Microservice Systems Research Articles

ASRMG: Business Topic Clustering‐Based Architecture Smell Refactoring for Microservices Granularity

ABSTRACTIntroductionMicroservices architecture is one of the most popular design approaches in software development. The granularity smell of microservices is a topic of great interest, which often leads to a degradation in the quality of the microservices architecture, so it needs to be eliminated through architecture refactoring. Existing research on architecture refactoring to address granularity smells in microservices is limited, with a lack of consideration for the semantic information of business logic, and suggestions for microservice refactoring rely too much on manual experience and lack standardized descriptions.ObjectivesThis paper aims to provide a novel approach for refactoring granularity smells in microservice architectures, addressing the existing shortcomings in considering semantic information of microservice business logic, the reliance on empirical experience for refactoring suggestions, and the lack of standardized suggestions for refactoring.MethodsThis paper introduces a novel method for refactoring granularity smells in microservices architecture based on business topic clusters, named ASRMG. This method extracts business topic clusters from the business logic code of interfaces, thereby defining the cohesion and coupling semantics of the system. It then employs an enhanced genetic algorithm for refactoring the microservices system, using a refactoring pattern database to automate the generation of fine‐grained refactoring suggestions.ResultsExperiments conducted on five open‐source microservices systems of varying scales and domains achieved a 99.24% elimination rate of granularity smells, with cohesion metrics improving by an average of 60.19% and coupling metrics reducing by an average of 15.32%, indicating a significant enhancement in the quality of microservice architecture.ConclusionThis paper introduces a novel method for refactoring and evaluating microservice systems, named ASRMG. ASRMG extracts business topic clusters from the code of microservice systems and refines the evaluation method for refactored microservice, and proposes an automated method for generating refactoring suggestions for granularity smells, enhancing the efficiency and quality of architecture smell refactoring.

Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis

Widely adopted for their scalability and flexibility, modern microservice systems present unique failure diagnosis challenges due to their independent deployment and dynamic interactions. This complexity can lead to cascading failures that negatively impact operational efficiency and user experience. Recognizing the critical role of fault diagnosis in improving the stability and reliability of microservice systems, researchers have conducted extensive studies and achieved a number of significant results. This survey provides an exhaustive review of 98 scientific papers from 2003 to the present, including a thorough examination and elucidation of the fundamental concepts, system architecture, and problem statement. It also includes a qualitative analysis of the dimensions, providing an in-depth discussion of current best practices and future directions, aiming to further its development and application. In addition, this survey compiles publicly available datasets, toolkits, and evaluation metrics to facilitate the selection and validation of techniques for practitioners.

Enhancing Stock Market Forecasting Through a Service-Driven Approach: Microservice System

Enhancing Stock Market Forecasting Through a Service-Driven Approach: Microservice System

Microservices, a Definition Analyzed by ßMACH

Microservices are an answer to various scalability challenges. They enable building large and complex systems by scaling the number of services, and development teams. Microservices allow self-management and agile processes such as Scrum. Nevertheless, a minimal management process should also be defined and documented. Documentation must be easily understandable and applicable to the developing teams to foster efficiency. We propose to use the ßMACH method, a software management guidance. The result is a minimal and systematic description. Based on the definition obtained using ßMACH, we can state that the service and team isolation is essential for a scalable microservice system. In addition, we introduce ßMACH and show how to document a software management process. The documentation is easy to understand by software developers and interesting for software engineers.

SYNCHRONIZATION OF EVENT-DRIVEN MANAGEMENT DURING DATA COLLECTION

The article considers an approach to implementing the architecture of a microservice system for processing large volumes of data based on the event-oriented approach to managing the sequence of using individual microservices. This becomes especially important when processing large volumes of data from information sources with different performance levels when the task is to minimize the total time for processing data streams. In this case, as a rule, the task is to minimize the number of requests for information sources to obtain a sufficient amount of data relevant to the request. The efficiency of the entire software system as a whole depends on how the microservices that provide extraction and primary processing of the received data are managed. To obtain the required amount of relevant data from diverse information sources, the software system must adapt to the request during its operation so that the maximum number of requests are directed to sources that have the maximum probability of finding the data necessary for the request in them. An approach is proposed that allows adaptively managing the choice of microservices during data collection and by emerging events and, thus, forming a choice of information sources based on an assessment of the efficiency of obtaining relevant information from these sources. Events are generated as a result of data extraction and primary processing from certain sources in terms of assessing the availability of data relevant to the request in each of the sources considered within the framework of the selected search scenario. Event-oriented microservice architecture adapts the system operation to the current loads on individual microservices and the overall performance by analyse the relevant events. The use of an adaptive event-oriented microservice architecture can be especially effective in the development of various information and analytical systems constructed by real-time data collection and design scenarios of analytical activity. The article considers the features of synchronous and asynchronous options in the implementation of event-oriented architecture, which can be used in various software systems depending on their purpose. An analysis of the features of synchronous and asynchronous options in the implementation of event-oriented architecture, their quantitative parameters, and features of their use depending on the type of tasks is carried out.

