Microservice Systems Research Articles

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

Test Coverage in Microservice Systems: An Automated Approach to E2E and API Test Coverage Metrics

Test coverage is a critical aspect of the software development process, aiming for overall confidence in the product. When considering cloud-native systems, testing becomes complex, as it becomes necessary to deal with multiple distributed microservices that are developed by different teams and may change quite rapidly. In such a dynamic environment, it is important to track test coverage. This is especially relevant for end-to-end (E2E) and API testing, as these might be developed by teams distinct from microservice developers. Moreover, indirection exists in E2E, where the testers may see the user interface but not know how comprehensive the test suits are. To ensure confidence in health checks in the system, mechanisms and instruments are needed to indicate the test coverage level. Unfortunately, there is a lack of such mechanisms for cloud-native systems. This manuscript introduces test coverage metrics for evaluating the extent of E2E and API test suite coverage for microservice endpoints. It elaborates on automating the calculation of these metrics with access to microservice codebases and system testing traces, delves into the process, and offers feedback with a visual perspective, emphasizing test coverage across microservices. To demonstrate the viability of the proposed approach, we implement a proof-of-concept tool and perform a case study on a well-established system benchmark assessing existing E2E and API test suites with regard to test coverage using the proposed endpoint metrics. The results of endpoint coverage reflect the diverse perspectives of both testing approaches. API testing achieved 91.98% coverage in the benchmark, whereas E2E testing achieved 45.42%. Combining both coverage results yielded a slight increase to approximately 92.36%, attributed to a few endpoints tested exclusively through one testing approach, not covered by the other.

Multi-task federated learning-based system anomaly detection and multi-classification for microservices architecture

The microservices architecture is extensively utilized in cloud-based application development, characterized by the construction of applications through a series of functionally independent, small, autonomous services. This architectural approach is renowned for its attributes such as high cohesion, availability, low coupling, and exceptional scalability. The detection of runtime system point anomalies in microservices architectures is crucial for enhancing the Quality of Service(QoS). Furthermore, identifying the classes of detected anomalies is critical in practical applications. However, given the highly dynamic nature of microservices systems as a distributed computing architecture, conducting real-time system anomaly detection on distributed independent microservices poses a challenging task. To address these challenges, we propose the System Anomaly Detection and Multi-Classification based on Multi-Task Feature Fusion Federated Learning (SADMC-MT-FF-FL) framework. Initially, we introduce a distributed learning framework based on Multi-task Federated Learning (MT-FL) to construct multi-classification anomaly detection models for each microservice. Secondly, to identify complex system anomaly patterns and features during the runtime of microservices, we develop a feature extractor based on External Attention Mechanism and Multi-channel Residual Structure (EA-MRS). Finally, we design a Local–Global Feature-based Parallel Knowledge Transfer (LGF-PKT) framework, utilizing parallel knowledge transfer to parallelize weight updates for local and global features. To validate the effectiveness of our approach, we conducted comprehensive comparative experiments on the microservices benchmark platforms Sock-Shop and Train-Ticket. The experimental results on anomaly detection for multiclassification systems demonstrate that SADMC-MT-FF-FL outperforms the best baseline method by 28.3% and 27.8% for Macro F1 and Micro F1 on Train-Ticket, and by 8.8% and 8.6% on Sock-Shop, respectively. Additionally, we conducted comparison experiments on three public datasets, SWaT, SMD, and SKAB. The F1 scores were 0.5% higher than those of the centralized methods on SMD, respectively, 6% and 2.8% higher than those of the federated learning based method on SWaT and SKAB. Source codes are available at: https://github.com/icc-lab-xhu1/SADMC-MT-FF-FL.

An Approach to Improving Availability of Microservices for Cyber-Physical Systems

The design of modern Cyber-Physical Systems (CPS) connects physical and digital realms from cloud systems to edge devices. Microservice architecture has been widely used for IT solutions and emerges as a promising approach for supporting CPS that are more efficient, adaptable, and interconnected. However, there is an increasing need to improve the availability, reliability, and resilience of microservice systems according to the needs. This paper summarizes the challenges and drawbacks of microservice architecture used for CPS. Then, the simplified microservice model has been created, initial properties have been defined, and an improvement plan has been presented. The microservice model’s availability has been improved using a novel approach with endpoint containerization. Then, the discussion and conclusions have been offered to explore the full potential of integrating the physical and digital realms.

Software engineering architecture and its promising opportunities

The evolution of software architecture has witnessed the transition from monolithic to microservices, offering enhanced scalability, maintainability, and flexibility. With the rise of Microservices Architecture (MA), containerization has emerged as a pivotal technology to encapsulate microservices in isolated environments, ensuring consistent deployment. This paper delves into the intricate relationship between Microservices Architecture and containerization, focusing on the benefits, challenges, and practical implications of integrating both. Through a comprehensive experimental setup simulating an e-commerce platform, we quantitatively evaluate the performance metrics of a containerized microservices system versus a traditional monolithic setup. Our findings accentuate the performance gains achieved through MA and containerization, while also shedding light on areas that demand caution and further research. The insights presented serve as a beacon for organizations aiming to transition to or optimize their microservices and containerization practices.

A service mesh approach to integrate processing patterns into microservices applications

Cloud is the new enabler of data processing, archiving and analyzing, wherein offered services are built with flexible and low-coupling schemes following a microservice architecture, which is commonly managed by service mesh managers. Microservice architecture allows designers to build microservice systems based on design patterns. However, current service mesh managers are based only on pipeline patterns and delegate the construction of other patterns to virtual container managers. This limitation prevents designers from defining new patterns that can provide microservice systems with different features. This paper presents a new approach for constructing microservices systems that integrate processing patterns following a service mesh strategy. This approach will enable designers to create processing patterns not considered in current service meshes and to build designs based on the combination of patterns. The approach proposes the integration of components for implicit handling of processing patterns; this means that service mesh core tasks such as discovery process, microservice coupling and workload management become transparent, eliminating manager or end-user intervention. Encouraging results were obtained, in terms of performance and execution flexibility, in a case study, where a set of existing applications collaborating in a traditional workflow was converted into a microservice application integrating processing patterns (parallel and distributed) generated following this new approach.

Cognitive complexity points: a metric to evaluate the design of microservices-based applications

The complexity of the software allows us to analyze how difficult to understand, implement and maintain the program can be. The metrics allow us to measure and estimate certain characteristics of the software to make decisions and corrective or preventive actions. The definition of the complexity of the microservices-based applications design is fundamental since it directly affects the performance of the application, development, testing, maintainability, storage (transactions and distributed queries), and the use and consumption of computational resources. In this paper, a cognitive complexity metric is proposed to evaluate the design and granularity of microservices-based applications, which define the required effort, or degree of difficulty to understand the microservices that make up the system. Typical cases were analyzed, which can appear in the design of microservices-based applications, the calculation of cognitive complexity was correct and consistent with the difficulty of understanding, maintaining, and developing a microservice system, therefore it is a viable option for analyzing complexity in microservices-based architecture.

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

DGERCL: A Dynamic Graph Embedding Approach for Root Cause Localization in Microservice Systems

DGERCL: A Dynamic Graph Embedding Approach for Root Cause Localization in Microservice Systems