Additional Material for the paper A Self-Adaptive Digital Twin Architecture for Dynamic Resource Management
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| Lingua: | inglese |
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2026
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| author | Sieve, Riccardo Kobialka, Paul Pferscher, Andrea Bencomo, Nelly Tapia Tarifa, Silvia Lizeth Rasmussen, Buster Salomon Johnsen, Einar Broch |
| author_facet | Sieve, Riccardo Kobialka, Paul Pferscher, Andrea Bencomo, Nelly Tapia Tarifa, Silvia Lizeth Rasmussen, Buster Salomon Johnsen, Einar Broch |
| contents | <h1>Additional Material for the paper A Self-Adaptive Digital Twin Architecture for Dynamic Resource Management</h1> <p>The repository contains additional material for the SEAMS 2026 conference paper.<br>The material includes:</p> <ul> <li>The source code of the prototype implementation of the approach presented in the paper in the <em>src</em> folder.</li> <li>The evaluation data and results in the <em>data</em> folder.</li> </ul> <p>To process the data and reproduce the graphs presented in the paper, the following Python packages are required:</p> <ul> <li>matplotlib</li> <li>numpy</li> </ul> <p>the requirements.txt file can be used to install the required packages using pip:</p> <pre><code>pip install -r requirements.txt</code></pre> <h2>Data evaluation</h2> <p>The `data` folder contains the evaluation data and results for the experiments presented in the paper, alongside the scripts used to generate the plots.<br>To execute the commands below, navigate to the `data` folder in your terminal</p> <pre><code>cd data</code></pre> <p>To reproduce the graph in Figure 9 in the paper, run the following command in the <em>data</em> folder:</p> <pre><code>python3 plot_single.py sim_output_no_peaks/normal_40_15_1_20_True</code></pre> <p>To reproduce the graph in Figure 10 in the paper, run the following command in the `data` folder:</p> <pre><code>python3 plot_multi.py --mean 40 --std 15 --mode normal --iterations 5 --time_steps 20 --adaptive --base sim_output_peaks</code></pre> <p>The results will be saved in the two folders, <em>sim_output_no_peaks</em> and <em>sim_output_peaks</em>, respectively.</p> <p>To generate the time statistics (with the different times captured) from the results, run the following commands in the <em>data</em> folder:</p> <pre><code>python3 plot_time_multi.py --iterations 10 --mean 40 --std 15 --time_steps 20 --adaptive --base sim_output_no_peaks</code></pre> <p>for the results without peak adaptation, and</p> <pre><code>python3 plot_time_multi.py --iterations 10 --mean 40 --std 15 --time_steps 20 --adaptive --base sim_output_peaks</code></pre> <p>for the results with peak adaptation.<br>The parameters passed to the scripts are as follows:</p> <ul> <li><em>--mean</em>: the lambda of the distribution used to generate the sensor data.</li> <li><em>--std</em>: the standard deviation. While not used in the Poisson distribution, it still serves to identify the correct folder names.</li> <li><em>--mode</em>: the distribution mode used to generate the distribution with different characteristics. We used `normal` in the experiments, and the parameter is used to identify the correct folder names.</li> <li><em>--iterations:</em> the number of iterations used in the experiments.</li> <li><em>--time_steps</em>: the number of time steps used in the experiments.</li> <li><em>--adaptive</em>: flag to indicate that the adaptive strategy was used.</li> <li><em>--base</em>: the base folder where the results are stored.</li> </ul> <h2>Source code</h2> <p>The <em>src</em> folder contains the source code of the prototype implementation of the approach presented in the paper.</p> <p>The README in the folder further shows how to install the system and run tests.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18270926 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Additional Material for the paper A Self-Adaptive Digital Twin Architecture for Dynamic Resource Management Sieve, Riccardo Kobialka, Paul Pferscher, Andrea Bencomo, Nelly Tapia Tarifa, Silvia Lizeth Rasmussen, Buster Salomon Johnsen, Einar Broch <h1>Additional Material for the paper A Self-Adaptive Digital Twin Architecture for Dynamic Resource Management</h1> <p>The repository contains additional material for the SEAMS 2026 conference paper.<br>The material includes:</p> <ul> <li>The source code of the prototype implementation of the approach presented in the paper in the <em>src</em> folder.</li> <li>The evaluation data and results in the <em>data</em> folder.</li> </ul> <p>To process the data and reproduce the graphs presented in the paper, the following Python packages are required:</p> <ul> <li>matplotlib</li> <li>numpy</li> </ul> <p>the requirements.txt file can be used to install the required packages using pip:</p> <pre><code>pip install -r requirements.txt</code></pre> <h2>Data evaluation</h2> <p>The `data` folder contains the evaluation data and results for the experiments presented in the paper, alongside the scripts used to generate the plots.<br>To execute the commands below, navigate to the `data` folder in your terminal</p> <pre><code>cd data</code></pre> <p>To reproduce the graph in Figure 9 in the paper, run the following command in the <em>data</em> folder:</p> <pre><code>python3 plot_single.py sim_output_no_peaks/normal_40_15_1_20_True</code></pre> <p>To reproduce the graph in Figure 10 in the paper, run the following command in the `data` folder:</p> <pre><code>python3 plot_multi.py --mean 40 --std 15 --mode normal --iterations 5 --time_steps 20 --adaptive --base sim_output_peaks</code></pre> <p>The results will be saved in the two folders, <em>sim_output_no_peaks</em> and <em>sim_output_peaks</em>, respectively.</p> <p>To generate the time statistics (with the different times captured) from the results, run the following commands in the <em>data</em> folder:</p> <pre><code>python3 plot_time_multi.py --iterations 10 --mean 40 --std 15 --time_steps 20 --adaptive --base sim_output_no_peaks</code></pre> <p>for the results without peak adaptation, and</p> <pre><code>python3 plot_time_multi.py --iterations 10 --mean 40 --std 15 --time_steps 20 --adaptive --base sim_output_peaks</code></pre> <p>for the results with peak adaptation.<br>The parameters passed to the scripts are as follows:</p> <ul> <li><em>--mean</em>: the lambda of the distribution used to generate the sensor data.</li> <li><em>--std</em>: the standard deviation. While not used in the Poisson distribution, it still serves to identify the correct folder names.</li> <li><em>--mode</em>: the distribution mode used to generate the distribution with different characteristics. We used `normal` in the experiments, and the parameter is used to identify the correct folder names.</li> <li><em>--iterations:</em> the number of iterations used in the experiments.</li> <li><em>--time_steps</em>: the number of time steps used in the experiments.</li> <li><em>--adaptive</em>: flag to indicate that the adaptive strategy was used.</li> <li><em>--base</em>: the base folder where the results are stored.</li> </ul> <h2>Source code</h2> <p>The <em>src</em> folder contains the source code of the prototype implementation of the approach presented in the paper.</p> <p>The README in the folder further shows how to install the system and run tests.</p> |
| title | Additional Material for the paper A Self-Adaptive Digital Twin Architecture for Dynamic Resource Management |
| url | https://doi.org/10.5281/zenodo.18270926 |