This autoscaler is created to scale the cluster used to serve the stateful Jupyter notebook web application. It is designed to cause no disruption to user and (in the future) support various cloud service providers.

Settings for the autoscaler should be loaded through environment variables. They are defined as follows:

MAX_UTILIZATION, MIN_UTILIZATION, OPTIMAL_UTILIZATION refer to the minimum, maximum and optimal utilization rate. They are by default set to 0.85, 0.65, 0.75.

MIN_NODES, MAX_NODES refer to an additional bound of the number of nodes. They are by default set to 3 and 72.

PREEMPTIBLE_LABELS are a list of label keys in Kubernetes. Pods with these label keys are dynamic, expected to be created and deleted when the workload changes. Using ':' as the delimiter, the list should have such a format: jupyter:student:notebook. Despite the name, in the current version these pods will not be preempted in any case. This list is by default empty.

OMIT_LABELS, OMIT_NAMESPACES are lists of label keys and namespace names. Pods with the given label keys or in the given namespaces will not be taken into account at all by the autoscaler. Using ':' as the delimiter, the list should have such a format: jupyter:student:notebook. They are by default set to "" and "kube-system".

Critical Pods = Pods that are not omitted or assigned a label indicating that they are "preemptible".

Workload = The sum of memory requests of pods that are not omitted.

Capacity = Total meory of the cluster

Utilization = On certain nodes, the ratio between the sum of Workload and the sum of Capacity

When scale.py is exected

1. The autoscaler will calculate the Utilization of the cluster.
2. If the Utilization of the cluster is between a predefined minimum and a predefined maximum, move the Unschedulable flag provided by Kubernetes between nodes, to delete them as soon as possible. Otherwise, the autoscaler will add or remove Unschedulable flags to approximate a predefined optimal utilization; if optimal utilization is not reached, new nodes can be created to meet the goal, to the predefined maximum number of nodes. 2a. Nodes running critical pods will never be marked unschedulable.
3. Make sure there are at least predefined minimum number of nodes schedulable by removing flags or adding new nodes.
4. Shutdown all empty and unschedulable nodes.

1. Read settings.py to make sure you like the current settings.
2. Run scale.py, a one-time scaling should happen, and the script will quit.

Python 3, with kubernetes installed;

Google Cloud client google-api-python-client;

Necessary privilege or credentials.

Only Google Cloud Platform is supported for booting and shutting down nodes for now.

This project was worked on in close collaboration with Cal Blueprint. Cal Blueprint is a student-run UC Berkeley organization devoted to matching the skills of its members to our desire to see social good enacted in our community. Each semester, teams of 4-5 students work closely with a non-profit to bring technological solutions to the problems they face every day.