Hi,
I am a product manager on the App Engine team.

We indeed rolled out a new scheduler over the last months. This happened
gradually across all regions and all apps, and we closely monitored the
rollout using performance and billing metrics. Within a few weeks, we will
be publishing this change in the App Engine release notes, as well as new
documentation and a blog post.

Our tests show that it delivers better scaling performance and more
efficient resource consumption, and on average lower costs for our users.
But as you pointed out, for very small applications, it can happen that the
cost increases.

We introduced new automatic scaling parameters (in the automatic_scaling
section
<https://cloud.google.com/appengine/docs/standard/python/config/appref#scaling_elements>
of app.yaml or appengine-web.xml) to control the scaling behavior. I invite
you to tweaks those parameters for your application:
- "target_cpu_utilization": represents the CPU utilization ratio threshold
used to determine if the number of instances should be scaled up or down.
This is probably the parameter you should tweak to optimize between
performance and costs. It take values between 0 and 1, 0.6 being the
default. A higher value is likely to reduce the number of instances
scheduled, but might have an impact on your application tail latency.
- "target_throughput_utilization":  Specify a value from 0.5 to 0.95. Used
with max_concurrent_requests to specify when a new instance is started due
to concurrent requests. When the number of concurrent requests reaches a
value equal to max_concurrent_requests times target_throughput_utilization,
the scheduler starts a new instance.
- "max_instances": allows you to cap the total number of instances to be
scheduled. You can use this new parameter as a safeguard. This new
parameter is still rolling out and might not be available today.

We will publish very soon these paramters in our documentation
I hope this helps.

Steren
Product Manager
Google Cloud Platform

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