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109 results for “Microservices”
Adaptation in Microservice-based Systems: A Systematic Literature Review
<p>Data extraction from the paper: Adaptation in Microservice-based Systems: A Systematic Literature Review.</p> <p>The attached file contains the result of data extraction from the primary studies following the defined extraction criteria, where: </p> <ul> <li><strong>1</strong>, corresponds to the property option that was identified in the study,</li> <li><strong>NS</strong> denotes that the property was specified in the study, and</li> <li><strong>Ad.</strong> denotes an additional option to the main one identified for that particular property.</li> </ul>
Software Architecture Reconstruction for Microservice Systems Using GraalVM Native Image
<p>This Dataset contains the System Architecture Reconstruction for Train-Ticket microservice benchmark.</p> <p>It includes: Manual Analysis, GraalVM Analysis, and Source code Analysis.</p> <p>It's described in a paper with title: Software Architecture Reconstruction for Microservice Systems Using GraalVM Native Image</p> <p> </p>
Microservices Refactoring Tools - Paper Appendix
<p>This data set contains the rapid review results and tool inspection details for the study "Tools for Refactoring to Microservices:<br> A Preliminary Usability Report" </p>
Microservice Anti-Patterns and Bad Smells: A Tertiary Study
<p>This dataset contains the search and classification processes of anti-patterns and bad smells catalog tertiary study.</p>
Extracting the Architecture of Microservices: An Approach for Explainability and Traceability
<p>Dataset and replication package</p>
Enhancing Microservice Reusability in MSA through the Reusable Microservices Framework: Development, Validation, and Evaluation
<p>The data of the paper: Enhancing Microservice Reusability in MSA through the Reusable Microservices Framework: Development, Validation, and Evaluation</p>
Microservice ChatGPT Reasoning
<p>This dataset contains:</p> <p>- The source code data extraction from TrainTicket testbench V0.1.0.</p> <p>- The PO-CCG (Persistence Operation aware Component Call Graph) data extraction of TrainTicket V0.1.0.</p> <p>- The questions/ answers and the evaluation of ChatGPT answers.</p>
Docker compose files to analyze structural patterns of containerized microservice applications
<p>The dataset contains 137 docker-compose files found in publicly available repositories on Github. This dataset was used to analyze the structural patterns of containerized microservice applications to figure out how the structure of microservice applications in benchmarks could be improved to resemble more real world structures.</p> <p>For more information and citation please refer to our paper:</p> <p>Vladimir Podolskiy, Maria Patrou, Panos Patros, Michael Gerndt, and Kenneth B. Kent. 2020. The weakest link: revealing and modeling the architectural patterns of microservice applications. In <em>Proceedings of the 30th Annual International Conference on Computer Science and Software Engineering</em> (<em>CASCON '20</em>). IBM Corp., USA, 113–122. https://dl.acm.org/doi/10.1145/3132747.3132772 </p>
Application Deployment using Containers with Auto-scaling for Microservices in Cloud Environment
<p>This dataset release supports the results presented in the paper "Application Deployment using Containers with Auto-scaling for Microservices in Cloud Environment" by S. N. Srirama, M. Adhikari, and S. Paul, accepted in Journal of Network and Computer Applications, ISSN: 1084-8045, vol. 160, pp. 1-20, 2020. Elsevier.</p> <p>When referring to the dataset please cite the paper above.</p>
Performance Anomaly Detection in Microservice Architectures under Continuous Change
<p>Supplementary material for the master's thesis<br> <strong>"Performance Anomaly Detection in Microservice Architectures under Continuous Change"</strong><br> of<br> <strong>Thomas F. Düllmann</strong></p> <p><br> The folder for the supplemental material is structured as follows:.</p> <ul> <li><strong>abstract-de.txt</strong> (abstract in german)</li> <li><strong>abstract-en.txt</strong> (abstract in english)</li> <li><strong>01-MicroserviceMetamodel</strong> (Eclipse project containing the Ecore meta model and the Xtend generation template)</li> <li><strong>02-AnomalyDetectionImplementation</strong> (Eclipse project containing the implementation of the customized RanCorr approach and the EAR approach) <ul> <li><strong>EARExperimentSetup</strong> (the Evaluation Setup that is run based on the input data from the experiment and the EAR implementation)</li> <li><strong>Kieker</strong> (Kieker and the customized RanCorr approach)</li> </ul> </li> <li><strong>03-ExperimentTools</strong> (supplementary microservices and scripts that were used for the experiment setup. The