DEW (Doer!): Distributed Eperiment Workflows Representation
DEW unifies behavior and topology of an experiment, but captures only relevant topological details.
DEW has several design goals:
It was important to us that DEW contains a human-readable, short description of what the experiment is supposed to do. Such a description facilitates reuse, because researchers could, at a glance, understand the experiment's actions and judge if this experiment is useful to them. Moreover, expressing what an experiment will do in broad strokes captures the process, which so far has existed only in the researcher's mind, and eases experiment design.
DEW should support many diverse experiments, and thus its language must be expressive enough to be broadly applicable, regardless of the experiment's goal or testbed infrastructure where it will be realized.
We wanted DEW to contain sufficient details to facilitate automated generation of experimental topologies and scripts, which would run on these topologies. If we could achieve this, then researchers could work with experiments at a high level, delegating tedious, detail-oriented and error-prone tasks to machines. This would further facilitate generation of topologies and scripts in many different languages, and for many different testbed infrastructures, enabling portability.
Decouple behavior from topology
We wanted to decouple the intended behavior from the topology where it will be realized, thus enabling the same experiment to be scaled up and down easily, by changing a few lines of DEW. Our goal was to capture only the necessary topology details in the form of constraints the behavior must place on resources.
We wanted to impose some natural structure on DEW, enabling researchers to easily locate and focus on the important pieces for their goal(s). This structure should facilitate experiment design and running, which are aligned with human cognitive process.
You'll need: - Python (either 2.x or 3.x) - pyparsing - networkx 2+ - Enum - numpy
Optionally you'll also want: - spaCy (for Natural Language Processing) - spaCy english model (en) - CEFTB Xir description language (for constraint processing and saving experiment descriptions)
See the readme in the Constraints directory for setting up and using the constraint server.
For installing Xir see: https://github.com/ceftb/xir https://github.com/ceftb/xir/tree/master/lang/python
To run the GUI: % python dew_gui.py
You can find the BNF notation of DEW in BNF folder.
Folder UI has our UI implementation
Folder translators will house translators from other representations into DEW