Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
109
datasets available to search
ShareScore release 0.9.0
Dataset results
109 results for “Microservices”
A Quantitative Approach for the Assessment of Microservice Architecture Deployment Alternatives by Automated Performance Testing
<p>Reprodicibility package, raw data and results for the paper "A Quantitative Approach for the Assessment of Microservice Architecture Deployment Alternatives by Automated Performance Testing". </p>
ATOM: Model-Driven Autoscaling for Microservices
<p>This dataset release supports the results presented in the paper<br> "ATOM: Model-Driven Autoscaling for Microservices", by A. U. Gias, G. Casale and M. Woodside, accepted in IEEE International Conference on Distributed Computing Systems (ICDCS), 2019. </p> <p>When referring to the dataset please cite the paper above.</p> <p> </p>
Replication Package: Microservice-tailored Generation of Session-based Workload Models for Representative Load Testing
<p>This is the replication package for the publication <em>Microservice-tailored Generation of Session-based Workload Models for Representative Load Testing</em>, MASCOTS 2019. It holds the experiment setup, results, and detailed analyses of the results.</p> <p>The README.md (or README.pdf) contains further descriptions and instructions.</p>
Migration of Monolithic Systems to Microservices: A Systematic Mapping Study - Replication package
Open the record for dataset details and reuse information.
Data and Validation Package for "On microservice JIT architectural changes..."
<p>CIMET Tool output for each project labled "output-{project}.xlsx"</p> <p>CIMET Tool validation for each project labled "{project}-validation.xlsx"</p>
CRISP: Critical Path Analysis of Large-Scale Microservice Architectures (Artifact)
<p>This zip includes the original artifact used in the <a href="https://www.usenix.org/system/files/atc22-zhang-zhizhou.pdf">CRISP</a> paper.</p> <p>The appendix in the original paper contains the details on the setup and execution of the artifact. There are three components in this artifact: top-down analysis, bottom-up analysis, and anomaly detection.</p> <p><strong>Note</strong></p> <ul> <li>Due to privacy and security concerns, most unrelated fields and tags are removed.</li> <li>All trace is sanitized consistently. The same service or endpoint will have identical mapping across three directories. (i.e., service 1 represents the same service in all traces). However, the mapping is <strong>inconsistent</strong> with <a href="https://zenodo.org/records/13947828">https://zenodo.org/records/13947828</a>, so please do not mix the traces between the two artifacts. </li> <li>To preserve privacy, the start time of each trace has been randomly shifted. As a result, the start and end times in the traces do not reflect the actual collection times, and users should not attempt to infer when the traces were gathered.</li> <li>Within each trace, the relative durations and timestamps of all spans remained consistent, as the shift was applied uniformly across the entire trace.</li> </ul> <h3>Top-down analysis</h3> <p>Simply run <code>bash top-down.sh</code> should be sufficient. Note the result looks slightly different since this artifact contains a subset of traces from the original paper.</p> <h3>bottom-up analysis</h3> <p><code>bash bottom-up.sh</code> should generate the figure 13-20 from the original paper. Note instead of having 1m+ traces in the original paper, this artifact contains a subset of 100k traces. So generated figure may look slightly different.</p> <h3>Anomaly detection</h3> <p>This part contains the original repo of TraceAnomaly. The dataset contains the original trace of <strong>Service 3</strong> in <strong>Table 2</strong> from the original paper.</p> <p>To run the experiment, first please refer to the appendix for the setup.</p> <p>Then please run the following:</p> <ul> <li><code>bash preprocess.sh</code> to generate the matrices that are used in the training</li> <li>within <code>TraceAnomaly</code> repo <ul> <li>run <code>bash run-training-WG.sh</code> to run the training using the whole graph. Correspond to <strong>Service 3</strong> row, column <strong>TA</strong> in <strong>Table 2</strong>.</li> <li>run <code>bash run-training-CS.sh</code> to run the training using the critical path only. Correspond to <strong>Service 3</strong> row, column <strong>CRISP</strong> in <strong>Table 2</strong>.</li> </ul> </li> <li>run <code>python3.6 parse-rnvp.py -i path_to_rnvp_file</code> to check the result</li> </ul> <p>Note</p> <ul> <li>The traces among <code>train</code>, <code>normal</code>, and <code>abnormal</code> are split randomly from normal traces. The error generation part can be found in lines 137-144 in <code>preprocess.py</code>. It is also described in Section 7.3 Methodology in the original paper. For real error traces, please refer to <a href="https://zenodo.org/records/13947828">https://zenodo.org/records/13947828</a>.</li> </ul> <p>If you use the traces in your research, please cite our paper</p> <blockquote> <pre>@inproceedings{zhang2022crisp, title={$\{$CRISP$\}$: Critical path analysis of $\{$Large-Scale$\}$ microservice architectures}, author={Zhang, Zhizhou and Ramanathan, Murali Krishna and Raj, Prithvi and Parwal, Abhishek and Sherwood, Timothy and Chabbi, Milind}, booktitle={2022 USENIX Annual Technical Conference (USENIX ATC 22)}, pages={655--672}, year={2022} }</pre> </blockquote> <p>If you have more questions, you can reach out to <a href="https://lollllcat.github.io/">Chris(Zhizhou) Zhang</a>.</p>
