This is a microservices dataset. For an exclusive explanation, please take a look at the paper and at the online appendix: https://github.com/darioamorosodaragona-tuni/Microservices-DatasetIn particular, this file contains all the projects labeled as:- Is it a microservices?: Yes | Uknown- Archived: Yes | NoCopyright:Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). MSR ’24, April 15–16, 2024, Lisbon, Portugal © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-0587-8/24/04 https://doi.org/10.1145/3643991.3644890

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.

The structure of the folder is as follows:

Each folder stores the respective data:

1. Logs:
   • original log file: LOGS_
   • parsed log files required for Loglizer (anomaly detection technique):
     • LOGS_
     • LOGS_
2. KPI Data: Monitoring_
3. Traces: Traces_

Dataset

This dataset was created by temp123$

Contents

A dataset containing system and service performance metrics, and user-facing quality metrics generated by running load tests against a microservice-based system under varying environmental and service configuration conditions.

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.

The abstract of the article is:

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.

The dataset is based on a dataset from a previous article: https://zenodo.org/record/6424722

Prior works have noted that existing public traces on anomaly detection and bottleneck localization in microservices applications only contain single, severe bottlenecks that are not representative of real-world scenarios. When such a bottleneck is introduced, the resulting latency increases by an order of magnitude (100x), making it trivial to detect that single bottleneck using a simple grid search or threshold-based approaches.

To create a more realistic dataset that includes traces with multiple bottlenecks at different intensities, we carefully benchmarked the social networking application under different interference intensities and duration of interference. We chose intensities and duration values that degrade the application performance but do not cause any faults or errors that can be trivially detected. We induced interference on different VMs at different times and also simultaneously. A single VM could be induced with different types of interference (e.g., CPU and memory), resulting in the hosted microservices experiencing a mixture of interference patterns. The resulting dataset consists of around 40 million request traces along with corresponding time series of CPU, memory, I/O, and network metrics. The dataset also includes application, VM, and Kubernetes logs.

A detailed description of the files is provided in the Data Explorer section. Please reach out to gagan at cs dot stonybrook dot edu if you have any questions or concerns.

If you find the dataset useful, please cite our WWW'24 paper "GAMMA: Graph Neural Network-Based Multi-Bottleneck Localization for Microservices Applications." Citation format (bibtex):

author = {Somashekar, Gagan and Dutt, Anurag and Adak, Mainak and Lorido Botran, Tania and Gandhi, Anshul},
title = {GAMMA: Graph Neural Network-Based Multi-Bottleneck Localization for Microservices Applications.},
year = {2024},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589334.3645665},
doi = {10.1145/3589334.3645665},
booktitle = {Proceedings of the ACM Web Conference 2024},
location = {Singapore},
series = {WWW '24}
}```

This dataset provides comprehensive microservice traces (around 1.4 million) collected from Uber microservice architecture, as described in our paper The Tale of Errors in Microservices, presented at SIGMETRICS 2025. This dataset enables researchers to study microservice behaviors, optimize performance, and investigate latency reduction techniques.

The data has been sanitized to protect proprietary information while retaining critical performance characteristics for academic research.

Artifact Structure and Decompression Instructions:

Due to Zenodo's file size constraints and upload issues, the large trace1-sanitized.tar.zst and trace2-sanitized.tar.zst files have been split into multiple pieces. The artifact is available in two parts (10.5281/zenodo.13947828 and 10.5281/zenodo.13952897). 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 .zst files individually. Each .zst file will require 300-500GB of disk space to decompress.

Reassembling the split files:

For trace1-sanitized.tar.zst and trace2-sanitized.tar.zst, use the following commands to reassemble them:

Once reassembled, you can decompress the files:

Contents of the Traces:

• trace1-sanitized.tar.zst and trace2-sanitized.tar.zst (in 10.5281/zenodo.13952897) contain around 1.4 million traces that correspond to the data described in Sections 3 and 4 of the original paper. Note: 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.
• driver-sanitized.tar.zst contains the sanitized version of the original trace and corresponds to the App-Launch Use Case discussed in Section 6.3 and Figure 17 of the original paper.

Note

• Due to privacy and security concerns, most unrelated fields and tags are removed. However, error-related tags are retained.
• 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 inconsistent with https://zenodo.org/records/13956078, so please do not mix the traces between the two artifacts.
• 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.
• Within each trace, the relative durations and timestamps of all spans remained consistent, as the shift was applied uniformly across the entire trace.

If you use the traces in your research, please cite our paper

The Tale of Errors in Microservices
I-Ting Angelina Lee (Washington University in St. Louis); Zhizhou Zhang, Abhishek Parwal (Uber Technologies Inc.); Milind Chabbi (Uber Technologies)
SIGMETRICS 2025 https://doi.org/10.1145/3700436

If you have more questions, you can reach out to Chris(Zhizhou) Zhang.

The Cloud Microservices Market Report is Segmented by Component (Platform, and Services), Enterprise Size (Small and Medium Enterprises, and Large Enterprises), End-User Industry (BFSI, Retail and E-Commerce, Manufacturing, IT and Telecom, Healthcare and Life Sciences, and More), Cloud Type (Public Cloud, Private Cloud, and Hybrid and Multi-Cloud), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Multivocal literature review for "From Microservice to Monolith", Ruoyu Su

Dataset

This dataset was created by Papa Moussa Sanogo

Released under Apache 2.0

Contents

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The Cloud Microservice Platform market is booming, projected to reach $15 billion in 2025 and grow at a 25% CAGR through 2033. This in-depth analysis explores market drivers, trends, restraints, key players (AWS, Microsoft, Google, etc.), and regional breakdowns. Discover the future of cloud-native application development.

Discover 313,037 companies using Containers And Microservices. Access firmographic data, tech stack intelligence, and buyer signals for Containers And Microservices users.

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