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1. Support autoscaling Flink jobs deployed on Kubernetes via the Flink Kubernetes operator
2. Provide a default Flink job metric collector via the Flink metrics API
3. Provide a robust default scaling algorithm
   1. Ensure scaling yields effective usage of assigned task slots, i.e. prevents backpressure but does not overprovision
   2. Ramp up in case of any backlog to ensure it gets processed in a timely manner
   3. Minimize the number of scaling decisions to prevent costly rescale operation
4. Allow plugging in a custom scaling algorithm and metric collection code

Non-Goals (for now)

1. Vertical scaling via adjusting container resources or changing the number of task slots per TM
2. Autoscaling in standalone Flink session cluster deployment mode
3. Integrate with the k8s scheduler to reserve required resources for rescaling

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