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  • Design Goal / Requirement

    BD-703. To add the use of Docker containers as a new granularity of deployment in the VM elasticity module, thus expands its functionality.

  • Design Questions and Answers

  1. Should an extractor type be managed by both an VM image and Docker, or only one of them?
    A: only one for simplicity. So need to specify this piece of information in the config file.
  2. Support managing the extractors both using VM images and using Docker containers at the same time?
    A: Yes.

  3. Separate Docker machines than the other machines to host Docker containers, or on the same machines?

    A: Separate. 

  4. Docker image storage: docker hub, or a private registry?
    A: Docker hub for now. Setting up a private registry takes time and a secure one requires getting a certificate from a CA. Can do it later when needed. Use ncsa/clowder-ocr, ncsa/clowder-python-base, ncsa/clowder-opencv-closeups, etc. for now.

  5. How do we restart a docker container if the application crashed/stopped?
    A: docker run --restart=always ...
    This will retry indefinitely, but with a delay that doubles before each retry, starting from 100 ms (0.1 second), to avoid flooding the server. Can also consider using "--restart=on-failure[:max-retries]" to limit the number of retries, but then that could leave a container in the stopped state, without any component to restart it. Usually a RabbitMQ server restart would cause an error, and the error was observed to persist for about 2 minutes.
  6. How do we scale up an application managed by docker?
    A: see below.
  7. How do we scale down?
    A: see below.
    1. Do we suspend and resume docker VMs, or always keep them running?
      A: We suspend and resume docker VMs, but keep at least 1.
  8. A manually created and updated VM image is used to start "Docker machines" or "Docker VMs" to host the docker containers. How do we start the Docker machines – externally bootstrap, or start them using the elasticity module?
    A: Use the elasticity module. Add the docker VM image info in the config file, set a min # of 1. Later on the scaling-up logic will start more as needed. Need special handling: the dockerized extractors depend on its existence, so if not already existing, a Docker machine needs to be started first.
  9. How do we detect idle extractors managed by docker?
    A: Same logic using the RabbitMQ API as before. After detection, perform docker-specific commands to stop the idle extractors.
  10. How do we detect idle docker machines if no container runs on them?
    A: Since a Docker machine itself does not make RabbitMQ connections.If no extractor runs on it, it won't show up in the extr->VM or VM->extr maps, so need to maintain separate maps for the Docker machines. Find the Docker machines that have no extractors running on them, add them to the idle VM list.
  11. How do we specify mapping between docker images and the corresponding extractors?
    A: add a [Docker] section in the config file, 1 to 1 mapping: "extr1: dockerimg1". When starting the elasticity module, load the config file, and check for errors: one extractor type should be managed only by one method: either docker or a VM image. If such configuration errors exist, print out, and use a default type such as docker – also make this choice a configurable item in the config file.
  12. Details of the Docker VM image?
    A: Ubuntu 14.04 base image + Docker installed.
    In the config file [OpenStack Image Info] section:
         docker-ubuntu-trusty = docker, m1.large, ubuntu, NCSA-Nebula, ''
    Use a larger flavor (4 or 8 CPUs), since one docker VM hosts multiple containers. Pull all needed docker images for the extractors for faster container start time at run time. Future enhancement: when starting the module, ensure that the docker images specified in the config file are valid and available, and pull them on to all Docker machines if not already.

  • Algorithm / Logic

    • Assumptions:
  1. The module manages the extractors using both VM images and Docker containers at the same time;
  2. A manually created and updated VM image is used to start "Docker machines" or "Docker VMs".
  3. Dockerized extractors run only in the Docker machines, the extractors managed by VM images do not run in the Docker machines.
  • Additional data structure to add support for Docker:
  1. a map to look up whether an extractor is managed by Docker or a VM image;
  2. a list/map of Docker machines, to start/suspend//resume Docker machines;
  3. a map of extractor to Docker images, to be used in adding extractor instances;
    • Scaling up
      Similar logic as that when managing with VM images. Differences:
      1. Split each of add_extractor_instance(), resume_VM_containing_this_extractor_successful() and start_new_VM_containing_this_extractor_successful() into 2 methods: one uses services, the other uses docker, and when entering, pick one depending on the extractor type.
      2. In resuming suspended VMs and starting new VMs, need to wait it to finish, then run a Docker command on it to add an extractor instance.
      3. Obviously, add new code with Docker commands to start/stop/remove the containers. Need new logic for naming the docker containers, possibly similar to that for naming the VMs.
    • Scaling down
    1. Stop idle extractor instances:
      for extr1 that's managed by docker:
      Loop through docker VMs:
             remove all extr1 containers, barring min #;
    2. Suspend idle VMs.
      If a docker VM has at least one app running on it,
          the RabbitMQ managment API will detect such an idle docker VM, and the existing logic will suspend it;
      otherwise, loop through docker VMs:
          suspend it if it does not have any containers running.

  • Other Considerations

  1. Threads:
    Due to time constraint, no threads will be used for now. May need to use multiple threads in the future. When scaling up, after resuming a suspended VM or starting a new docker machine, the module needs to log into it to start a new container, so need to block waiting for the resuming or starting to finish, and this may take a while – starting a new Docker VM takes 1–2 minutes.
  2. Remove stopped containers?
    Since starting a new container is fast, simplify the design of scaling down by removing the containers instead of leaving them around. Later can explore keeping them as stopped when scaling down, and start the stopped ones when scaling up – a possible future improvement.

  • Programming Language and Application Type

Continue with the use of Python and a stand-alone program.

  • Testing

  • Input
    Use a script to generate high request rates with OCR and OpenCV extractors to test scaling up. Stop sending the requests to test scaling down.
  • Output
    Use the OpenStack CLI / web UI to monitor the VMs, and use the RabbitMQ web UI and ssh into the docker machines to monitor the extractors, to verify that the docker containers are started/stopped, and the docker VMs are started/resumed/suspended as expected.

 

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