Examining the use of Dynamic Data when undertaking Monolith to Microservice Architecture Migration
<p>A spreadsheet that shows the process of the Rapid Review protocol conducted in this MSc thesis.</p> <p> </p> <p>The IEEE search engine was used to retrieve quality, peer-reviewed papers. The search for primary studies in IEEE was restricted between 2015 and 2021 inclusive. The time frame commenced in 2015 as it was the year that microservice architecture started to gain traction in the industry and academia. Topics were identified such as; Monolith, Microservices, Migration and Dynamic Data. </p> <p>The selection procedure was based on the following criteria:</p> <ul> <li>The study must be in English.</li> <li>The study must be a primary study.</li> <li>The study must be in the context of migrating a monolith architecture to a microservice architecture using a dynamic data collection tool to gather dynamic data.</li> <li>The migration context of the study must be in Java as it is widely used when building enterprise software.</li> </ul> <p>The procedure was defined in 3 stages in which the set of criteria was applied. The first stage involved reading the title and keywords of the articles; the second stage involved reviewing the abstract of each article, and finally, the third stage involved a complete reading of each article.</p> <p> </p> <p> </p>
Examining the use of Dynamic Data when undertaking Monolith to Microservice Architecture Migration
<p>A spreadsheet that shows the selected studies from conducting the Rapid Review protocol in this MSc thesis. The steps can be seen in this <a href="https://zenodo.org/record/7174010#.Y2ZXkuzP0UF">link</a>.</p>
Roadmap to Reasoning in Microservice systems: A Rapid Review Dataset
<p>It contains data retrieved from Scopus based on the following query:</p> <p>TITLE-ABS-KEY ( ( microservices OR microservice OR micro-service ) AND architecture AND ( ( reconstruction OR "Intermediate Representation" ) OR reasoning OR ( evolution AND changes ) ) AND ( technique OR solution OR model ) ) AND PUBYEAR > 1959 AND PUBYEAR < 2023 AND PUBYEAR > 1959 AND PUBYEAR < 2023 </p> <p> </p> <p>The filter process is applied by the authors to build a microservice systems reasoning roadmap.</p>
Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment
<p>Supplemental Figures for <em>Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment</em></p>
Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment
<p>Case Study Results for <em>Detecting microservice anti-patterns using interactive service call graphs: Effort Assessment</em></p>
Online Survey: Impacts of Smells and Refactorings for Microservice Security
<p>This file contains the responses given to an online survey by practitioners and researchers working with microservice applications. The survey was aimed at measuring their agreement/disagreement with the possible impacts of smells and refactorings for microservices security [1], which were elicited by means of thematic analysis of both white and grey literature on the topic.</p> <p>[1] https://doi.org/10.1016/j.jss.2022.111393</p>
GNN-powered Approach to Decompose Monoliths to Microservices: A Case Study on Third Party Benchmark
<p>This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using an autoencoder with K-means (AE-K). The common classes are highlighted in green.</p>
GNN-powered Approach to Decompose Monoliths to Microservices: A Case Study on Third Party Benchmark
<p>This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using an autoencoder with C-means (AE-C). The common classes are highlighted in green.</p>
GNN-powered Approach to Decompose Monoliths to Microservices: A Case Study on Third Party Benchmark
<p>The results describe the similarity between the ftgo-microservice application and the decomposition predicted by three AI models.</p>
Classes in ftgo-microservice and VAE-C decomposition
<p>This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using a variational autoencoder with C-means (VAE-C). </p>
Classes in ftgo-microservice and AE-C decomposition
<p>This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using an autoencoder with C-means (AE-C). </p>
Classes in ftgo-microservice and AE-K decomposition
<p>This table shows the original classes in the ftgo-microservice application and the decomposition predicted by the approach using an autoencoder with K-means (AE-K). </p>
[dataset] Performance Analysis of Microservice Applications via Automated Load Testing and Bayesian Inference
<p>Anonymized replication package of the experiments presented in the research paper: "Performance Analysis of Microservice Applications via Automated Load Testing and Bayesian Inference".</p> <p>See the README.md file.</p>
Data set of ICSE 2021 paper submission "Static and Dynamic Analysis for the Migration of Monolith Systems to a Microservices Architecture"
<p>Data set of ICSE 2021 paper submission "Static and Dynamic Analysis for the Migration of Monolith Systems to a Microservices Architecture"</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.