Schedule-Abstraction Graph in GO

Unofficial implementation of schedule-abstraction graph using GO lang

Dependencies • Build • Input Format • Usage • Features • Limitations • License

NOTICE: THIS PROGRAM IS UNDER DEVELOPMENT...

Schedule-abstraction graph (SAG) is a reachability-based response-time analysis for real-time systems.

You can visit the official repository of SAG here.

📦 Required Packages

This program uses the following packages:

github.com/docopt/docopt-go
github.com/lfkeitel/verbose
gopkg.in/yaml.v3

📋 Build Instructions

For building the program, you can use the following command:

go build ./nptest.go

For running the program, you can use the following command:

go run ./nptest.go -j <input-file> [options]

📄 Input Format

This tool works with old SAG input format with csv format (Example) and also new SAG input format with yaml format (Example). Each input file describes a set of jobs. Each job is described by the following fields:

1. Task ID — an arbitrary numeric ID to identify the task to which a job belongs
2. Job ID — a unique numeric ID that identifies the job
3. Release min — the earliest-possible release time of the job (equivalently, this is the arrival time of the job)
4. Release max — the latest-possible release time of the job (equivalently, this is the arrival time plus maximum jitter of the job)
5. Cost min — the best-case execution time of the job (can be zero)
6. Cost max — the worst-case execution time of the job
7. Deadline — the absolute deadline of the job
8. Priority — the priority of the job (EDF: set it equal to the deadline)

⚙️ Usage

For running the test for an example input file example4.csv, use the following command:

go run ./nptest.go -j ./example/example4.csv -r 5 -c

If you already have a compiled version of the program, you can use the following command:

./nptest -j ./example/example4.csv -r 5 -c

See the help ./nptest --help or go run ./nptest.go -h for further options.

🔧 Features

• Classic single processor SAG.
• Single processor SAG with partial-order reduction.

🚧 Limitations

• For now, the framework just supports single processor.

📝 TODO

• Implementation of uni-processor
• Implementation of uni-processor with partial-order reduction
• Implement dependency
• Implement IIP
• Implement global multi-processor

🌱 Contribution

With your feedback and conversation, you can assist me in developing this application.

• Open pull request with improvements
• Discuss feedbacks and bugs in issues

📜 License

Copyright © 2022 Pourya Gohari

This project is licensed under the Apache License 2.0 - see the LICENSE.md file for details