Search Results (242)

This paper presents a controlled comparative evaluation of SQL and NoSQL persistence mechanisms in containerized microservice architectures under variable workload conditions. Three persistence configurations—SQL with indexing, SQL without indexing, and a document-oriented NoSQL database, including supplementary hybrid SQL variants used for robustness analysis—are assessed across read-dominant, write-dominant, and mixed workloads, with concurrency levels ranging from low to high contention. The experimental setup is fully containerized and executed in a single-node environment to isolate persistence-layer behavior and ensure reproducibility. System performance is evaluated using multiple metrics, including percentile-based latency (p95), throughput, CPU utilization, and memory consumption. The results reveal distinct performance trade-offs among the evaluated configurations, highlighting the sensitivity of persistence mechanisms to workload composition and concurrency intensity. In particular, indexing strategies significantly affect read-heavy scenarios, while document-oriented persistence demonstrates advantages under write-intensive workloads. The findings emphasize the importance of workload-aware persistence selection in microservice-based systems and support the adoption of polyglot persistence strategies. Rather than providing absolute performance benchmarks, the study focuses on comparative behavioral trends that can inform architectural decision-making in practical microservice deployments. Full article

With the growing popularity of DevOps culture among companies and the corresponding increase in Microservices architecture development—both known to boost productivity and efficiency in software development—an increasing number of organizations are aiming to integrate them. Implementing DevOps culture and best practices can be challenging, but it is increasingly important as software applications become more robust and complex, and performance is considered essential by end users. By following the Design Science Research methodology, this paper proposes an iterative framework that closely follows the recommended DevOps practices, validated with the assistance of expert interviews, for implementing DevOps practices into Microservices architecture software development, while also offering a series of tools that serve as a base guideline for anyone following this framework, in the form of a theoretical use case. Therefore, this paper provides organizations with a guideline for adapting DevOps and offers organizations already using this methodology a framework to potentially enhance their established practices. Full article

Microservices as an emerging architectural approach have been widely applied in the development of online applications. However, in large-scale service systems, frequent data communications, complex invocation dependencies, and strict latency requirements pose significant challenges to efficient microservice orchestration. In addition, microservices need to frequently access the database to achieve data persistence, creating a mutual dependency between the two, and this symmetry further increases the complexity of service orchestration and coordinated deployment. In this context, the strong coupling of service deployment, database layout, and request routing makes effective local optimization difficult. However, existing research often overlooks the impact of databases, fails to achieve joint optimization among databases, microservice deployments, and routing, or lacks fine-grained orchestration strategies for multi-instance models. To address the above limitations, this paper proposes a joint optimization framework based on the Database-as-a-Service (DaaS) paradigm. It performs fine-grained multi-instance queue modeling based on queuing theory to account for delays in data interaction, request queuing, and processing. Furthermore this paper proposes a proximal policy optimization algorithm based on multi-stage joint decision-making to address the orchestration problem of microservices and database instances. In this algorithm, the action space is symmetrical between microservices and database deployment, enabling the agent to leverage this characteristic and improve representation learning efficiency through shared feature extraction layers. The algorithm incorporates a two-layer agent policy stability control to accelerate convergence and a three-level experience replay mechanism to achieve efficient training on high-dimensional decision spaces. Experimental results demonstrate that the proposed algorithm effectively reduces service request latency under diverse workloads and network conditions, while maintaining global resource load balancing. Full article

The reliable and continuous acquisition of seismic data from multiple open sources is essential for real-time monitoring, hazard assessment, and early-warning systems. However, the heterogeneity among existing data providers such as the United States Geological Survey, the European-Mediterranean Seismological Centre, and the Spanish National Geographic Institute creates significant challenges due to differences in formats, update frequencies, and access methods. To overcome these limitations, this paper presents a modular and automated framework for the scheduled near-real-time ingestion of global seismic data using open APIs and semi-structured web data. The system, implemented using a Docker-based architecture, automatically retrieves, harmonizes, and stores seismic information from heterogeneous sources at regular intervals using a cron-based scheduler. Data are standardized into a unified schema, validated to remove duplicates, and persisted in a relational database for downstream analytics and visualization. The proposed framework adheres to the FAIR data principles by ensuring that all seismic events are uniquely identifiable, source-traceable, and stored in interoperable formats. Its lightweight and containerized design enables deployment as a microservice within emerging data spaces and open environmental data infrastructures. Experimental validation was conducted using a two-phase evaluation. This evaluation consisted of a high-frequency 24 h stress test and a subsequent seven-day continuous deployment under steady-state conditions. The system maintained stable operation with 100% availability across all sources, successfully integrating 4533 newly published seismic events during the seven-day period and identifying 595 duplicated detections across providers. These results demonstrate that the framework provides a robust foundation for the automated integration of multi-source seismic catalogs. This integration supports the construction of more comprehensive and globally accessible earthquake datasets for research and near-real-time applications. By enabling automated and interoperable integration of seismic information from diverse providers, this approach supports the construction of more comprehensive and globally accessible earthquake catalogs, strengthening data-driven research and situational awareness across regions and institutions worldwide. Full article

