Some PEP-8 fixes for lib/galaxy/model/__init__.py.
Tighten permissions for workflow invocations.

Sharing a workflow with a user was previously sufficient to grant access to all invocations of that workflow. This isn't a huge problem since the information potentially leaking out was limitted to invocation counts, various encoded ids, and update times. Still I think no information about invocations should be avaialble as a result of sharing a workflow - and upcoming changes to Galaxy will result in much more information being made available via the workflow invocation API.
Update workflow invocation to_dict for recent collection workflow changes.

There may now be multiple WorkflowInvocationSteps for each WorkflowStep for steps that are mapped over collections - so to_dict creating a dictionary of this information indexed on order step is problematic because only one WorkflowInvocationStep will be represented per step. Instead now just returning a big list of all of the invocations - which contains all of the same information. This is a backward incompatible API change for the workflow invocation API.

Also update the input mapping stuff with logic for dealing with data collection inputs.
Work toward workflow progress refactor.
Implement workflow requests (API only) and a scheduling 'plugin' framework to evaluate them.

The model changes and WorkQueue class are based on modifications to Kyle Ellrott's Galaxy farm work (https://bitbucket.org/kellrott/galaxy-farm/branch/workflow_migrate). I made heavy modifications to the model to enforce referential integrity on parameter to workflow step mappings and made some cosmetic changes various other details. I further filled out WorkQueue (adding ability to 'complete' requests, implementing tests, etc...). The work context (allowing tool's to be executed outside the context of a web thread), workflow module and API modifications, and the workflow scheduling framework are my own contributions.

I like some of this - I remain skeptical about much of it though. I think maintaining queues of generic requests and distributing them across multiple processes, machines, etc... is both a hard problem and one for which many adequete solutions already exist - some of which we are already using (if only optionally) in other parts of Galaxy - so I don't understand why we need/want to so explicitly replicate this in Galaxy and not just defer to an external message queue.

In internal discussions it has been argued that message queues are appropriate for short requests but long term requests are better tracked in the database. I know that intuitively these operations may feel different - but I am unconvienced they are from a technical standpoint. Delaying an action that would have happened in milliseconds to hours later is many order of magnitude larger than the additional delay when going from the hours to days or weeks.

My own preference would be that both this work and Galaxy farm further investigate using message queues before making such substantial changes to the data model. It certainly may prove that this approach is needed - but I personally would like to understand why maintain the work queue in a message queue would not work first.

However, if the Galaxy project is going to add a WorkRequest object to the database and something that looks like the WorkQueue class in the new galaxy.work module and store and distribute workflows that way for the farm project, then I believe that typical workflow requesting and scheduling should make use of it also instead of developing a competing implementation. (Because my interaction pattern with Galaxy has always been if you cannot beat them join them.)

Testing

One can issue a workflow request to the WorkQueue and scheduled via the scheduling framework by running the following test.

./run_tests.sh -api test/api/test_workflows.py:WorkflowsApiTestCase.test_workflow_request

Final Note: The workflow scheduling stuff is a bit rough right now - it is just enough to prove to myself that this approach can be taken and the farm-stuff and workflow scheduling stuff can utilize the same models. I wanted to try to spark a broader conversation on this (re-encounrage use of message queues) before putting more effort into this approach.
Work toward delayed evaluation.
Rebase to progress.
Rebase into workflow scheduling.
WIP: Pause module for workflows.