generated services need to be placed next to these files and have to have the folder prefix "gen-") <ul> <li><strong>jmeter</strong> (the microservice that is used for generating load)</li> <li><strong>jmsserver</strong> (the microservice that runs ActiveMQ to bundle the monitoring logs from the services)</li> <li><strong>monitoringserver</strong> (the microservice that collects the monitoring data from the jmsserver microservice and stores them)</li> <li><strong>registry</strong> (the microservice that is responsible for managing the delays that should be injected)</li> <li><strong>copyResults.sh</strong> (simple bash script that masks the scp command to copy the monitoring data from the monitoringserver to the local file system) </li> <li><strong>deployPackage.sh</strong> (bash script that uploads the docker images to defined remote systems via ssh and initiates the start of the microservices on a Kubernetes cluster using the said docker images)</li> <li><strong>dockerinit.sh</strong> (bash script that goes into the microservice folders to compile and package them and create the corresponding Docker images (useful if running minikube for example))</li> <li><strong>kubeinit.sh</strong> (bash script that starts the microservices on a Kubernetes cluster that is associated with the kubectl Kubernetes tool)</li> <li><strong>kubeclean.sh</strong> (bash script that removes the microservices from a Kubernetes cluster that is associated with the kubectl Kubernetes tool) </li> </ul> </li> <li><strong>04-EvaluationData</strong> <ul> <li><strong>RawData </strong> (the raw data that was extracted from the experiment environment) <ul> <li>kieker-monitoring-data (the Kieker monitoring data obtained from the experiment setup)</li> <li><strong>anomalies.log</strong> (the log data that shows the injected real anomalies)</li> <li><strong>events.log</strong> (log file that contains the timesstamps, the scope and the type of event injections)</li> <li><strong>registry.log</strong> (registry microservice log showing the injections of real and change anomalies)</li> </ul> </li> <li><strong>Results</strong> (results for the evaluation with different thresholds) <ul> <li><strong>*folders*</strong> (contain the results of the anomaly detection using the threshold represented by the folder name)</li> <li><strong>results.csv</strong> (the calulated results in terms of TP/FN/FP/FN for every approach with every threshold)</li> <li><strong>results-calculated.csv</strong> (further metrics that were calculated based on the TP/FN/FP/FN values)</li> </ul> </li> </ul> </li> <li><strong>05-EvaluationServices</strong> (the folders containing the microservices that were used for the evaluation)</li> </ul> <p> </p>
Supplemental package of a study on Microservices versus Monoliths: Identifying Challenges and Proposing Practical Recommendations
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Microservice Metrics Dataset
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LO2: Microservice Dataset of Logs and Metrics
<div> <h2>LO2 dataset</h2> <p>This is the data repository for the LO2 dataset.</p> <p>Here is an overview of the contents.</p> <p><strong>lo2-data.zip</strong></p> <p>This is the main dataset. This is the completely unedited output of our data collection process. Note that the uncompressed size is around 540 GB. For more information, see the paper and the data-appendix in this repository.</p> <p><strong>lo2-sample.zip</strong></p> <p>This is a sample that contains the data used for preliminary analysis. It contains only service logs and the most relevant metrics for the first 100 runs. Furthermore, the metrics are combined on a run level to a single csv to make them easier to utilize. </p> <p><strong>data-appendix.pdf</strong></p> <p>This document contains further details and stats about the full dataset. These include file size distributions, empty file analysis, log type analysis and the appearance of an unknown file.</p> <p><strong>lo2-scripts.zip</strong></p> <p>Various scripts for processing the data to create the sample, to conduct the preliminary analysis and to create the statistics seen in the data-appendix.</p> <ul> <li><strong>csv_generator.py, csv_merge*.py: </strong>These scripts create and combine the metrics into csv files. They need to be run in order. Merging runs to global is very memory intensive.</li> <li><strong>findempty.py: </strong>Finds empty files in the folders. As some are expected to be empty, it also counts the unexpected ones. Used in data-appendix.</li> <li><strong>loglead_lo2.py: </strong>Script for the preliminary analysis of the logs for error detection. Requires LogLead version 1.2.1.</li> <li><strong>logstats.py: </strong>Counts log lines and their type. Used for creating the figure of number of lines per type and service.</li> <li><strong>node_exporter_metrics.txt: </strong>Metric descriptions exported from Prometheus (text file).</li> <li><strong>pca.py: </strong>The Principal Component Analysis script used for preliminary analysis.</li> <li><strong>reduce_logs.py: </strong>Very important for fair analysis as in the beginning of the files there are some initialization rows that leak information regarding correctness.</li> <li><strong>requirements.txt: </strong>Required Python libraries to run the scripts.</li> <li><strong>sizedist.py: </strong>Creating distributions of file sizes per filename for the data-appendix.</li> </ul> <p><em>Version v3: Updated data appendix introduction, added another stage in the log analysis process in loglead_lo2.py</em></p> </div>