Connecting distributed applications across multiple cloud-native domains is growing in complexity. Applications have become containerized and fragmented across heterogeneous infrastructures, such as public clouds, edge nodes, and private data centers, including emerging IoT-driven environments. Existing networking solutions like CNI plugins and service meshes have proven insufficient for providing isolated, low-latency and secure multi-cluster communication. By combining SDN control with Kubernetes abstractions, we present L2S-CES, a Kubernetes-native solution for multi-cluster layer-2 network slicing that offers flexible isolated connectivity for microservices while maintaining performance and automation. In this work, we detail the design and implementation of L2S-CES, outlining its architecture and operational workflow. We experimentally validate against state-of-the-art alternatives and show superior isolation, reduced setup time, native support for broadcast and multicast, and minimal performance overhead. By addressing the current lack of native link-layer networking capabilities across multiple Kubernetes domains, L2S-CES provides a unified and practical foundation for deploying scalable, multi-tenant, and latency-sensitive cloud-native applications. Full article

Root-cause analysis (RCA) in large-scale microservice-based payment systems is challenging due to complex failure propagation along service dependencies, limited availability of labeled incident data, and heterogeneous service topologies across deployments. We propose ServiceGraph-FM, a pretrained graph-based model for RCA, where “foundation” denotes a self-supervised graph encoder pretrained on large-scale production cluster traces and then adapted to downstream diagnosis. ServiceGraph-FM introduces three components: (1) masked graph autoencoding pretraining to learn transferable service-dependency embeddings for cross-topology generalization; (2) a temporal relational diffusion module that models anomaly propagation as graph diffusion on dynamic service graphs (i.e., Laplacian-governed information flow with learnable edge propagation strengths); and (3) a causal attention mechanism that leverages multi-hop path signals to better separate likely causes from correlated downstream effects. Experiments on the Alibaba Cluster Trace and synthetic PayPal-style topologies show that ServiceGraph-FM outperforms state-of-the-art baselines, improving Top-1 accuracy by 23.7% and Top-3 accuracy by 18.4% on average, and reducing mean time to detection by 31.2%. In zero-shot deployment on unseen architectures, the pretrained model retains 78.3% of its fully fine-tuned performance, indicating strong transferability for practical incident management. Full article

The increasing complexity of integrating modern convolutional neural networks into software systems imposes significant computational demands on machine learning infrastructures. Existing MLOps systems lack mechanisms for dynamic model selection based on dataset complexity, leading to inefficient resource utilization and limited scalability under high-load conditions. This study employs convolutional neural network-based machine learning algorithms for image classification and ensemble methods for quantitative feature classification. The paper presents a self-optimizing machine learning pipeline that integrates a microservices-based architecture with a formal process for estimating image complexity and an optimization-based model selection strategy. The proposed methodology is based on designing an adaptive microservice-based ML pipeline that dynamically reconfigures its computation graph at runtime. The results confirm the effectiveness of the proposed approach for building resilient and high-performance distributed systems. The mechanism proposed in this work enables the adaptive use of modern deep learning algorithms, leading to improved result quality. A comparative analysis with existing approaches demonstrates superiority in model selection complexity, pipeline overhead, and scalability. The outcome of the proposed mechanism is an adaptive algorithm selection process based on bias-related parameters, enabling the selection of the most suitable module for data processing. Full article

The purpose of the present study is to improve the efficiency of phishing web resource detection through multimodal analysis and using methods of explainable artificial intelligence. We propose a late fusion architecture in which independent specialized models process four modalities and are combined using weighted voting. The first branch uses CatBoost for URL features and metadata; the second uses CNN1D for symbolic-level URL representation; the third uses a Transformer based on a pretrained CodeBERT for the homepage HTML code; and the fourth uses EfficientNet-B7 for page screenshot analysis. SHAP, Grad-CAM, and attention matrices are used to interpret decisions; a local LLM generates a consolidated textual explanation. A prototype system based on a microservice architecture, integrated with the SOC, has been developed. This integration enables streaming processing and reproducible validation. Computational experiments using our own updated dataset and the public MTLP dataset show high performance: F1-scores of up to 0.989 on our own dataset and 0.953 on MTLP; multimodal fusion consistently outperforms single-modal baseline models. The practical significance of this approach for zero-day detection and false positive reduction, through feature alignment across modalities and explainability, is demonstrated. All limitations and operational aspects (data drift, adversarial robustness, LLM latency) of the proposed prototype are presented. We also outline areas for further research. Full article

