Skip to content
Jupiter is an orchestrator for Dispersed (Networked) Computing that uses Docker containers and Kubernetes.
Python Dockerfile Shell
Branch: develop
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
app_specific_files
ci
circe
docs
mulhome_scripts
profilers
task_mapper
tutorial
.gitignore
.gitmodules
LICENSE.txt
README.md
jupiter_config.ini
jupiter_config.py
k8_requirements.txt
nodes.txt
sourceit.sh

README.md

Jupiter

Jupiter is an orchestrator for Dispersed Computing (distributed computing with networked computers) that uses Docker containers and Kubernetes (K8s).

Jupiter enables complex computing applications that are specified as directed acyclic graph (DAG)-based task graphs to be distributed across an arbitrary network of computers in such a way as to optimize the execution of the distributed computations. Depending on the scheduling algorithm/task mapper used with the Jupiter framework, the optimizations may be for different objectives, for example, the goal may be to try and minimize the total end to end delay (makespan) of the computation for a single set of data inputs. Jupiter includes both centralized task mappers such as one that performs the classical HEFT (heterogeneous earliest finish time) scheduling algorithm, as well as an innovative new distributed task mapping framework called WAVE. In order to do enable optimization-oriented task mapping, Jupiter also provides tools for profiling the application run time on the computers as well as profiling and monitoring the performance of the network. Jupiter also provides for container-based code dispatch and execution of the distributed application at run-time for both single-shot and pipelined (streaming) computations.

The Jupiter system has three main components: Profilers, Task Mappers, and CIRCE Dispatcher. (For a detailed documentation refer to our http://jupiter.readthedocs.io/)

Profilers

Jupiter comes with two different profiler tools: DRUPE (Network and Resource Profiler) and an one time Execution Profiler.

DRUPE is a tool to collect information about computational resources as well as network links between compute nodes in a dispersed computing system to a central node. DRUPE consists of a network profiler and a resource profiler.

The onetime Execution Profiler is a tool to collect information about the computation time of the pipelined computations described in the form of a directed acyclic graph (DAG) on each of the networked computation resources. This tool runs a sample execution of the entire DAG on every node to collect the statistics for each of the task in the DAG as well as the makespan of the entire DAG.

Task Mappers

Jupiter comes with three different task mappers: HEFT, WAVE Greedy, WAVE Random; to effciently map the tasks of a DAG to the processors such that the makespan of the pipelines processing is optimized.

HEFT i.e., Heterogeneous Earliest Finish Time is a static centralized algorithm for a DAG based task graph that efficiently maps the tasks of the DAG into the processors by taking into account global information about communication delays and execution times.

WAVE is a distributed scheduler for DAG type task graph that outputs a mapping of tasks to real compute nodes by only taking into acount local profiler statistics. Currently we have two types of WAVE algorithms: WAVE Random and WAVE Greedy.

WAVE Random is a very simple algorithm that maps the tasks to a random node without taking into acount the profiler data.

WAVE Greedy is a Greedy algorithm that uses a weighted sum of different profiler data to map tasks to the compute nodes.

CIRCE

CIRCE is a dispatcher tool for dispersed computing, which can deploy pipelined computations described in the form of a directed acyclic graph (DAG) on multiple geographically dispersed computers (compute nodes). CIRCE deploys each task on the corresponding compute node (from the output of WAVE), uses input and output queues for pipelined execution, and takes care of the data transfer between different tasks.

Clone Instructions:

This Repository comes with a submodule with links to another repository that contains codes related to one application (Distributed Network Anomaly Detection) of Jupiter.

If you are interested in cloning just the Jupiter Orchestrator, not the application specific files, just run

    git clone git@github.com:ANRGUSC/Jupiter.git

If you are interested in cloning the Jupiter Orchestrator along with the Distributed Network Anomaly Detection related files, run

    git clone --recurse-submodules git@github.com:ANRGUSC/Jupiter.git
    cd Jupiter
    git submodule update --remote

Requirements:

In order to use the Jupiter Orchestrator tool, your computer needs to fulfill the following set of requirements.

