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 dataset titled "Greedy Multi-Cloud Selection Approach to Deploy an Application Based on Microservices" consists of 400,000 rows and 11 columns, capturing various parameters essential for deploying microservices-based applications across multiple cloud environments. This dataset is designed to simulate and analyze the deployment decisions and outcomes when using a greedy algorithm approach for cloud provider selection.

Columns Overview: Application ID: Unique identifier for each application instance. Microservice Name: Name of the microservice within the application. Cloud Provider: Chosen cloud provider for deploying the microservice (e.g., AWS, Azure, Google Cloud, IBM Cloud). Region: Geographic region where the cloud provider's data center is located (e.g., US-East, EU-West, Asia-Pacific). Resource Utilization (%): Percentage of allocated resources utilized by the microservice. Latency (ms): Average latency experienced by the microservice in milliseconds. Cost ($): Deployment cost in US dollars incurred by the microservice. Deployment Time (hrs): Time taken to deploy the microservice in hours. Success Rate (%): Percentage of successful deployments for the microservice. Data Transfer (GB): Amount of data transferred by the microservice in gigabytes. Environment: Deployment environment phase (e.g., Development, Testing, Production). Dataset Usage: This dataset facilitates research and analysis into the efficacy of a greedy algorithm for selecting optimal cloud providers based on various performance and cost metrics. Researchers and practitioners can use this dataset to:

Evaluate the impact of cloud provider choice on resource utilization and deployment costs. Analyze latency variations across different geographic regions and cloud providers. Assess the success rate of deployments and its correlation with selected cloud providers and deployment environments. Model and optimize deployment strategies for microservices-based applications in diverse cloud environments. Data Characteristics: The data ranges were simulated to reflect realistic scenarios encountered in multi-cloud deployments, ensuring variability in cloud provider performance and deployment outcomes. Random generation methods such as uniform distributions for costs and deployment times, normal distributions for latency, and categorical choices for cloud providers and deployment environments provide a diverse yet controlled dataset suitable for comprehensive analysis.

Potential Applications: This dataset is valuable for researchers, data scientists, and cloud architects involved in optimizing cloud resource utilization, minimizing deployment costs, and enhancing application performance through effective cloud provider selection strategies. It can also serve as a benchmark for comparing different algorithms and methodologies in the field of multi-cloud deployment and management.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

We are releasing the tracing dataset of four microservice benchmarks deployed on our dedicated Kubernetes cluster consisting of 15 heterogeneous nodes. The dataset is not sampled and is from selected types of requests in each benchmark, i.e., compose-posts in the social network application, compose-reviews in the media service application, book-rooms in the hotel reservation application, and reserve-tickets in the train ticket booking application. The four microservice applications come from DeathStarBench and Train-Ticket. The performance anomaly injector is from FIRM. The dataset was preprocessed from the raw data generated in FIRM's tracing system. The dataset is separated by on which microservice component is the performance anomaly located (as the file name suggests). Each dataset is in CSV format and fields are separated by commas. Each line consists of the tracing ID and the duration (in 10^(-3) ms) of each component. Execution paths are specified in execution_paths.txt in each directory.

This repository contains two datasets used for anomaly detection in microservices-based systems. The datasets are summarized versions of several CSV files containing data from normal and anomalous instances. The two datasets provided here are representative of service and application anomalies. For more detailed information, please refer to the README file included in the repository.

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.

Microservices is a popular architectural style for the development of distributed software, with an emphasis on modularity, scalability, and flexibility. Indeed, in microservice systems, functionalities are provided by loosely coupled, small services, each focusing on a specific business capability. Building a system according to the microservices architectural style brings a number of challenges, mainly related to how the different microservices are deployed and coordinated and how they interact. In this paper, we provide a survey about how techniques in the area of Artificial Intelligence have been used to tackle these challenges.

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.

The global microservices architecture market size reached USD 4.2 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 13.1 Billion by 2033, exhibiting a growth rate (CAGR) of 12.7% during 2025-2033. The increased demand for scalability, digital transformation initiatives, expanding e-commerce industry, and ongoing technological advancements are primarily driving the market's growth.

IMARC Group provides an analysis of the key trends in each segment of the global microservices architecture market report, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on component, deployment type, organization size, and industry vertical.

124 company records matching microservices architecture companies associated with the United States with size bands, recorded city locations, and verified-contact coverage. Compiled by RevenueBase from its company graph.

According to our latest research, the global BMS Microservices Architecture market size reached USD 1.98 billion in 2024, reflecting a robust momentum driven by the digital transformation of building management systems worldwide.

How often Microservices appears in Berlin data & AI job postings.

Kit drop-in per sviluppatori che costruiscono app AI/RAG/agent sui microservizi (decomposizione, service mesh, Saga, resilienza): golden eval set verificato, corpus RAG pulito, prompt di produzione (Claude/GPT/Cursor/Copilot) e data model con dataset etichettato.

According to Market Research Intellect, the microservices architecture market stood at USD 4.06 Billion in 2025 and is forecast to reach USD 18.08 Billion by 2035, progressing at a CAGR of 16.1.

3,204 company records matching microservices developers associated with the United States with size bands, recorded city locations, and verified-contact coverage. Compiled by RevenueBase from its company graph.

The Microservices market was valued at $7.42 billion in 2025 and is projected to reach $34.18 billion by 2034, growing at 18.5% CAGR.

According to our latest research, the global Microservices in Banking market size reached USD 2.65 billion in 2024, reflecting the rapid adoption of digital transformation strategies within the financial sector.

This is the dataset for the paper: Understanding the Issues, Their Causes and Solutions in Microservices Systems: An Empirical Study. The dataset is recorded in an MS Excel file which contains the following Excel sheets, and the description of each sheet is briefly presented below. (1) Selected Systems contains the 15 selected open source microservices systems with the color code and URL of each system. (2) Raw Data contains the information of initially retrieved 10,222 issues, including issue titles, issue links, issue open date, issue closed date, and the number of participants in each issue discussion. (3) Screened Issues contains the issues that meet the initial selection criteria (i.e., 5,115 issues) and the issues that do not meet the initial selection criteria (i.e., 5,107 issues). (4) Selected Issues (Round 1) contains the list of 5,115 issues that meet the initial selection criteria. (5) Selected Issues (Round 2) contains the issues related to RQs (i.e., 2,641 issues) and the issues not related to RQs (i.e., 2,474 issues). (6) Selected Issues contains the list of selected 2,641 issues, which were used to answer the RQs. (7) Initial Codes contains the initial codes for identifying the types of issues, causes, and solutions. We used these codes to further generate the subcategories and categories of issues, causes, and solutions. (8) Interview Questionnaire contains the interview questions we asked microservices practitioners to identify any missing issues, causes, and solutions, as well as to improve the proposed taxonomies. (9) Interview Results contains the results of interviews that we conducted to confirm and improve the developed taxonomies of issues, causes, and solutions. (10) Survey Questionnaire contains the survey questions we asked microservices practitioners through a Web-based survey to validate our taxonomies of issues, causes, and solutions. (11) Issue Taxonomy contains the detailed issue taxonomy consisting of 19 categories, 54 subcategories, and 402 types of issues. (12) Cause Taxonomy contains the detailed cause taxonomy consisting of 8 categories, 26 subcategories, and 228 types of causes. (13) Solution Taxonomy contains the detailed solution taxonomy consisting of 8 categories, 32 subcategories, and 177 types of solutions.

Microservices In Healthcare Market size was valued at USD 290.24 Million in 2024 and is projected to reach USD 1256.31 Million by 2032, growing at a CAGR of 20.10% during the forecast period 2026-2032. Global Microservices In Healthcare Market DriversThe Microservices in Healthcare Market is experiencing rapid growth, fundamentally driven by the industry's shift away from rigid, monolithic legacy systems toward flexible, modern IT infrastructure. Microservices architecture breaks down complex healthcare applications into smaller, independent services, enabling unprecedented levels of agility, scalability, and innovation. This architectural change is no longer a luxury but a necessity for providers and payers seeking to navigate the complexities of modern healthcare delivery, including compliance, patient engagement, and real-time data management. The key drivers below illustrate the compelling reasons healthcare organizations are making this critical technological transition.Need for Enhanced Interoperability and Real-Time Data Exchange: Seamless data interoperability is a primary market driver. Healthcare systems traditionally suffer from data silos, making it difficult for Electronic Health Records (EHRs), lab systems, and medical devices to communicate. Microservices, with their use of standardized APIs (Application Programming Interfaces), enable these disparate systems to exchange data efficiently and securely in real time. This capability is vital for supporting crucial initiatives like Health Information Exchanges (HIEs) and Value-Based Care models, ensuring providers have a comprehensive, up-to-the-minute view of a patient's medical history for better clinical decision-making. #HealthcareInteroperability #RealTimeData #FHIRDemand for Faster Innovation and Time-to-Market: The pressure to deliver rapid innovation is significantly boosting microservices adoption. Traditional monolithic applications require lengthy, complex deployment cycles for even minor updates, slowing down the launch of new patient-facing features or regulatory compliance updates. Microservices allow small, independent development teams to develop, test, and deploy services autonomously.

This dataset contains scraped data from 11 open-source repositories that implement microservices architecture. The data includes issues, commits, and pull requests, providing insights into the collaborative development processes, version control activities, and maintenance efforts in microservices-based projects. The dataset is intended for research purposes in software engineering, repository mining, and data analysis.