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.

In 2021, 85 percent of respondents from large organizations with 5,000 or more employees state currently using microservices. This suggests that larger organizations are perhaps more likely to benefit from and require microservice utilization in their operations.

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- Archived: 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

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 conditons.

The report covers Global Cloud Microservices Market Growth and it is segmented by deployment mode (platforms, services), enterprise size (small and medium enterprises, large enterprises), end-user industry (BFSI, retail, e-commerce, manufacturing, telecommunications, IT and ITes, healthcare, and other end-user industries), and geography (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa). The market sizes and forecasts are in terms of value (USD million) for all the above segments.

In 2021, 45 percent of respondents state that data analytics/business intelligence applications use microservices. Microservices are a cloud native architectural approach that typically have their own technology stack, such as data management model and database, and can communicate with one another via APIs.

The global microservices architecture market size was valued at USD 4.10 billion in 2022. It is estimated to reach USD 18.46 billion by 2031, growing at a CAGR of 18.2% during the forecast period (2023–2031). The rise of the In Report Scope:

According to Future Market Insights, the global microservices orchestration market size had reached US$ 649.4 million in 2018. Demand for microservices orchestration recorded Y-o-Y growth of 14.6% in 2022, and thus, the global market is expected to reach US$ 1,274.7 million in 2023. Over the projection period 2023 to 2033, microservices orchestration solutions sales in the global market is projected to exhibit 16.4% CAGR and total a market size of US$ 5,837.0 million by 2033-end.

Country-Wise Insights

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 US$ 3.7 Billion in 2023. By 2032, It will reach a value of US$ 11.8 Billion, growing at a CAGR of 13.4% during (2024-2032).

In 2021, 37 percent of respondents state using microservices partially. Microservices, or microservice architecture, is an architectural style that enables frequent and reliable delivery of applications.

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 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.

This dataset provides materials used and produced in the context of the research study leading to the article Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs. It includes materials used to conduct the study, as well as aggregated and anonymized data produced in its context.

We investigated how practitioners perceive the impact of 14 patterns on 7 quality attributes. In particular, we conducted 9 semi-structured interviews to collect industry expertise regarding (1) knowledge and adoption of software patterns, (2) the perceived architectural trade-offs of patterns, and (3) metrics professionals use to measure quality attributes.

Research Objective

Our objective with this work was to obtain insights on the relevance of design patterns in industry, how practitioners perceive their influence on software qualities as a consequence of their usage, and what metrics practitioners use, if any, to determine these derived effects, reflected as software qualities.

Research Questions

• RQ1: What is the rationale for the adoption of patterns in microservices systems?
• RQ2: How are QAs influenced as a result of applying microservice patterns?
• RQ3: How are QAs measured in microservices?

Interview Artifacts and Results

Materials used to conduct the study:

• interview-signup-form.pdf - Form used by participants to signup for the study. The data collected through this form was used to characterize the participant, and make an early assessment of her or his experience with software development and microservices.
• interview-guide.pdf - Notes used by the researcher to conduct the interviews, including the general structure to follow.
• interview-helper.pdf - Slides used by the researcher during the interviews, to illustrate each of the patterns.

Data produced in the context of the study:

• PreliminaryTradeoffAnalysis.csv - Benefits and liabilities gathered by the researchers from the description of the analyzed patterns. Those classified QAs as mixed when we considered that assigning a positive or negative value was a too simplistic judgement.
• Demographics.csv - Demographic data for each of the interviewees.
• InterviewsStats.csv - Simple statistics regarding the interviews, including number of words of the transcripts and duration of the interviews.
• Adoptions.csv - Reported patterns adopted by the interviewees.
• Gains.csv - Gains of each pattern as reported by interviewees.
• Pains.csv - Pains of each pattern as reported by interviewees.
• Indicators.csv - Reported indicators for measuring each QA.
• Techniques.csv - Reported techniques for addressing each QA.
• ThirdPartyMonitoringTools.csv - Reported third-party monitoring tools and the number of participants who use them.

This is the Bibliographic Dataset on Microservices and Security of the paper: Microservice Security: A Systematic Literature Review

This dataset provides the JAR files for the Smart Port microservices together with the EPL schema and patterns for the case study, the EPL schema and patterns used for the performance evaluation and the data collected from such evaluation for the paper entitled "A Microservice Architecture for Real-time IoT Data Processing: a Reusable Web of Things Approach for Smart Ports": - Smart Port microservices: it includes the JAR files for the three microservices (SmartPortTransformers, SmartPortCEP and SmartPortActions), the instructions for their deployment (readme.md) and the schema and patterns defined for the case study (SmartPortSchemaAndPatterns.txt).- Performance EPL schema and patterns: it includes the Esper EPL schema and patterns defined both for the short performance tests as well as for the long ones.- Performance evaluation results: it includes the spreadsheets response time values obtained from every performance test both for the short performance tests as well as for the long ones.

The global Cloud Microservices Market size is expected to reach USD 8.69 Billion in 2032 registering a CAGR of 22.9%. Our report provides a comprehensive overview of the industry, including key players, market share, growth opportunities and more.

Cloud Microservices Market size was valued at USD 6.4 billion in 2021 and is poised to grow from USD 7.76 billion in 2022 to USD 36.12 billion by 2030, growing at a CAGR of 21.2% in the forecast period (2023-2030).

Global Cloud Microservices Market Size By Components (Platform, Service), By End-Use Industry (Manufacturing, Telecom and IT), By Geographic Scope And Forecast