cloud-native architecture

This study addresses prolonged task completion times, low resource utilization, and high resource release latency in Docker/Kubernetes containers on cloud-native platforms running compute-intensive workloads (e.g., big data and deep learning). We systematically evaluate the performance impact of diverse resource scheduling strategies through system-level monitoring—leveraging cgroups and metrics-server—and multi-workload stress testing. For the first time, we empirically quantify how key resource configurations significantly affect task completion time (±79.4% variation) and resource release latency (+116.7% degradation). Based on these findings, we propose an evidence-driven configuration optimization paradigm that reduces maximum task completion time by up to 79.4% and precisely identifies configuration bottlenecks responsible for latency. Our results provide reproducible, transferable empirical foundations for resource management tuning and deployment decisions in cloud-native environments.

This work addresses the challenges of resource utilization and operational efficiency in microservice architectures by proposing a performance-metric-driven automated framework that intelligently determines the optimal deployment strategy for individual microservices between Infrastructure-as-a-Service (IaaS) and Function-as-a-Service (FaaS). By analyzing intrinsic microservice characteristics, the framework enables a scalable and reproducible migration from conventional IaaS deployments to a hybrid IaaS+FaaS model. Experimental evaluation on two real-world applications demonstrates that the approach accurately identifies microservices well-suited for serverless execution, significantly improving both deployment efficiency and resource utilization. Furthermore, the study clarifies the respective applicability boundaries and advantages of different cloud service models, offering practical guidance for architecture design in heterogeneous cloud environments.

Adaptive performance management in cloud-native environments faces persistent challenges in jointly optimizing adaptability, elasticity, and efficiency. This paper systematically surveys 96 peer-reviewed publications from 2017 to 2023 and proposes a novel five-dimensional classification framework—spanning optimization objectives, control scope, decision-making mechanisms, automation levels, and validation methodologies. It is the first to holistically integrate reactive/predictive feedback loops, ML-driven resource forecasting, cross-dimensional benchmark datasets, and AIOps toolchains, identifying pattern-based adaptive architectures at the application layer as a critical research gap. Key findings include a marked surge in related work since 2023 and the consolidation of feedback control and machine learning as dominant paradigms. The study further releases a standardized validation dataset inventory—categorized by application, resource, and network dimensions—and a taxonomy of mainstream AIOps tools, thereby enabling reproducible, comparable experimental evaluation.

This paper addresses the lack of formal, reconfigurable architectural models for cloud-native applications (CNApps). We propose a graph-theoretic and functionally semantic abstraction method: modeling CNApps as dynamic, reconfigurable directed acyclic multigraphs, where vertices represent microservices and edges encode communication protocols; microservices are further abstracted as event-driven serverless function collections, with dependencies captured via event-triggered dataflow graphs. Our key contribution is the first introduction of *functionals*—higher-order constructs from functional computation—as a formal mechanism to specify protocol implementations and service recombination logic. Integrating relational calculus with event-driven semantics, the model enables runtime protocol switching and topology-aware adaptive reconfiguration. This yields significant improvements in verifiability, elasticity, and service composability.

Research on containerization in multi-cloud environments remains fragmented, lacking a systematic, up-to-date synthesis. Method: We conduct a Systematic Mapping Study (SMS) spanning 2013–2024, analyzing 121 high-quality publications through bibliometric analysis, thematic coding, and ISO/IEC 25010 quality attribute modeling. Contribution/Results: We propose the first four-level classification framework—“Theme–Strategy–Quality Attribute–Tactic”—identifying four core research themes, 98 implementation strategies, 10 critical quality attributes, and 47 corresponding architectural tactics. Innovatively, we introduce a two-dimensional challenge-solution taxonomy organized along Security, Automation, Deployment, and Monitoring dimensions. This yields the first structured, reusable landscape of multi-cloud containerization, bridging theoretical research and industrial practice by supporting architecture design and technology selection—thereby addressing a longstanding gap in systematic knowledge integration for this domain.

This study addresses the infrastructure complexity of cloud-edge-end协同 architectures, which has emerged as a major bottleneck hindering developer productivity and innovation. Through 101 semi-structured interviews across 86 organizations, this work empirically identifies deployment complexity and onboarding difficulty as core challenges. It proposes four architectural directions to mitigate these issues: Object-as-a-Service (unified object abstraction), internal developer platforms, declarative AI/ML pipelines, and lightweight edge runtimes. Findings indicate that high-level abstractions and automation significantly enhance developer experience—outweighing the impact of execution performance optimizations—and thereby establish a new paradigm for platform engineering and distributed system design.

This paper addresses the lack of systematic optimization for CPU and memory resource allocation during the Release phase of cloud-native DevOps. We propose the first pre-deployment offline performance optimization framework for microservices—distinct from mainstream auto-scaling research focused on the Ops phase. Our approach performs fine-grained resource configuration tuning *before* deployment, thereby mitigating auto-scaling failures caused by suboptimal memory provisioning. Methodologically, we integrate Bayesian optimization, statistical experimental design, and a goal-directed factor screening strategy to balance sampling cost and approximation accuracy. Extensive evaluation on the TeaStore benchmark demonstrates that our pre-deployment optimization significantly improves memory suitability and API-level resource utilization. Moreover, it empirically validates the necessity and context-dependent applicability of factor screening under varying optimization objectives.

This study addresses the challenges faced by edge-cloud-native applications in cross-industry adoption, including fragmented toolchains, steep learning curves, and inconsistent performance across hybrid environments. Through in-depth interviews with practitioners from multiple sectors, the work offers the first systematic insight into the real-world pain points experienced by non-technical teams during digital transformation. It reveals that such teams prioritize productivity, service quality, and usability over cost alone. Grounded in qualitative analysis, the research identifies key platform design requirements centered on developer-friendliness, end-to-end lifecycle simplification, and SLA-aware orchestration, with a focus on distributed network computing, hybrid cloud management, and service-level agreement (SLA) assurance. These findings provide a practice-oriented roadmap for the evolution of converged cloud-network infrastructures.

This study addresses the lack of systematic guidance for enterprise software teams in choosing between monolithic and microservices architectures. The work proposes a decision-making framework that integrates technical and organizational factors, evaluating the trade-offs of each architecture across dimensions such as scalability, reliability, deployment efficiency, and organizational complexity. The assessment is grounded in system scale, business requirements, operational maturity, and long-term maintainability. Through architectural pattern analysis, a structured evaluation model, and multiple case studies, the authors develop a practical selection methodology tailored to real-world engineering contexts. This approach offers enterprises clear architectural evolution pathways and actionable guidelines aligned with their developmental stages, thereby significantly enhancing the rationality and sustainability of system design decisions.

This work addresses the challenges in edge and embedded application development—namely, heterogeneous software stacks, multi-language runtimes, and difficult debugging—which lead to rigid deployment workflows and complex fault diagnosis. To overcome these limitations, the paper proposes a novel architecture enabling unified end-edge-cloud development. Its core components include a single programming language, a retargetable runtime system, a local recording and replay mechanism for distributed events, and a cross-platform deployment framework. This design breaks down traditional debugging barriers in edge–cloud collaborative development, facilitating seamless scalability, consistent testing, and flexible deployment across heterogeneous environments. Evaluation of the prototype system demonstrates that the proposed approach significantly simplifies deployment procedures and enhances fault diagnosis efficiency.