SoK: Microservice Architectures from a Dependability Perspective
- URL: http://arxiv.org/abs/2503.03392v1
- Date: Wed, 05 Mar 2025 11:12:58 GMT
- Title: SoK: Microservice Architectures from a Dependability Perspective
- Authors: Dāvis Kažemaks, Jérémie Decouchant,
- Abstract summary: Microservice architecture splits monolithic applications into smaller services that interact using lightweight communication schemes.<n>We explore the known faults and vulnerabilities that microservice architecture might suffer from, and the recent scientific literature that addresses them.
- Score: 0.8287206589886882
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The microservice software architecture leverages the idea of splitting large monolithic applications into multiple smaller services that interact using lightweight communication schemes. While the microservice architecture has proven its ability to support modern business applications, it also introduces new possible weak points in a system. Some scientific literature surveys have already addressed fault tolerance or security concerns but most of them lack analysis on the fault and vulnerability coverage that is introduced by microservice architectures. We explore the known faults and vulnerabilities that microservice architecture might suffer from, and the recent scientific literature that addresses them. We emphasize runtime detection and recovery mechanisms instead of offline prevention and mitigation mechanisms to limit the scope of this document.
Related papers
- Network Centrality as a New Perspective on Microservice Architecture [48.55946052680251]
The adoption of Microservice Architecture has led to the identification of various patterns and anti-patterns, such as Nano/Mega/Hub services.<n>This study investigates whether centrality metrics (CMs) can provide new insights into MSA quality and facilitate the detection of architectural anti-patterns.
arXiv Detail & Related papers (2025-01-23T10:13:57Z) - In-Context Experience Replay Facilitates Safety Red-Teaming of Text-to-Image Diffusion Models [104.94706600050557]
Text-to-image (T2I) models have shown remarkable progress, but their potential to generate harmful content remains a critical concern in the ML community.<n>We propose ICER, a novel red-teaming framework that generates interpretable and semantic meaningful problematic prompts.<n>Our work provides crucial insights for developing more robust safety mechanisms in T2I systems.
arXiv Detail & Related papers (2024-11-25T04:17:24Z) - An Infrastructure Cost Optimised Algorithm for Partitioning of Microservices [20.638612359627952]
As migrating applications into the cloud is universally adopted by the software industry, have proven to be the most suitable and widely accepted architecture pattern for applications deployed on distributed cloud.
Their efficacy is enabled by both technical benefits like reliability, fault isolation, scalability and productivity benefits like ease of asset maintenance and clear ownership boundaries.
In some cases, the complexity of migrating an existing application into the architecture becomes overwhelmingly complex and expensive.
arXiv Detail & Related papers (2024-08-13T02:08:59Z) - Microservice Vulnerability Analysis: A Literature Review with Empirical Insights [2.883578416080909]
We identify, analyze, and report 126 security vulnerabilities inherent in microservice architectures.
This comprehensive analysis enables us to (i) propose a taxonomy that categorizes microservice vulnerabilities based on the distinctive features of microservice architectures.
We also conduct an empirical analysis by performing vulnerability scans on four diverse microservice benchmark applications.
arXiv Detail & Related papers (2024-07-31T08:13:42Z) - Microservices-based Software Systems Reengineering: State-of-the-Art and Future Directions [17.094721366340735]
Designing software compatible with cloud-based Microservice Architectures (MSAs) is vital due to the performance, scalability, and availability limitations.
We provide a comprehensive survey of current research into ways of identifying services in systems that can be redeployed as Static, dynamic, and hybrid approaches have been explored.
arXiv Detail & Related papers (2024-07-18T21:59:05Z) - Systematic Mapping of Monolithic Applications to Microservices
Architecture [2.608935407927351]
It discusses the advantages of and the challenges that organizations face when transitioning from a monolithic system.
It presents a case study of a financial application and proposed techniques for identifying on monolithic systems using domain-driven development concepts.
arXiv Detail & Related papers (2023-09-07T15:47:11Z) - Enhancing Architecture Frameworks by Including Modern Stakeholders and their Views/Viewpoints [48.87872564630711]
The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks.<n>We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
arXiv Detail & Related papers (2023-08-09T21:54:34Z) - Heterogeneous Continual Learning [88.53038822561197]
We propose a novel framework to tackle the continual learning (CL) problem with changing network architectures.
We build on top of the distillation family of techniques and modify it to a new setting where a weaker model takes the role of a teacher.
We also propose Quick Deep Inversion (QDI) to recover prior task visual features to support knowledge transfer.
arXiv Detail & Related papers (2023-06-14T15:54:42Z) - AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study [8.026381963838272]
The use of AI in (MSs) is an emerging field as indicated by a substantial number of surveys.<n>We take an exhaustive approach to reveal all possible connections between the use of AI techniques for improving any quality attribute (QA) of MSs during the DevOps phases.
arXiv Detail & Related papers (2023-05-25T14:24:37Z) - Understanding the Issues, Their Causes and Solutions in Microservices
Systems: An Empirical Study [11.536360998310576]
Technical Debt, Continuous Integration, Exception Handling, Service Execution and Communication are the most dominant issues in systems.
We found 177 types of solutions that can be applied to fix the identified issues.
arXiv Detail & Related papers (2023-02-03T18:08:03Z) - Inspect, Understand, Overcome: A Survey of Practical Methods for AI
Safety [54.478842696269304]
The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings.
In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged.
Our paper addresses both machine learning experts and safety engineers.
arXiv Detail & Related papers (2021-04-29T09:54:54Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.