Assessing Evolution of Microservices Using Static Analysis

Microservices have gained widespread adoption in enterprise software systems because they encapsulate the expertise of specific organizational subunits. This approach offers valuable insights into internal processes and communication channels. The advantage of microservices lies in their self-contained nature, streamlining management and deployment. However, this decentralized approach scatters knowledge across microservices, making it challenging to grasp the holistic system. As these systems continually evolve, substantial changes may affect not only individual microservices but the entire system. This dynamic environment increases the complexity of system maintenance, emphasizing the need for centralized assessment methods to analyze these changes. This paper derives and introduces quantification metrics to serve as indicators for investigating system architecture evolution across different system versions. It focuses on two holistic viewpoints of inter-service interaction and data perspectives derived through static analysis of the system’s source code. The approach is demonstrated with a case study using established microservice system benchmarks.

Retriever: A view-based approach to reverse engineering software architecture models

Software systems, which have become an integral part of our daily lives, are constantly evolving and growing in complexity. The challenge of understanding and managing these systems has led to a shift towards automated extraction of models from software artifacts. However, extracting architectural models for web service or microservice systems is challenging due to the heterogeneity of formats, languages, and semantics. This challenge arises from the independent deployment and loose coupling of services, as well as the diverse technologies and platforms that comprise the heterogeneous artifacts. This paper addresses this issue with two key ideas: a knowledge representation model for diverse artifacts and a framework for integrating individual views into a unified architectural model. The former involves model-to-model transformations that consider technology-specific relationships and concepts. At the same time, the latter uses model transformation and composition techniques to create a unified model based on an existing metamodel. This work goes beyond the current state of the art by introducing an approach for reconstructing component-based software architectures and combining model-driven reverse engineering processes to capture information across multiple views. As empirical evidence of its applicability and accuracy, we report on its successful application in real projects of different sizes. Our evaluation shows scalability in generating architectural models and can reconstruct the components of a software system and their interactions with F1 values up to 0.94 for structural properties and up to 0.88 for behavioral properties.

UAC-AD: Unsupervised Adversarial Contrastive Learning for Anomaly Detection on Multi-Modal Data in Microservice Systems

UAC-AD: Unsupervised Adversarial Contrastive Learning for Anomaly Detection on Multi-Modal Data in Microservice Systems

ITCRL: Causal-Intervention-Based Trace Contrastive Representation Learning for Microservice Systems

iTCRL: Causal-Intervention-Based Trace Contrastive Representation Learning for Microservice Systems

Multilayered Fault Detection and Localization With Transformer for Microservice Systems

Multilayered Fault Detection and Localization With Transformer for Microservice Systems

Diagnosing Performance Issues for Large-Scale Microservice Systems With Heterogeneous Graph

Diagnosing Performance Issues for Large-Scale Microservice Systems With Heterogeneous Graph

ChatOps for microservice systems: A low-code approach using service composition and large language models

The Microservice Architecture (MSA) plays a pivotal role in contemporary e-business, promoting service independence, autonomy, and continual evolution in line with the principles of DevOps. However, the distributed nature of the MSA introduces additional complexity, which requires familiarity with multiple DevOps (Development and Operations) tools, thereby increasing the learning curve. This paper presents a specialized ChatOps (Chat Operations) approach that allows MSA developers to compose new ChatOps capabilities in a low-code way (i.e., with minimal coding). The proposed ChatOps4Msa approach leverages established ChatOps functionalities to facilitate the real-time monitoring of service status, conduct service testing, track issues, and receive alerts using natural language or the proposed ChatOps Query Language (CQL). The use of large language models (LLMs) for functional intents also enhances the usability of the DevOps toolchain in microservices systems to streamline implementation.

HeMiRCA: Fine-Grained Root Cause Analysis for Microservices with Heterogeneous Data Sources

Microservices architecture improves software scalability, resilience, and agility but also poses significant challenges to system reliability due to their complexity and dynamic nature. Identifying and resolving anomalies promptly is crucial because they can quickly propagate to other microservices and cause severe damage to the system. Existing root-cause metric localization approaches rely on metrics or metrics-anomalies correlations but overlook other monitoring data sources ( e.g. , traces). We are the first to identify and leverage the anomaly-aware monotonic correlation between heterogeneous monitoring data, motivated by which we propose a novel framework, HeMiRCA , for hierarchical root cause analysis using Spearman correlation. HeMiRCA is based on the key observation that the microservice responsible for a particular type of fault exhibits a monotonic correlation between the trends of its associated metrics and the trace-based anomaly score of the system. HeMiRCA first calculates time-series anomaly scores using traces and then exploits the correlations between multivariate metrics and the scores to rank the suspicious metrics and microservices. HeMiRCA has been evaluated on two datasets collected from widely used microservice systems. The results show that HeMiRCA outperforms the state-of-the-art approaches by a large margin in identifying root causes at both service level and metric level, achieving a top-1 hit ratio of 82.7% and 74% on average, respectively.

