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109 results for “Microservices”
Microservice Security Detectors & Metrics & Detection Strategies: Dataset
<p>This is the dataset for replicability for the article "Detection Strategies for Microservice Security Tactics." It provides the code needed to replicate the study in the article and the model data set of 10 system models and 20 variants of those models.</p> <p> </p> <p>The abstract of the article is:</p> <p> </p> <p>Microservice architectures are widely used today to implement distributed systems. Securing microservice architectures is challenging because of their polyglot nature, continuous evolution, and various security concerns relevant to such architectures. This article proposes a novel, model-based approach providing detection strategies to address the automated detection of security tactics (or patterns and best practices) in a given microservice architecture decomposition model. Our novel detection strategies are metrics-based rules that decide conformance to a security recommendation based on a statistical predictor. The proposed approach models this recommendation using Architectural Design Decisions (ADDs). We apply our approach for four different security-related ADDs on access management, traffic control, and avoiding plaintext sensitive data in the context of microservice systems. We then apply our approach to a model data set of 10 open-source microservice systems and 20 variants of those systems. Our results are detection strategies showing a very low bias, a very high correlation, and a low prediction error in our model data set.<br> <br> The dataset is based on a dataset from a previous article: https://zenodo.org/record/6424722<br> </p>
Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
<h2>Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</h2><p>This is our replication package for our study on <i>Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</i>.</p><p>All scalability experiments are performed with the scalability benchmarking framework <a href="https://www.theodolite.rocks/">Theodolite</a> at <a href="https://www.se.informatik.uni-kiel.de/en/research/software-performance-engineering-lab-spel">Kiel University's Software Performance Engineering Lab (SPEL)</a> or Google Cloud.</p><p>With this replication package, we provide:</p><ul><li><a href="https://www.theodolite.rocks/concepts/benchmarks-and-executions.html">Benchmark execution files</a> in <i>executions</i>,</li><li>our benchmark (raw) results in <i>results</i>, and</li><li>analysis script for our results in <i>analysis</i>.</li></ul><h3>Repeating Benchmark Executions</h3><p>All our Theodolite executions are tailored to either the SPEL cluster or the Google Cloud.</p><h4>Kiel University's Software Performance Engineering Lab (SPEL)</h4><p>The SPEL cluster consists of 5 nodes, named <i>kube1-1</i> to <i>kube1-5</i> and labeled with <i>env=dev</i>. To run them in your local cluster, make sure to provide the same infrastructure or rename node selectors in the execution files accordingly.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml</p></blockquote><p>or for the vertical scalability experiment:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml -f se-cluster-dev-vertical.yaml</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h4>Google Cloud</h4><p>In the public cloud baseline experiments, the cluster consists of 5 e2-standard-32 nodes.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-dev.yaml</p></blockquote><p>For the experiments testing higher load intensities, the cluster consists of 4 e2-standard-16 nodes labeled with <i>type=infra</i> and 4 or 8 e2-standard-16 nodes with label <i>type=sut</i>. To install Theodolite in this cluster, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-stress.yaml</p></blockquote><p>In both cases, change the maximum load generated per load generator instance:</p><blockquote><p># Generate max. 100000 rec/sec per load generator instance export MAX_RECORDS_PER_INSTANCE=100000 kubectl patch benchmarks uc1-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]"</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h3>Repeating Results Analysis</h3><p>To inspect, repeat, or extend our results analysis, see <i>results</i> or run the corresponding notebooks in <i>analysis</i>.</p><p>For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.7 or 3.8 is required (e.g., in a virtual environment). Moreover, we require some Python libraries, which can be installed by:</p><blockquote><p>python3.8 -m venv .venv # source .venv/bin/activate pip install -r analysis/requirements.txt</p></blockquote><p> </p>
Towards a method for monitoring the coupling evolution of microservice-based architectures
<p>Backup video for the presentation of the paper "Towards a method for monitoring the coupling evolution of microservice-based architectures", published in SBCARS - CBSoft 2020</p>
Replication Package for: Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures
<p>This repository contains a replication package and experimental results for our study <em>Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures</em>.</p> <p>The following description can also be found in the README.md file.</p> <p><strong>Repeating Benchmark Execution</strong></p> <p><em><strong>The following introduction describes how to repeat our scalability experiments. If you plan to conduct your own studies, we suggest to use the latest version of <a href="https://github.com/cau-se/theodolite">Theodolite</a> with significantly enhanced usability.</strong></em></p> <p>The Apache Kafka Streams scalability experiments of our study were executed with <a href="https://github.com/cau-se/theodolite/tree/v0.1.2">Theodolite v0.1.2</a>. To repeat our Kafka Streams experiments:</p> <ol> <li>Clone and install <a href="https://github.com/cau-se/theodolite/tree/v0.1.2">Theodolite v0.1.2</a> according to the official documentation located in <code>execution</code>.</li> <li>Copy the file <code>repeat-kstream.sh</code> into Theodolite's <code>execution</code> directory.</li> <li>Run the repetition file with <code>./repeat-kstream.sh</code> from within the <code>execution</code> directory.</li> </ol> <p>Our Apache Flink benchmark implementations are currently migrated to the latest version of Theodolite. <a href="https://github.com/cau-se/theodolite/tree/apache-flink">Theodolite's <code>apache-flink</code> Branch</a> provides the basis for our Flink scalability experiments. To repeat them:</p> <ol> <li>Clone <a href="https://github.com/cau-se/theodolite/tree/apache-flink">Theodolite's <code>apache-flink</code> Branch</a> and install Theodolite according to the official documentation located in <code>execution</code> (should be identical to the installation for Kafka Streams (see above)).</li> <li>Copy the files <code>repeat-flink-without-checkpointing.sh</code> and <code>repeat-flink-with-checkpointing.sh</code> into Theodolite's <code>execution</code> directory.</li> <li>Switch to the <code>execution</code> directory.</li> <li>Run the first repetition file with <code>./repeat-flink-with-checkpointing.sh</code>.</li> <li>Disable checkpointing by reconfiguring the Kubernetes resources <code>jobmanager-job.yaml</code> and <code>taskmanager-job-deployment.yaml</code> for each benchmark (<code>uc{1,2,3,4}-application</code>) by setting the environment variable <code>CHECKPOINTING</code> to <code>"false"</code>.</li> <li>Run the second repetition file with <code>./repeat-flink-without-checkpointing.sh</code>.</li> </ol> <p><em>Please note that the naming of our benchmarks recently changed. While our publication already uses the new naming, the corresponding Theodolite versions are is still using the old one. Specifically, this means that UC1 in the publication is UC1 in Theodolite, UC2 in the publication is UC3 in Theodolite, UC3 in the publication is UC4 in Theodolite, and UC4 in the publication is UC2 in Theodolite.</em></p> <p><strong>Raw Measurements</strong></p> <p>The results of above benchmark execution can be found in the <code>measurements</code> directory. These are CSV files, containing the measured lag trend over time for a certain subexperiment. Theodolite creates a bunch of additional files, which serve for debugging and preliminary interpretation. As these files are not required for replication, we do not included them in this package.</p> <p>The CSV files are named according to the schema <code>exp{id}_{uc}_{load}_{inst}_totallag.csv</code>, where <code>{id}</code> represents the experiment ID, assigned by Theodolite, <code>{uc}</code> the benchmark name, <code>{load}</code> the generated load, and <code>{inst}</code> the number of evaluated instances.</p> <p>The CSV table <code>experiments.csv</code> provides an overview about the configurations used in each experiment.</p> <p><strong>Reproducing Scalability Analysis</strong></p> <p><em><strong>The following introduction describes how to repeat our scalability analysis, either with our measurements or with your own. If you plan to conduct your own studies, we suggest to use the latest version of <a href="https://github.com/cau-se/theodolite">Theodolite</a> with significantly enhanced usability.</strong></em></p> <p>Analyzing the Theodolite's measurements is done using two Jupyter notebooks. In general, these notebooks should be runnable by any Jupyter server. Python 3.7 or 3.8 is required (e.g., in a virtual environment) as well as some Python libraries, which can be installed via: <code>pip install -r requirements.txt</code>. See the <a href="https://github.com/cau-se/theodolite/tree/master/analysis">Theodolite documentation</a> for additional installation guidance.</p> <p><strong>Obtaining a Scalability Graph as a CSV File</strong></p> <p>The <code>scalability-graph.ipynb</code> notebook combines the measurements (i.e., the <code>totallag.csv</code> files) of one experiment. It produces a CSV file, which provides a mapping of load intensities to minimum required resources for that load (i.e., the scalability graph). The CSV files are named according to the schema <code>exp{id}_min-suitable-instances.csv</code>, where <code>{id}</code> represents the experiment ID. Additional guidance is provided in the notebook.</p> <p><strong>Resulting Scalability Graph CSV Files</strong></p> <p>The <code>results</code> directory provides the scalability graphs for all our executed experiments.</p> <p><strong>Visualization of the Scalability Graph</strong></p> <p>The <code>scalability-graph-plotter.ipynb</code> notebook creates PDF plots of a scalability graph and allows to combine multiple scalability graphs in one plot. It can be adjusted to match the desired visualization.</p> <p><strong>Acknowledgments</strong></p> <p>This research is funded by the German Federal Ministry of Education and Research (BMBF) under grant no. 01IS17084 and is part of the <a href="https://www.industrial-devops.org">Titan project</a>.</p>
