This adds (experimental) auto-scaling support for Ray clusters based on GCS load metrics. The auto-scaling algorithm is as follows:
Based on current (instantaneous) load information, we compute the approximate number of "used workers". This is based on the bottleneck resource, e.g. if 8/8 GPUs are used in a 8-node cluster but all the CPUs are idle, the number of used nodes is still counted as 8. This number can also be fractional.
We scale that number by 1 / target_utilization_fraction and round up to determine the target cluster size (subject to the max_workers constraint). The autoscaler control loop takes care of launching new nodes until the target cluster size is met.
When a node is idle for more than idle_timeout_minutes, we remove it from the cluster if that would not drop the cluster size below min_workers.
Note that we'll need to update the wheel in the example yaml file after this PR is merged.