Microservice architecture has become a foundational component of modern distributed systems due to its modularity, scalability, and deployment flexibility. However, the increasing complexity and dynamic nature of service interactions have introduced substantial challenges in accurately detecting runtime anomalies. Existing methods often rely on multiple monitoring metrics, which introduce redundancy and noise while increasing the complexity of data collection and model design. This paper proposes a novel spatiotemporal anomaly detection framework that integrates Dynamic Graph Neural Networks (D-GNN) combined with Long Short-Term Memory (LSTM) networks to model both the structural dependencies and temporal evolution of microservice behaviors. Unlike traditional approaches, our method uses only CPU utilization as the sole monitoring metric, leveraging its high observability and strong correlation with service performance. From a symmetry perspective, normal microservice behaviors exhibit approximately symmetric spatiotemporal patterns: structurally similar services tend to share similar CPU trajectories, and recurring workload cycles induce quasi-periodic temporal symmetries in utilization signals. Runtime anomalies can therefore be interpreted as symmetry-breaking events that create localized structural and temporal asymmetries in the service graph. The proposed framework is explicitly designed to exploit such symmetry properties: the D-GNN component respects permutation symmetry on the microservice graph while embedding the evolving structural context of each service, and the LSTM module captures shift-invariant temporal trends in CPU usage to highlight asymmetric deviations over time. Experiments conducted on real-world microservice datasets demonstrate that the proposed method delivers excellent performance, achieving 98 percent accuracy and 98 percent F1-score. Compared to baseline methods such as DeepTraLog, which achieves 0.93 precision, 0.978 recall, and 0.954 F1-score, our approach performs competitively, achieving 0.980 precision, 0.980 recall, and 0.980 F1-score. Our results indicate that a single-metric, symmetry-aware spatiotemporal modeling approach can achieve competitive performance without the complexity of multi-metric inputs, providing a lightweight and robust solution for real-time anomaly detection in large-scale microservice environments. Full article

The swift development of digital financial services has increased transaction volumes and heightened system performance requirements. Cardless cash deposit transactions at PT Bank XYZ have significantly increased since 2022. This growth necessitates an evaluation and improvement of the existing system architecture. This study proposes a microservices-based architecture deployed in a middleware environment to enhance performance, scalability, and availability. Key enhancements include asynchronous service processing, dual-layer authentication, and data caching using the Terracotta Server Array. The evaluation uses metrics such as CPU usage, RAM usage, latency, throughput, error rate, success rate, and recovery time. Both the monolithic and microservice architectures were assessed through stress testing. Tools used include Red Hat OpenShift Dashboard, NMon Visualizer, and Apache JMeter. Results indicate that the microservices architecture outperforms the monolithic architecture by delivering better resource efficiency, lower latency, higher throughput, and faster recovery times. Moreover, implementing dual-layer authentication enhances security without significantly increasing system complexity. The findings confirm the long-term viability of the microservices architecture for high-demand financial applications. Full article

Modern distributed systems increasingly rely on reactive programming to meet the demands of high throughput and low latency under extreme concurrency. While the theoretical advantages of non-blocking I/O are well-established, empirical understanding of its behavior across heterogeneous enterprise workloads remains fragmented. This study presents a unified architectural evaluation of asynchronous (thread-per-request) and reactive (event-loop) paradigms within a functionally equivalent Java microservice environment. Unlike prior studies that isolate specific workloads, this research benchmarks the architectural crossover points across three distinct operational categories: distributed caching, CPU-bound processing, and blocking I/O, under loads up to 1000 concurrent users. The results quantify specific boundary conditions: the reactive model demonstrates superior elasticity in I/O-bound caching scenarios, achieving 75% higher throughput and 68% lower memory footprint. However, this advantage is strictly workload-dependent; both paradigms converge to an identical CPU wall at processor saturation, where the reactive model incurs a quantifiable latency penalty due to event-loop contention. Furthermore, under blocking conditions, the reactive model’s memory efficiency (reducing footprint by ~50%) provides resilience against Out-Of-Memory (OOM) failures, even as throughput gains plateau. These findings move beyond generic performance comparisons to provide precise, data-driven guidelines for hybrid architectural adoption in complex distributed systems. Full article

