Use predictive analytics to activate a pod

[jeffhollan]

A very “blue sky” feature but would be amazing to have KEDA look at historic data and patterns for deployments to try to predict when events may be coming in and scale proactively

[aminebizid]

Ok let's run this in kubeflow😁 Le mer. 15 mai 2019 à 16:24, Jeff Hollan <notifications@github.com> a écrit :

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A very “blue sky” feature but would be amazing to have KEDA look at historic data and patterns for deployments to try to predict when events may be coming in and scale proactively — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#197?email_source=notifications&email_token=AB3W6GMUBS3WEBK44PHG7K3PVQMLBA5CNFSM4HND3DV2YY3PNVWWK3TUL52HS4DFUVEXG43VMWVGG33NNVSW45C7NFSM4GT6CFWQ>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AB3W6GN2BHTCZZAVDY565S3PVQMLBANCNFSM4HND3DVQ> .

[GokGokalp]

A very “blue sky” feature but would be amazing to have KEDA look at historic data and patterns for deployments to try to predict when events may be coming in and scale proactively

Especially for RabbitMQ scaler, that would be great. If we can use some additional variables too such as "consumer utilization", "consumer ack" and "delivery" with the "queueLength" variable, maybe we can scale pods in a reactive way?

These days I'm playing with KEDA and testing it on our staging area. And KEDA currently scaling our pods based on the "queueLength" variable. It's great. But our some RabbitMQ consumers doing I/O based operations. If KEDA keeps continuing the scaling as a linear based on the "queueLength" variable, our other I/O services, that in the consumer, are starting to get in the bottleneck.

[turbaszek]

That's a really interesting idea. A few months ago I was doing research about best practices/patterns for autoscaling application and I stumbled on a research paper covering doing autoscaling using a predictive model.

KEDA look at historic data

@jeffhollan what historic data you had in mind? Also, should it be something maintained by KEDA itself or should this be a pluggable solution so anyone can use a custom model / algo?

[stale]

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