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knix-microfunctions / knix

Serverless computing platform with process-based lightweight function execution and container-based application isolation. Works in Knative and bare metal/VM environments.
https://knix.io
Apache License 2.0
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Make the application container resources in Kubernetes user-decidable #54

Open iakkus opened 4 years ago

iakkus commented 4 years ago

Currently, the resources assigned to an application container in Kubernetes are static and defined in deploy/helm/microfunctions/values.yaml. I think these values should be configurable, so that during the workflow deployment the user can pick how much resources should be assigned to her application's containers.

iakkus commented 4 years ago

@manuelstein , @paarijaat , @ruichuan, any thoughts?

ruichuan commented 4 years ago

In my opinion, this values.yaml is the place where one could make changes to better adapt to its infrastructure. Are you suggesting we should have a settings file like what we do for ansible installation? I feel that might end up with a similar file as the current values.yaml ;)

On Wed, Jul 15, 2020 at 2:11 PM Istemi Ekin Akkus notifications@github.com wrote:

@manuelstein https://github.com/manuelstein , @paarijaat https://github.com/paarijaat , @ruichuan https://github.com/ruichuan, any thoughts?

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iakkus commented 4 years ago

No, I mean for the end users to configure how their workflows could be deployed (i.e., with what kind of container resources). This would be similar to the way AWS Lambda users would pick their configuration with different RAM.

manuelstein commented 4 years ago

The limits of a container would depend on the functions that are deployed in it and the targeted level of concurrency that the container should be able to handle. Based on this, scaling thresholds can be configured. If the concurrency level is low, scaling might start to scale-out early, and response times might suffer during the scale-out. If provided resources allow high concurrency, we accept over-provisioning for better response times.

Should we allow the user to provide an initial resource estimate per function instance? Could we report/collect the CPU time and memory use after a function invocation has completed?

ksatzke commented 4 years ago

Hi Manuel, I think whenever a service provide in the future is interested in billing for KNIX FaaS-like services he will need this type of information - same as for other services like object storer to. I share your view on allowing a user to provide an estimate on memory usage, but what will happen if the estimated memory consumption is wrong/exceeded? In case of AWS Lambda e.g. such a function could either simply fail, or not terminate within the container lifetime constraints, do we want e.g. give an extra memory allowance just in case?

Best regards, Klaus

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Von: Manuel Stein notifications@github.com Gesendet: Wednesday, July 15, 2020 7:58:37 PM An: knix-microfunctions/knix knix@noreply.github.com Cc: Subscribed subscribed@noreply.github.com Betreff: Re: [knix-microfunctions/knix] Make the application container resources in Kubernetes user-decidable (#54)

The limits of a container would depend on the functions that are deployed in it and the targeted level of concurrency that the container should be able to handle. Based on this, scaling thresholds can be configured. If the concurrency level is low, scaling might start to scale-out early, and response times might suffer during the scale-out. If provided resources allow high concurrency, we accept over-provisioning for better response times.

Should we allow the user to provide an initial resource estimate per function instance? Could we report/collect the CPU time and memory use after a function invocation has completed?

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ruichuan commented 4 years ago

Oh, my bad -- missed the "user configuration" part... Then, it's generally a good thing to have. The question is that, whether a user really knows how much resource its function/workflow needs, or should knix profile the resource usage of a function/workflow for the user?

On Wed, Jul 15, 2020 at 5:13 PM Istemi Ekin Akkus notifications@github.com wrote:

No, I mean for the end users to configure how their workflows could be deployed (i.e., with what kind of container resources). This would be similar to the way AWS Lambda users would pick their configuration with different RAM.

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub, or unsubscribe.

iakkus commented 4 years ago

Yes, that is true. I forgot to consider that the users shouldn't have to reason about the resource usage of their functions.

I think it would be good if we could measure each function instance's CPU and memory usage. It would help to adjust the scaling policy for the kubernetes setup as well as determining the scaling policy for the bare-metal case (which is something we should also work on).

We can start gathering statistics about already deployed functions. When we need to scale out a deployed workflow, we can adjust the resources accordingly for the new scaled instance (e.g., this sandbox had 1CPU and 1GB RAM, and i am now scaling it up for 3rd time in the last 5 minutes, so maybe I'll just give it more CPU and RAM this time). Similarly, if this function is used in another workflow that is being deployed, we can also consider that information.

Then during deployment, we could pick the resources of the sandbox according to some heuristics with regards the functions and states utilized in the workflow. For example, if the workflow contains a 'Parallel' or 'Map' state, we pick a sandbox flavour with more CPU and RAM.

That said, how about we have a few different flavours of sandboxes with different RAM and CPU configurations (e.g., [1 CPU, 1 GB], [1 CPU, 2 GB], [2 CPUs, 4GB]) to choose from? Or we could just have dynamic values in the deployWorkflow procedure.

My original point was that these values are currently fixed and determined at installation time, and not adjustable afterwards during runtime, so we should try to make it more dynamic.

iakkus commented 3 years ago

See commit f347d3eb986f4a340340fe149e017a220060731e for the management functionality that allows this configuration for kubernetes environments.

Once we have the corresponding changes in the following, we can close this issue.