Methods for detecting anomalies in microservices using statistical analysis

The subject of the study is methods of detecting anomalies in microservices using statistical analysis. Microservices is a popular software development architecture that allows for flexible and scalable systems. However, due to their complexity, such systems can be vulnerable to various types of anomalies that can affect their performance and reliability. The goal of the work is an analytical review of existing methods of detecting anomalies in microservice systems using statistical analysis methods. Detection of anomalies is critical to ensure stable system operation and quick response to possible problems. To achieve the purpose, the following tasks are defined: review of methods for detecting anomalies in microservices; description of the principles of regression analysis, cluster analysis and the method of principal components; comparison of methods according to the criteria of efficiency, computational complexity, resistance to noise and adaptability; recommendations for choosing a method and the possibility of combining them; summary of results and identification of directions for future research. A method for detecting anomalies in microservices is considered, which includes regression analysis, cluster analysis, and the method of principal components (PCA). The results of the study confirmed that each method has its advantages and limitations. Regression analysis is effective in systems with clear trends, but less effective in complex and dynamic systems. Cluster analysis has proven to be robust to noise and capable of detecting both individual anomalies and groups of anomalous events but requires significant computational resources. The method of principal components (PCA) is a powerful tool for the analysis of high-dimensional data, but it has limitations in the high complexity of calculations and interpretation of results. Each of the considered methods has its pros and cons, so the study proposed a new method that would consist in combining them. The conclusions emphasize the importance of statistical analysis for monitoring microservice systems. Well-chosen data analysis techniques facilitate the detection of anomalies in complex environments such as microservices. The use of regression analysis, cluster analysis and the method of principal components allows you to get a deep insight into the operation of the system. However, for best results, it is recommended to combine different methods and analyze their results in the context of a specific system. This approach provides greater resistance to anomalies and faster response to them in microservice architectures.

MicroIRC: Instance-level Root Cause Localization for Microservice Systems

The use of microservice architecture is gaining popularity in the development of web applications. However, identifying the root cause of a failure can be challenging due to the complexity of interconnected microservices, long service invocation links, dynamic changes in service states, and the abundance of service deployment nodes. Furthermore, as each microservice may have multiple instances, it can be difficult to identify instance-level failures promptly and effectively when the microservice topology and failure types change dynamically. To address this issue, we propose MicroIRC (Instance-level Root Cause Localization for Microservice Systems), a novel metrics-based approach that localizes root causes at the instance level while exhibiting robustness to adapt to dynamic changes in topology and new types of anomalies. We begin by training a graph neural network to fit different root cause types based on extracted time series features of microservice system metrics. Next, we construct a heterogeneous weighted topology (HWT) of microservice systems and execute a personalized random walk to identify root cause candidates. These candidates, along with real-time metrics from the anomalous time window, are then fed into the trained graph neural network to generate a ranked root cause list. Experiments conducted on five real-world datasets demonstrate that MicroIRC can accurately locate the root cause of microservices at the instance level, achieving a precision rate of 93.1% for the top five results. Furthermore, compared to the state-of-the-art methods, MicroIRC can improve the accuracy of root cause localization by more than 17% at the service level and more than 11.5% at the instance level. Remarkably, it exhibits robustness in scenarios involving new failure types, achieving an accuracy of 84.2% for the top result amid dynamic topological changes.

Enhancing fault localization in microservices systems through span-level using graph convolutional networks

Enhancing fault localization in microservices systems through span-level using graph convolutional networks

An Architectural View Model for Designing and Implementing Microservices-based Systems: Use Case in FinTech

Describing the architecture of microservices systems allows for the illustration of various aspects of design and implementation with information tailored to different stakeholders. On the one hand, the microservices architecture provides flexibility, scalability, faster deployment times, and the potential to design solutions incorporating multiple technologies. An architecture-centered, scenario-driven, iterative development method, on the other hand, is used to explain the system from the perspective of many stakeholders, such as project managers, system architects, developers, and end users. To complement this point of view, this paper proposes an architectural view model that highlights numerous viewpoints and disciplines in microservices-based software development. Such methods are typically well suited for developing and implementing microservices-based software processes to satisfy stakeholders' demands and expectations.

Research on the Construction of Intelligent Library Micro-services System

With the advancement of the construction of smart libraries in China, grasping the new opportunities of scientific and technological revolution, providing micro-services based on their own resources to meet users' knowledge and information needs has become an important way for libraries to improve their own resource utilization, realize service upgrading and show their own value. Among many cutting-edge technologies, small programs based on micro-services have won the favor of the construction of smart library micro-services because of their characteristics of no download and instant search. However, at present, there are still many problems in smart library micro-services, such as lack of innovation in service content, single application of small program and lack of perfect service system. Therefore, it is necessary and urgent to deeply explore and study smart library micro-services.

An effective parallel convolutional anomaly multi-classification model for fault diagnosis in microservice system

An effective parallel convolutional anomaly multi-classification model for fault diagnosis in microservice system

MADMM: microservice system anomaly detection via multi-modal data and multi-feature extraction

MADMM: microservice system anomaly detection via multi-modal data and multi-feature extraction