Skip to content
GitLab
Explore
Sign in
Register
Primary navigation
Search or go to…
Project
R
rand-sched
Manage
Activity
Members
Plan
Wiki
Code
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Snippets
Deploy
Releases
Container registry
Model registry
Analyze
Contributor analytics
Repository analytics
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
GitLab community forum
Contribute to GitLab
Provide feedback
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
Nils Vreman
rand-sched
Commits
349b02ac
Commit
349b02ac
authored
6 years ago
by
Nils Vreman
Browse files
Options
Downloads
Patches
Plain Diff
Readme update
parent
4f70c11d
Branches
Branches containing commit
No related tags found
No related merge requests found
Changes
1
Show whitespace changes
Inline
Side-by-side
Showing
1 changed file
README.md
+18
-1
18 additions, 1 deletion
README.md
with
18 additions
and
1 deletion
README.md
+
18
−
1
View file @
349b02ac
...
...
@@ -4,6 +4,23 @@
The owner (and main contributor) of this repo is Nils Vreman.
Collaborators: Richard Pates, Kristin Krüger, Gerhard Fohler, Martina Maggio.
## Running tests
### Packages
To be able to run this benchmark test, aside from __python3.6__, two packages are needed:
1.
numpy
2.
ortools
These packages can easily be installed with the commands:
pip3 install numpy
pip3 install ortools
### Execution
After installing necessary packages, the benchmark test could be executed using
the __Makefile__ placed in the Code directory. It will create a directory called
"data" containing all the results and corresponding tasksets. The class
__FileSystemManager__
is then used to access the results.
## Navigation
### Code
...
...
@@ -34,7 +51,7 @@ found. Each page in the pdf-file contains the results from the experiments
performed on a certain maximum hyperperiod (written as a title in the top left
corner of each side). Each figure then represents a specific number of tasks in
each taskset of that experiment. The results written in red boxes is the
accur
r
acy of the algorithm, i.e., for how large fraction of the tasksets our algorithm
accuracy of the algorithm, i.e., for how large fraction of the tasksets our algorithm
found an optimal schedule set within 60 minutes. This is also summarized in the
table below.
...
...
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment