ABSTRACT Microservice bad smells (MBSs) affect system quality. However, the lack of comprehensive and detailed explanations of MBSs and current classification methods does not comprehensively encompass the microservice characteristics, making it challenging to study and address MBSs. Existing studies have focused on MBS detection methods, but identifying and quantifying the effects of different smells and properly assessing system health remain challenging. This review aims to provide an exhaustive list of the MBSs and formulate a reasonable classification based on microservice characteristics. By reasonably assessing the harmfulness of smells, we would comprehensively explain different MBSs. We would explore microservice system evaluation methods and propose a microservice system health assessment model (MSHAM) based on MBSs. We conducted a systematic literature review (SLR) of the catalog and evaluation literature in the field of MBSs. We also combined the open questionnaire form to answer the research questions qualitatively and quantitatively. This paper presents a comprehensive list of 69 types of MBSs, incorporating a highly scalable classification. Following the quality characteristics in ISO/IEC 25010:2023, we determined the sets of characteristics affected by different smells, and provided detailed explanations of smells in the form of MBSs knowledge base. Furthermore, we introduced the MSHAM, revealing the quantification process of harmfulness. The formulated smells list and classification method capture MBS characteristics comprehensively, ensuring scalability. We have exposed the impact of MBSs, enhancing researcher and practitioner understanding. MSHAM provides a logical model to quantify MBSs harm and system health, supporting the development of real‐time health monitoring.

To address the limitations of existing microservice bad smell (MBS) detection and refactoring tools, particularly the lack of fully automated architectural bad smell refactoring solutions, this paper proposes a MAPE-K-based self-adaptive framework for microservice bad smell refactoring (SABER). The framework aims to eliminate architectural smells through closed-loop self-repair, thereby reducing risks related to main-tainability, scalability, and security. SABER employs a cloud-edge collaborative architecture: edge-side components collect real-time metrics from a Kubernetes cluster, while cloud-side components detect architectural smells and dynamically generate refactoring strategies. These strategies include service merging, splitting, adding, and adjustment. By automatically executing these strategies, SABER achieves architectural bad smell elimi-nation. Experimental results show that the framework achieves 95.53 % precision and 84.71 % recall across ten benchmark systems, significantly improving refactoring efficiency compared to semi-automated and manual methods. Its deep integration with DevOps pipelines validates its effectiveness in sustaining microservice health, offering a novel paradigm for autonomous maintenance in distributed systems.

ABSTRACT Microservice bad smells, arising from poor design and development practices, can severely degrade system quality if unaddressed. While rule‐based detection methods exist, their applicability is limited by subjective metric thresholds and the difficulty in defining certain bad smells, particularly complex microservice bad smells that are challenging to express through rules or involve high subjectivity. These smells often involve multiple services or manifest across multiple layers within a service, making them particularly challenging to detect using traditional methods. Without efficient and accurate detection mechanisms, the self‐healing capabilities of microservices during operation and continuous evolution will also be compromised. Given the promise of machine learning in code smell detection, this study empirically evaluates its performance in detecting complex microservice bad smells. We employ two sampling techniques and eight classification models on 1180 samples from 55 systems, generating 45 detection models and identifying top classifiers for seven complex microservice bad smell types. We compare machine learning with rule‐based methods for high‐subjectivity smells, analyze performance gaps, and propose a MAPE‐K‐based conceptual framework for runtime detection and refactoring. Finally, we discuss the necessity for future research.

In modern data centers and enterprise networks, software switches have become critical components for achieving flexible and efficient network management. Due to resource constraints in software switches, sketches have emerged as a promising approach for network traffic measurement. However, their accuracy is often impacted by hash collisions. Existing hash functions treat all collisions equally, failing to account for the differing impacts of collisions involving elephant flows versus mouse flows. We propose FSA-Hash, a novel flow-size-aware hashing scheme that separates elephant flows from each other and from mouse flows, minimizing the most detrimental collisions. FSA-Hash is designed based on two insights: separating elephant flows from mouse flows avoids overestimating mouse flows, while separating elephant flows from each other enables accurate heavy-hitter detection. We implement FSA-Hash using machine learning models trained on network traffic data (LFSA-Hash), and also design a lightweight online variant (OLFSA-Hash) that learns the hash model solely from sketch queries on the software switch, obviating traffic collection overheads. Evaluations across four sketches and two tasks demonstrate FSA-Hash’s superior accuracy over standard hash functions. Moreover, OLFSA-Hash closely matches LFSA-Hash’s performance, making it an attractive option for adaptively refining the hash model without monitoring traffic.

With the development of network measurement technologies, a hybrid measurement architecture can effectively optimize the sketch structure in switches, making it more adaptable to the current complex and volatile network environment. However, current optimization technologies based on hybrid measurement architectures generally suffer from insufficient automation, difficulty of learning effective numerical features, and lack of generality, resulting in poor scalability in real deployment. To solve these problems, we propose theTalentSketchframework, based on which we further developDeepSketchfor effective sketch optimization. First, we useSeq2Seqto automatically identify target flows instead of relying on manual thresholds. Second, we propose a new training strategy that extracts low-precision flows for models with weak learning capabilities. Last, we develop a new sketch optimization framework that can optimize different kinds of sketches only by changing the training data for generality. A large number of experimental results show thatDeepSketchexhibits superior performance. For example: (1) the accuracy of optimized sketches has increased by 20% to 73%, (2) Without replacing the model structure, the accuracy of the optimized sketches can generally reach over 80%. (3) The impact of low sampling rates on accuracy is less than 1% on various sketches.

To overcome the defects of landslide monitoring methods on ground or airborne- or satellite-based remote sensing, the micro unmanned aerial vehicle (micro-UAV) based remote sensing method is used to monitoring Qinglingou slope, a steep slope in Three Gorges Reservoir, China. A tailormade micro-UAV with multi-rotor, high-accuracy position orientation system and digital camera is assembled, and it can fly and photograph automatically according to flight plan. After acquiring all photographs, the digital orthophoto and digital terrain model (DTM) with high spatial resolution and high accuracy can be produced by photogrammetric processing. Accordingly, the exact spatial characteristics, especially the new surface deformation can be easily identified, even if these deformations occurred in inaccessible area and covered by dense vegetation. Results show that the method has incomparable superiority, because it can directly reflect the whole surface of slope or landslide from aeroview.