README.md

mu2sls

This is the repository for the mu2sls prototype, described in the POPL23 paper titled "Executing Microservice Applications on Serverless, Correctly".

Running POPL23 paper experiments

See the POPL23_ARTIFACT_README.md.

Requirements

The mu2sls framework requires python3.8 and above.

Deploying an application and running tests

In order to deploy an application on your local machine, you need to use the following script with the .csv file as the first argument. For example, for an application that contains a single-stateful service, you can use the experiments/single-stateful.csv as follows:

python3 test_services.py experiments/single-stateful.csv local

The test that will be run is defined in the TEST_FUNC_FROM_FILE dictionary in file tests/test_services.py.

(Local) Building and pushing application docker images

In order to run an application, you need to first build it on your local machine and push it to an image registry. If you need to do that, update the $docker_io_username variable in vars.sh to include your docker_io_username, alternatively, you can use the prebuilt images.

Example usage of build script:

## Build and deploy the single-stateful application
python3 scripts/knative_dev.py ${docker_io_username} experiments/single-stateful.csv

Developing an application using mu2sls

For an example of a service Caller2 that simply forwards its results to a backend service (Backend) see tests/source_specs/async-test/caller2.py, and for the example of a backend service that tries to perform a transaction where it increments the counter for the input key see tests/source_specs/async-test/backend.py.

Then, you have to write a csv file that contains the information for all services for the compiler to compile them and to be able to build and deploy them. You should put this csv file in the experiments directory if you want to reuse our scripts for uploading it to the remote cluster.

For an example of a csv file that composes Caller2, Backend, and a frontend service Caller1, see experiments/chain.csv.

The names of the services (which need to correspond with their class definitions) are in the first field of the csv and the source files of the application services are indicated in the second field of the csv files.

Then you can add a test for your application by adding a function in tests/test_services.py and then adding a line in the TEST_FUNC_FROM_FILE definition (lines 132-139 in tests/test_services.py) to associate your csv application file with the test function name. To write the test function, see run_test_async (lines 118-130 in tests/test_services.py).

You can add multiple calls to your test, first calling a few "update" methods and then calling some method that shows the state to test that it was updated correctly.

Then you can run this test locally by calling the following:

export csv_file=# Your csv file
python3 tests/test_services.py "${csv_file}" local

As a bonus, to run the test remotely, you first need to build and push the docker images and upload the csv to the remote machine using the following:

export csv_file=# Your csv file
export docker_io_username=# your docker.io username so that you push the images
python3 scripts/knative_dev.py "${docker_io_username}" "${csv_file}"

## For the following to find your csv and upload it, it needs to be in the
## experiments directory.
./scripts/upload-to-remote.sh cloudlab ${node_address} ${node_username} ${private_key}

and then connect to the remote machine where the cluster is running (see the "Setup the Cloudlab machine" section above), and run:

## On the remote machine
export csv_file=# The csv file path on the remote machine
export docker_io_username=# your docker.io username where you have pushed the images
python3 test_services.py "${csv_file}" knative --docker_io_username "${docker_io_username}"