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2 results for “virtual network function”
Quality-aware Analysis and Optimisation of Virtual Network Function
<p># SPLC'22 Quality-aware Analysis and Optimisation of Virtual Network Functions</p><p> </p><p>DATA: Quality-aware Analysis and Optimisation of Virtual Network Functions</p><p> </p><p>This repository contains the models, operations and results empirically used in [Quality-aware Analysis and Optimisation of Virtual Network Functions](https://doi.org/10.1145/3546932.3547007) at SPLC 2022.</p><p>Due to copyright issues, it does not contain the tools (i.e., automated reasoners), although their official sites are provided.</p><p> </p><p>It is licensed under the [MIT license](https://github.com/danieljmg/SPLC22/blob/main/LICENSE).</p><p> </p><p> </p><p>## SPLC'22 Models, Categorical Operations and Datasets</p><p> </p><p>This data-set contains:</p><p> </p><p>1. The 5 SPL categories in CQL alongside the 11 tested operations.</p><p>2. The 5 SPL Clafer models.</p><p>3. The 5 SPL XMLs (for the AAFM Python Framework).</p><p>4. The 5 SPL XMLs (for SATIBEA).</p><p>5. The previous models are enriched with quality attributes measurements at feature and configuration levels.</p><p>6. A Microsoft Excel file with the scalability results obtained.</p><p> </p><p> </p><p>## Automated Reasoners</p><p> </p><p>- CQL IDE: https://github.com/CategoricalData/CQL</p><p>- Clafermoo: http://t3-necsis.cs.uwaterloo.ca:8092/</p><p>- AAFM Python Framework: https://pypi.org/project/famapy/</p><p>- SATIBEA: https://github.com/jmguo/SMTIBEA</p><p> </p><p> </p><p>## Requirements</p><p> </p><p>The data-set has been generated using Java JDK 18.0.2 for CQL IDE, Clafermoo, and SATIBEA, and Python 3.9.13 x86_64 for AAFM Python Framework.</p><p> </p><p>## Authors</p><p> </p><p>1. **[Daniel-Jesus Munoz](https://github.com/danieljmg)**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p><p>2. **Mónica Pinto**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p><p>3. **Lidia Fuentes**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p>
Quality-aware Analysis and Optimisation of Virtual Network Functions
<p><strong>To watch it in Youtube:</strong></p> <p><a href="https://youtu.be/RGwIVCgANcU">https://youtu.be/RGwIVCgANcU</a></p> <p><strong>This is the live presentation of a conference paper. Please, access and cite the published version:</strong></p> <p><a href="https://doi.org/10.1145/3546932.3547007">https://doi.org/10.1145/3546932.3547007</a></p> <p>The softwarisation and virtualisation of network functionality is the last milestone in the networking industry. <em>Software-Defined Networks</em> (SDN) and <em>Network Function Virtualization</em> (NFV) offer the possibility of using software to manage computer and mobile networks and build novel <em>Virtual Network Functions</em> (VNFs) deployed in heterogeneous devices. To reason about the variability of network functions and especially about the quality of a software product defined as a set of VNFs instantiated as part of a service (i.e., Service Function Chaining), a variability model along with a quality model is required.</p> <p>However, this domain imposes certain challenges to quality-aware reasoning of service function chains, such as numerical features or configuration-level <em>Quality Attributes</em> (QAs) (e.g., energy consumption). Incorporating numerical reasoning with quality data into SPL analyses is challenging and tool support is rare. In this work, we present 3 groups of operations: model report, aggregate functions to dynamically convert QAs at the feature-level into the configuration-level, and quality-aware optimisation. Our objective is to test the most complete reasoning tools to exploit the extended variability with quality attributes needed for VNFs.</p>
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