Frontier is an experimental edge processing platform for the Internet of Things (IoT) that aims to provide high-throughput data-parallel processing across multiple edge devices. Further details on Frontier, including a paper that explains the underlying model, can be found at the project website. The research that lead to Frontier was sponsored by the ITA project.
Frontier is licensed under EPL (Eclipse Public License). The Frontier system is under heavy development and should be considered an alpha release. This is not considered a "stable" branch.
Frontier builds on previous research into data-parallel stream processing for datacenter environments as part of the SEEP project, albeit heavily modified to operate in an edge environment.
Below is some information regarding how to build and run Frontier.
The project follows the standard Maven directory structure, with two differentiated modules, seep-system and seep-streamsql.
From the top level directory:
./frontier-bld.sh pi
Will build seep-system and the example applications stateless-simple-query and
acita_demo_2015 to execute on Raspberry Pi. Alternatively, to build for the
CORE/EMANE wireless network emulator:
./frontier-bld.sh core
The system requires one master node and N worker nodes (one worker node per Operator).
First set the IP address of the master node in mainAddr inside
config.properties and rebuild the system. By default it is 127.0.0.1
so you don't need to change anything if running in local mode (see below).
Next run the master in the designated node:
java -jar <system.jar> Master <query.jar> <Base-class>
where query.jar is the compiled query and the last parameter is the name of
the base class, not a path.
e.g. To run the master for the acita_demo_2015 example:
cd seep-system/examples/acita_demo_2015
java -jar lib/seep-system-0.0.1-SNAPSHOT.jar Master `pwd`/dist/acita_demo_2015.jar Base
Finally run as many worker nodes as your query requires:
java -jar <system.jar> Worker
To run Frontier in a single local machine, append a different port to each Worker node:
java -jar <system.jar> Worker <port>
e.g. For the acita_demo_2015 example:
cd seep-system/examples/acita_demo_2015
java -jar lib/seep-system-0.0.1-SNAPSHOT.jar Worker 3501
java -jar lib/seep-system-0.0.1-SNAPSHOT.jar Worker 3502
java -jar lib/seep-system-0.0.1-SNAPSHOT.jar Worker 3503
Note you will need to run the master and each worker in a different shell. Then follow the instructions on the master command prompt.
Specifically, after giving the workers a few seconds to register with the master, enter 1 at the master command prompt.
This will deploy the operators from the query in src/Base.java to the workers.
Once that has completed, simply press 2 to start the query, and enter to start the source.
You should see tuples being received in shell output for the worker running the sink operator.
To run one of the face recognition queries on raspberry pi:
cd seep-system/examples/acita_demo_2015
cp ~/.m2/repository/org/bytedeco/javacv/1.2/javacv-1.2.jar lib
cp ~/.m2/repository/org/bytedeco/javacpp/1.2/javacpp-1.2.jar lib
cp ~/.m2/repository/org/bytedeco/javacpp-presets/1.2/javacpp-1.2.jar lib
cp ~/.m2/repository/org/bytedeco/javacpp-presets/opencv/3.1.0-1.2/opencv-3.1.0-1.2.jar lib
cp ~/.m2/repository/org/bytedeco/javacpp-presets/opencv/3.1.0-1.2/opencv-3.1.0-1.2-linux-armhf.jar lib
ant clean
ant
Next, start the master and workers using the following command line from the directory seep-system/examples/acita_demo_2015/tmp, i.e.
cd tmp
java -classpath "../lib/*" uk.ac.imperial.lsds.seep.Main Master `pwd`/../dist/acita_demo_2015.jar Base
N.B. Note the command line is different to before since we are now specifying the classpath explicitly so that it picks up all the jars in lib.
To start the workers, N.B. again from the directory seep-system/examples/acita_demo_2015/tmp
cd tmp
java -classpath "../lib/*" uk.ac.imperial.lsds.seep.Main Worker 3501
cd tmp
java -classpath "../lib/*" uk.ac.imperial.lsds.seep.Main Worker 3502
cd tmp
java -classpath "../lib/*" uk.ac.imperial.lsds.seep.Main Worker 3503
Now if you follow the master command prompt as before it should run a face recognition query. N.B. You must allow sufficient time for step 1 to complete. It may take a couple of minutes for the workers to train the prediction model on a raspberry pi. When the workers are ready, the master command prompt will reappear. Do not proceed to stage 2 at the master before the 2nd command prompt appears!
Frontier was created by Dan O'Keeffe (formerly in the Large-Scale Distributed Systems (LSDS) group, Imperial College London, now Royal Holloway University of London), Theodoros Salonidis (IBM Research T.J. Watson), and Peter Pietzuch (LSDS group, Imperial College London).