ARCHI4MOM: Using Tracing Information to Extract the Architecture of Microservice-based Systems from Message-oriented Middleware
<p>The data published here are relevant for ESCA_2022 publication. </p>
Adopting Microservices and DevOps in the Cyber-Physical Systems Domain: A Rapid Review and Case Study at Siemens AG
<p>This repository contains the artifacts for a rapid review and interview-based case study at Siemens AG. We analyzed challenges and practices related to microservices and DevOps in the context of the cyber-physical systems (CPS) domain.</p>
Anomalies in Microservice Architecture (train-ticket) based on version configurations
<p>The work contains ten datasets containing monitoring data (logs, Jaeger Traces and Prometheus KPI data). The datasets contain monitoring data from train-ticket, a benchmark system for microservices. The dataset includes a short description with explanations of identified anomalies.</p> <p>The structure of the folder is as follows:</p> <p><strong><changed-microservice>_<third-party-library>_<version>_<date-when-data-was-collected></strong></p> <p>Each folder stores the respective data:</p> <ol> <li>Logs: <ul> <li>original log file: LOGS_<change-microservice>_<third-party-library>_<Version>.txt</li> <li>parsed log files required for Loglizer (anomaly detection technique): <ul> <li>LOGS_<change-microservice>_<third-party-library>_<Version>.txt_structured.csv</li> <li>LOGS_<change-microservice>_<third-party-library>_<Version>.txt_templates.csv</li> </ul> </li> </ul> </li> <li>KPI Data: Monitoring_<change-microservice>_<third-party-library>_<Version></li> <li>Traces: Traces_<change-microservice>_<third-party-library>_<Version></li> </ol> <p> </p> <p> </p> <p> </p>
Additional Material for Beyond SonarQube: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture
<p>This upload contains the replication package for the study "Beyond SonarQube: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture" submitted for review on the ECSA 2024 Industry Track.<br><br>It contains the following:<br>Focus group-plan.pdf - The plan for the focus group discussion created by the authors.<br>Focus group slides.pdf - The slides used during the focus group discussion.<br>SonarQube measurement results.xlsx - Detailed results of the SonarQube analysis for each microservice in the researched system.</p>
On a Microservice System Benchmark with Multiple Architected Variants for Software Engineering Research
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μjUniter: Automated Unification of Microservices into Monoliths
<p>Reproducibility package for ASE 2024.</p> <p>A tool for automating the migration from Java Spring-Boot microservice systems to monolith systems.</p> <p>/mjuniter is the main research product. Can be packaged with mvn and then run with java. The output monolith can be run with mvn spring-boot:run or as a container after proper configurations are made.</p> <p>/rmgenerator is the synthetic microservice generator. Can be packaged with mvn and then run with java. Output microservices can be packaged with maven, and deployed as containers with docker.</p> <p>gucc.py is a python script used to automate some of the building and testing pipeling for the synthetically generated microservices. It generates, unites, compares the lines of code, and compiles the tools. It depends on the presence of the CLOC executable availabl on windows at <a href="https://github.com/AlDanial/cloc">https://github.com/AlDanial/cloc</a>.</p> <p>The microservice systems generated by rmgenerator can get very large and were not included as they can be easily reproduced.</p> <p>The third party example is available at <a href="https://github.com/piomin/sample-spring-microservices-new/tree/master/organization-service/src/main/java/pl/piomin/services/organization">https://github.com/piomin/sample-spring-microservices-new/tree/master/organization-service/src/main/java/pl/piomin/services/organization</a>.</p>
Analysis of System-Oriented Testing Based on Microservices Architecture: A Systematic Literature Review
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.