Data from paper: Semi-automatic Feedback for Improving Architecture Conformance to Microservice Patterns and Practices
<p>All the data of the study <strong><em>Semi-automatic Feedback for Improving Architecture Conformance to Microservice Patterns and Practices. The file contains all the scripts and models of this study</em></strong></p>
Quantitative Reasoning of Microservice System Evolution
<p>This dataset contains the extracted data of Software Architecture Reconstruction process from TrainTicket testbench.</p><p>It includes the data and service view data, in addition to the metrics calculations.</p><p>This dataset is employed in a research paper with title: Assessing Microservice System Evolution through Quantitative Reasoning</p>
Prometheus stress testing data from the microservices-demo "sockshop" application
<p>Stress testing done with Locust, stressing the various microservice API endpoints available from the sockshop microservices demo found in https://microservices-demo.github.io/ </p><p>Part of a master's thesis project</p>
Artifacts of a Controlled Experiment About the Impact of Microservice API Patterns on Understandability
<p>This repository includes the data set of responses from 66 participants and the analysis scripts of a controlled experiment about the impact of microservice API patterns on understandability.</p>
Overview of the dependency taxonomy, dependencies from the literature, tools, and benchmarks relationships in Microservice Architecture.
<p>This figure is part of the publication titled "Multivocal Study in Microservice Dependencies".</p>
Linked Data Conversion using Microservices
<p>Conference talk at the LINCS 2021 conference describing the motivations, design, and development process behind the creation of the NSSI (NERVE Secure Scalable Infrastructure) data conversion platform.</p>
Automated Performance Regression Detection in Microservice Architectures - Raw Measurements
<p>These measurements contain a ZIP folder in which influxdb dumps of the several load testing runs of the corresponding bachelor's thesis "Automated Performance Regression Detection in Microservice Architectures" can be found. The dumps are named by date. The two dumps which are additionally named, contain the data sets which were used for the final evaluation.</p>
On a Microservice System Benchmark with Multiple Architected Variants for Software Engineering Research
Open the record for dataset details and reuse information.
Improving Comprehensibility of Event-Driven Microservice Architectures by Graph-Based Visualizations
<p>These are the anonymized protocols from our initial interviews and the raw data from the evaluation.</p>
An Empirical Study on Challenges of Event Management in Microservice Architectures
<p>Data set of the submitted manuscript "An Empirical Study on Challenges of Event Management in Microservice Architectures"</p>
Replication package for the paper: We Need to Talk About Microservices
<p>This is a replication package for the paper "We Need to Talk About Microservices", published at the mining challenge track of MSR'19.</p> <p> </p> <p>This package is composed of two files: data.zip and code.zip. On data.zip, one will find the raw and intermediate data for each and every step of our experimental methodology. On code.zip, one will find the source code we employed to perform our analysis.</p>
ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging
<p>Dataset for the paper: <em>ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging.</em></p> <div> </div>
Microservice migration survey and responses
<p>Survey sent out for our research on microservice migration of legacy systems, and the responses by the participants, as well as a thematic analysis of the open questions.</p>
Change Impact Analysis in Microservice Systems A Systematic Literature Review
<p>Reproduction package for Change Impact Analysis in Microservice Systems A Systematic Literature Review</p>
Microservice Semantic Dependency
<p>This dataset is associated with a publication on component-based semantic dependency detection methodology in microservices-based systems.</p> <p>It includes a prototype implementation and the generated semantic clones data derived from the TrainTicket v0.1.0 benchmark.</p>
[dataset] Automated Test-based Learning and Verification of PerformanceModels for Microservices Systems
<p>This repository contains the replication package of experiments presented in the research paper "Automated Test-based Learning and Verification of PerformanceModels for Microservices Systems" (M Camilli, A Janes, B Russo).</p>
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