The adaptive scheduler works with the Observation Portal to get the set of schedulable requests, and cancel and submit sets of observations on sites. It solves an optimization problem using a mixed integer programming solver to maximize the priority of scheduled observations. It supports the three types of observations within the Observation Portal (Normal, Rapid Request, and Time Critical). It has optional support to query elasticsearch to incorporate telescope telemetry into it's decision to schedule on a telescope. It connects to the Configuration Database to get the set of schedulable instruments.
Optional prerequisites can be skipped for reduced functionality.
- Python == 3.4 (for now, due to our current gurobi license. This dependency will be removed in a future update)
- A running Configuration Database
- A running Observation Portal
- (Optional) A running Downtime Database
- (Optional) A running Elasticsearch with index for telescope telemetry
- (Optional) A running Redis instance - used for caching rise-set values
| Variable | Description | Default | |
|---|---|---|---|
| Gurobi | GRB_LICENSE_FILE |
The location of the gurobi license file within your container | Empty string |
| Metrics | OPENTSDB_HOSTNAME |
The host name for an opentsdb server, for metrics | Empty string |
| External Services | CONFIGDB_URL |
The url to the configuration database | http://127.0.0.1:7500 |
DOWNTIMEDB_URL |
The url to the downtime database | http://127.0.0.1:7000 |
|
ELASTICSEARCH_URL |
The url to the elasticsearch cluster | Empty string |
|
OBSERVATION_PORTAL_URL |
The url to the observation portal | http://127.0.0.1:8000 |
|
REDIS_URL |
The url of the redis cache (or the linked container name) | redis |
The scheduler can either be run directly on a machine, or in the provided Docker container
First install the requirements into a python3.4 virtualenv. There are a large number of input arguments, with defaults defined in the SchedulerParameters class in adaptive_scheduler/scheduler_input.py.
python as.py --help
You can build the Dockerfile locally with local changes by running
docker build -t name_of_my_container .
You can then update the container name in the supplied docker-compose.yml and run the tests using
docker-compose up
To run the actual scheduler instead of its unit tests, change the entrypoint in the docker-compose.yml file to
invoke the main entrypoint python as.py with the desired arguments. Currently this will only work if you have a Gurobi
license for Gurobi 6.0.2 mounted into the container and linked appropriately, or switch the code to not use Gurobi.
In the near future, we will be migrating away from the direct Gurobi dependency and try to use Google's OR-Tools. This should allow easy trial of several free optimization algorithms, along with Gurobi if a license is provided.