  1. You MUST have kubectl installed (instructions here)

  2. You MUST have python3 installed

  3. You MUST have certain python packages (listed in k8_requirements.txt) installed. You can install them by simply running pip3 install -r k8_requirements.txt

  4. You MUST have a working kubernetes cluster with proxy capability.

  5. To control the cluster, you need to grab the admin.conf file from the k8s master node. When the cluster is bootstrapped by kubeadm, the admin.conf file is stored in /etc/kubernetes/admin.conf. Usually, a copy is made into the $HOME folder. Either way, make a copy of admin.conf into your local machine's home folder. Currently, you need to have admin.conf in the $Home folder. Our python scripts need it exactly there to work. Next, you need to run the commands below. You can wrap it up in a script you source or directly place the export line and source line into your .bashrc file. However, make sure to re-run the full set of commands if the admin.conf file has changed:

    export KUBECONFIG=$HOME/admin.conf #check if it works with `kubectl get nodes`
    source <(kubectl completion bash)
  1. The directory structure of the cloned repo MUST conform with the following:
        Jupiter
        │   jupiter_config.py 
        |   jupiter_config.ini
        |   nodes.txt
        │
        └───profilers
        │  
        └───task_mapper
        |   
        └───circe
        |
        └───app_specific_files
        |   |
        |   └───APP_folder
        |       |
        |       |   configuration.txt 
        |       |   app_config.ini 
        |       |
        |       └───scripts
        |       |
        |       └───sample_input
        |
        └───scripts

Deploy Instructions:

For a step by step instruction for deployment of Jupiter, please refer to our tutorial.

Tutorial

Step by step instructions to set up Jupiter on a private network provided by Sean Griffin (Raytheon BBN Technologies) [here (tutorial/Jupiter_setup.pdf)]

Applications:

Jupiter accepts pipelined computations described in a form of a Graph where the main task flow is represented as a Directed Acyclic Graph (DAG). Thus, one should be able separate the graph into two pieces, the DAG part and non-DAG part. Jupiter requires that each task in the DAG part of the graph to be written as a Python function in a separate file under the scripts folder. On the other hand the non-DAG tasks can be either Python function or a shell script with any number of arguments, located under the scripts folder.

As an example, please refer to our codes available for the following applications customized for the Jupiter Orchestrator:

  1. Coded Network Anomaly Detection (Coded DNAD)
  2. Multi-Camera Processing DAG (MCP DAG)
  3. Automatic-DAG-Generator(Dummy DAG)

Visualization

The visualization tool for Jupiter is given here. This tool generates an interactive plot to show the scheduling result of WAVE and the dispatcher mapping of CIRCE. To visualize your own application, make sure the format of your logs are in line with the input files of the tools. We will integrate this as a real-time visualization tool for Jupiter in the next release.

References

[1] Pradipta Ghosh, Quynh Nguyen, and Bhaskar Krishnamachari, “Container Orchestration for Dispersed Computing“, 5th International Workshop on Container Technologies and Container Clouds (WOC ’19), December 9–13, 2019, Davis, CA, USA.

[2] Quynh Nguyen, Pradipta Ghosh, and Bhaskar Krishnamachari, “End-to-End Network Performance Monitoring for Dispersed Computing“, International Conference on Computing, Networking and Communications, March 2018

[3] Pranav Sakulkar, Pradipta Ghosh, Aleksandra Knezevic, Jiatong Wang, Quynh Nguyen, Jason Tran, H.V. Krishna Giri Narra, Zhifeng Lin, Songze Li, Ming Yu, Bhaskar Krishnamachari, Salman Avestimehr, and Murali Annavaram, “WAVE: A Distributed Scheduling Framework for Dispersed Computing“, USC ANRG Technical Report, ANRG-2018-01.

[4] Aleksandra Knezevic, Quynh Nguyen, Jason A. Tran, Pradipta Ghosh, Pranav Sakulkar, Bhaskar Krishnamachari, and Murali Annavaram, “DEMO: CIRCE – A runtime scheduler for DAG-based dispersed computing,”, The Second ACM/IEEE Symposium on Edge Computing (SEC) 2017. (poster)

Acknowledgment

This material is based upon work supported by Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001117C0053. Any views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

You can’t perform that action at this time.