[7.x] [ML] Autoscaling for machine learning (#59309) #65151
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This provides an autoscaling service integration for machine learning. The underlying logic is fairly straightforward with a couple of caveats: - When considering to scale up/down, ML will automatically translate between Node size and the memory that the node will potentially provide for ML after the scaling plan is implemented. - If knowledge of job sizes is out of date, we will do a best effort check for scaling up. But, if that cannot be determined with our current view of job memory, we attempt a refresh and return a no_scale event - We assume that if the auto memory percent calculation is being used, we treat all JVM sizes on the nodes the same. - For scale down, we keep our last scale down calculation time in memory. So, if master nodes are changed in between, we reset the scale down delay.
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Pinging @elastic/ml-core (:ml) |
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Backports the following commits to 7.x: