eventual consistency of consumer lag.
> >> >> You meant that is eventual consistent because it changes very quickly
> >> >> even within one second?
> >> >>
> >> >>
> >> >> Thanks
> >> >> Regards
> >> >> Dominic
> >> >>
> >> >>
> >> >>
> >> >> 2018-05-08 17:25 GMT+09:00 Markus Thoemmes <
> markus.thoemmes@de.ibm.com
> >> <ma
> >> >> ilto:markus.thoemmes@de.ibm.com>>:
> >> >>
> >> >> Hey Dominic,
> >> >>
> >> >> Thank you for the very detailed writeup. Since there is a lot in
> here,
> >> >> please allow me to rephrase some of your proposals to see if I
> >> understood
> >> >> correctly. I'll go through point-by-point to try to keep it close to
> >> your
> >> >> proposal.
> >> >>
> >> >> **Note:** This is a result of an extensive discussion of Christian
> >> >> Bickel (@cbickel) and myself on this proposal. I used "I" throughout
> >> the
> >> >> writeup for easier readability, but all of it can be read as "we".
> >> >>
> >> >> # Issues:
> >> >>
> >> >> ## Interventions of actions.
> >> >>
> >> >> That's a valid concern when using today's loadbalancer. This is
> >> >> noisy-neighbor behavior that can happen today under the circumstances
> >> you
> >> >> describe.
> >> >>
> >> >> ## Does not wait for previous run.
> >> >>
> >> >> True as well today. The algorithms used until today value correctness
> >> >> over performance. You're right, that you could track the expected
> queue
> >> >> occupation and schedule accordingly. That does have its own risks
> >> though
> >> >> (what if your action has very spiky latency behavior?).
> >> >>
> >> >> I'd generally propose to break this out into a seperate discussion.
> It
> >> >> doesn't really correlate to the other points, WDYT?
> >> >>
> >> >> ## Invoker coordinates all requests.
> >> >>
> >> >> I tend to disagree with the "cannot take advantage of parallel
> >> >> processing" bit. Everything in the invoker is parallelized after
> >> updating
> >> >> its central state (which should take a **very** short amount of time
> >> >> relative to actual action runtime). It is not really optimized to
> >> scale to
> >> >> a lot of containers *yet*.
> >> >>
> >> >> ## Not able to accurately control concurrent invocation.
> >> >>
> >> >> Well, the limits are "concurrent actions in the system". You should
> be
> >> >> able to get 5 activations on the queue with today's mechanism. You
> >> should
> >> >> get as many containers as needed to handle your load. For very
> >> >> short-running actions, you might not need N containers to handle N
> >> >> messages
> >> >> in the queue.
> >> >>
> >> >> ## TPS is not deterministic.
> >> >>
> >> >> I'm wondering: Have TPS been deterministic for just one user? I'd
> argue
> >> >> that this is a valid metric on its own kind. I agree that these
> numbers
> >> >> can
> >> >> drop significantly under heterogeneous load.
> >> >>
> >> >> # Proposal:
> >> >>
> >> >> I'll try to rephrase and add some bits of abstraction here and there
> to
> >> >> see if I understood this correctly:
> >> >>
> >> >> The controller should schedule based on individual actions. It should
> >> >> not send those to an arbitrary invoker but rather to something that
> >> >> identifies those actions themselves (a kafka topic in your example).
> >> I'll
> >> >> call this *PerActionContainerPool*. Those calls from the controller
> >> will
> >> >> be
> >> >> handled by each *ContainerProxy* directly rather than being threaded
> >> >> through another "centralized" component (the invoker). The
> >> >> *ContainerProxy*
> >> >> is responsible for handling the "aftermath": Writing activation
> >> records,
> >> >> collecting logs etc (like today).
> >> >>
> >> >> Iff the controller thinks that the existing containers cannot sustain
> >> >> the load (i.e. if all containers are currently in use), it advises
a
> >> >> *ContainerCreationSystem* (all invokers combined in your case) to
> >> create a
> >> >> new container. This container will be added to the
> >> >> *PerActionContainerPool*.
> >> >>
> >> >> The invoker in your proposal has no scheduling logic at all (which
is
> >> >> sound with the issues lined out above) other than container creation
> >> >> itself.
> >> >>
> >> >> # Conclusion:
> >> >>
> >> >> I like the proposal in the abstract way I've tried to phrase above.
> It
> >> >> indeed amplifies warm-container usage and in general should be
> >> superior to
> >> >> the more statistical approach of today's loadbalancer.
> >> >>
> >> >> I think we should discuss this proposal in an abstract,
> >> >> non-technology-bound way. I do think that having so many kafka topics
> >> >> including all the rebalancing needed can become an issue, especially
> >> >> because the sheer number of kafka topics is unbounded. I also think
> >> that
> >> >> the consumer lag is subject to eventual consistency and depending on
> >> how
> >> >> eventual that is it can turn into queueing in your system, even
> though
> >> >> that
> >> >> wouldn't be necessary from a capacity perspective.
> >> >>
> >> >> I don't want to ditch the proposal because of those concerns though!
> >> >>
> >> >> As I said: The proposal itself makes a lot of sense and I like it a
> >> lot!
> >> >> Let's not trap ourselves in the technology used today though. You're
> >> >> proposing a major restructuring so we might as well think more
> >> >> green-fieldy. WDYT?
> >> >>
> >> >> Cheers,
> >> >> Christian and Markus
> >> >>
> >> >>
> >> >>
> >> >>
> >> >>
> >> >>
> >> >
> >>
> >
> >
>

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