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109 results for “Microservices”
Towards Secure Management of Edge-Cloud IoT Microservices using Policy as Code
<p>This repository contains high quality images of all the Figures included in the paper "<span>Towards Secure Management of Edge-Cloud IoT </span><span>Microservices using Policy as Code".</span></p>
The Tale of Errors in Microservices (Artifact part 1)
<p>This dataset provides comprehensive microservice traces (around 1.4 million) collected from Uber microservice architecture, as described in our paper <strong>The Tale of Errors in Microservices</strong>, presented at <strong>SIGMETRICS 2025</strong>. This dataset enables researchers to study microservice behaviors, optimize performance, and investigate latency reduction techniques.</p> <p>The data has been sanitized to protect proprietary information while retaining critical performance characteristics for academic research.</p> <h3>Artifact Structure and Decompression Instructions:</h3> <p>Due to Zenodo's file size constraints and upload issues, the large <code>trace1-sanitized.tar.zst</code> and <code>trace2-sanitized.tar.zst</code> files have been split into multiple pieces. The artifact is available in two parts (<a href="#" rel="noopener">10.5281/zenodo.13947828</a> and <a href="https://doi.org/10.5281/zenodo.13952897">10.5281/zenodo.13952897</a>). To access the sanitized microservice traces, download all the split parts from both artifacts. After downloading, reassemble the files using the following commands and then decompress the <code>.zst</code> files individually. Each <code>.zst</code> file will require 300-500GB of disk space to decompress.</p> <h4>Reassembling the split files:</h4> <p>For <code>trace1-sanitized.tar.zst</code> and <code>trace2-sanitized.tar.zst</code>, use the following commands to reassemble them:</p> <div> <div><code>cat trace1_* > trace1-sanitized.tar.zst</code></div> <div><code>cat trace2_* > trace2-sanitized.tar.zst</code></div> </div> <p>Once reassembled, you can decompress the files:</p> <div> <div><code>zstd -d trace1-sanitized.tar.zst </code></div> <div><code>zstd -d trace2-sanitized.tar.zst </code></div> </div> <h4>Contents of the Traces:</h4> <ul> <li><code>trace1-sanitized.tar.zst</code> and <code>trace2-sanitized.tar.zst</code> (in <a href="#" rel="noopener">10.5281/zenodo.13952897</a>) contain around 1.4 million traces that correspond to the data described in Sections 3 and 4 of the original paper. <strong>Note:</strong> The traces in this dataset were collected on different days than those used in the paper, so analysis results may vary slightly from what is reported in the publication.</li> <li><code>driver-sanitized.tar.zst</code> contains the sanitized version of the original trace and corresponds to the <strong>App-Launch Use Case</strong> discussed in Section 6.3 and Figure 17 of the original paper.</li> </ul> <p><strong>Note</strong></p> <ul> <li>Due to privacy and security concerns, most unrelated fields and tags are removed. However, <strong>error-related</strong> tags are retained.</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/13956078">https://zenodo.org/records/13956078</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> <p>If you use the traces in your research, please cite our paper</p> <blockquote> <p><strong>The Tale of Errors in Microservices</strong><br><em>I-Ting Angelina Lee (Washington University in St. Louis); Zhizhou Zhang, Abhishek Parwal (Uber Technologies Inc.); Milind Chabbi (Uber Technologies)<br>SIGMETRICS 2025 <a href="https://doi.org/10.1145/3700436">https://doi.org/10.1145/3700436</a></em></p> </blockquote> <p>If you have more questions, you can reach out to <a href="https://lollllcat.github.io/">Chris(Zhizhou) Zhang</a>.</p>
Traces, Metrics, and Logs for Anomaly Detection and Root Cause Localization in Microservices
<p>Here are the data used in our paper published at ICSE 2023: </p><p>"Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data".<br><br>Please make sure to cite our paper whenever you use the data in your research:<br><br>@inproceedings{DBLP:conf/icse/LeeYCSL23, author = {Cheryl Lee and Tianyi Yang and Zhuangbin Chen and Yuxin Su and Michael R. Lyu}, title = {Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data}, booktitle = {45th {IEEE/ACM} International Conference on Software Engineering, {ICSE} 2023, Melbourne, Australia, May 14-20, 2023}, pages = {1750--1762}, publisher = {{IEEE}}, year = {2023}, url = {https://doi.org/10.1109/ICSE48619.2023.00150}, doi = {10.1109/ICSE48619.2023.00150}, timestamp = {Wed, 19 Jul 2023 10:09:12 +0200}, biburl = {https://dblp.org/rec/conf/icse/LeeYCSL23.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }</p>
Cohesion data of microservices of Spinnaker application
<p>This repository stores the cohesion data of the Spinnaker application.</p>
Multivocal Study on Microservice Dependencies
Open the record for dataset details and reuse information.
Open source data for microservice partitioning
<p>Open-source datasets for microservice partitioning.</p>
Additional Material for The Technical Debt Gamble: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture
Open the record for dataset details and reuse information.
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>
μjUniter: Automated Unification of Microservices into Monoliths
<p>Reproducibility package for ASE 2024.</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.