Cloud-native microservice architectures offer scalability and resilience but introduce complex interdependencies and new attack surfaces, making them vulnerable to resource-exhaustion Distributed Denial-of-Service (DDoS) attacks. These attacks propagate along service call chains, closely mimic legitimate traffic, and evade traditional detection and mitigation techniques, resulting in cascading bottlenecks and degraded Quality of Service (QoS). Existing Moving Target Defense (MTD) approaches lack adaptive, cost-aware policy guidance and are often ineffective against spatiotemporally adaptive adversaries. To address these challenges, this paper proposes ScaleShield, an adaptive MTD framework powered by Deep Reinforcement Learning (DRL) that learns coordinated, attack-aware defense policies for microservices. ScaleShield formulates defense as a Markov Decision Process (MDP) over multi-dimensional discrete actions, leveraging a Multi-Dimensional Double Deep Q-Network (MD3QN) to optimize service availability and minimize operational overhead. Experimental results demonstrate that ScaleShield achieves near 100% defense success rates and reduces compromised nodes to zero within approximately 5 steps, significantly outperforming state-of-the-art baselines. It lowers service latency by up to 72% under dynamic attacks while maintaining over 94% resource efficiency, providing robust and cost-effective protection against resource-exhaustion DDoS attacks in cloud-native environments. Full article

The factors restricting the large-scale deployment of smart vehicular networks include application service placement/migration, mobility management, network congestion, and latency. Current vehicular networks are striving to optimize network performance through decentralized framework deployments. Specifically, the urban-level execution of current network deployments often fails to achieve the quality of service required by smart cities. To address these issues, we have proposed a vehicular edge–fog computing (VEFC)-enabled adaptive area-based traffic management (AABTM) architecture. Our design divides the urban area into multiple microzones for distributed control. These microzones are equipped with roadside units for real-time collection of vehicular information. We also propose (1) a vehicle mobility management (VMM) scheme to facilitate seamless service migration during vehicular movement; (2) a dynamic vehicular clustering (DVC) approach for the dynamic clustering of distributed network nodes to enhance service delivery; and (3) a dynamic microservice assignment (DMA) algorithm to ensure efficient resource-aware microservice placement/migration. We have evaluated the proposed schemes on different scales. The proposed schemes provide a significant improvement in vital network parameters. AABTM achieves reductions of 86.4% in latency, 53.3% in network consumption, 6.2% in energy usage, and 48.3% in execution cost, while DMA-clustering reduces network consumption by 59.2%, energy usage by 5%, and execution cost by 38.4% compared to traditional cloud-based urban traffic management frameworks. This research highlights the potential of utilizing distributed frameworks for real-time traffic management in next-generation smart vehicular networks. Full article

As organizations shift to microservice architectures, the need for effective anomaly detection becomes more critical. Classic approaches rely heavily on predefined thresholds or labeled data, both of which scale poorly in distributed and dynamic environments. This paper introduces the Night’s Watch algorithm, a novel unsupervised method for detecting anomalies in microservices. By integrating multi-source data and temporal features, the algorithm addresses key limitations of existing approaches. Our experiments demonstrate that the Night’s Watch algorithm significantly improves precision (up to 92%) and recall (up to 39%) depending on the training set size. These results indicate that the algorithm can reduce false positives and enhance real-time anomaly detection in microservice environments. These findings contribute to the development of more robust AI-driven monitoring systems, advancing the state of anomaly detection in cloud-native architectures. Full article

Real-time vessel tracking and environmental assessment in developing regions face significant challenges due to the high cost and proprietary constraints of commercial Automatic Identification System (AIS) services. We introduce MAGI, an open-source, low-cost, IoT-distributed architecture that integrates Orange Pi 5 edge nodes with software-defined radio (SDR) AIS receivers and containerized microservices to capture, preprocess, and stream AIS messages. During a ten-day field campaign in Panama, our decentralized deployment processed over 500,000 AIS transmissions, achieving 99% uptime and delivering vessel position and speed updates with sub-second latency. Based on the collected data, we also evaluated system scalability, energy consumption, and per node cost, demonstrating that a complete coastal network can be deployed for under USD 1200 per site. These results confirm that MAGI is a scalable, secure, and affordable IoT solution for AIS-based vessel tracking and environmental monitoring in resource-constrained settings. Full article