cloud and infrastructure

This study addresses the high manual overhead faced by DevOps teams in managing multi-interface cloud infrastructures. We propose and systematically evaluate an LLM-driven AI agent framework for automation. Methodologically, the agent unifies heterogeneous interfaces—including SDKs, CLIs, Infrastructure-as-Code (IaC) tools, and web portals—to support core tasks such as configuration deployment, monitoring/alerting, and incident remediation. Key contributions include: (1) the first evaluation framework specifically designed for AI agents in cloud infrastructure management; (2) identification and systematic mitigation of three critical bottlenecks—interface semantic gaps, action execution reliability, and security constraint compliance; and (3) domain-specific optimization strategies validated in real-world deployments, demonstrating both task feasibility and cross-scenario generalizability. Our work establishes a reusable methodology and empirical benchmark for AI-native cloud operations.

This paper addresses the conceptual ambiguity, ill-defined boundaries, and lack of implementation standards between Infrastructure-as-Code (IaC) and Pipeline-as-Code in DevOps practice. To resolve these issues, we systematically delineate their respective roles and synergistic mechanisms within the DevOps ecosystem and propose a reusable, standardized IaC-driven CI/CD implementation framework. Our approach integrates Terraform for infrastructure provisioning, Ansible for configuration management, GitLab CI for pipeline orchestration, and Docker/Kubernetes for containerized deployment—enabling an end-to-end automated delivery pipeline. Empirical evaluation demonstrates 99.8% configuration change accuracy, reduces environment provisioning time from hours to minutes, and significantly improves deployment consistency and delivery efficiency.

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 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.

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 coordination challenges arising from independent control across cloud, high-performance computing (HPC), and edge AI infrastructures. Conceptualizing the AI platform as a "system of systems," this work proposes an architectural paradigm characterized by usage fusion and federated control. Methodologically, it adopts a systems engineering framework that achieves cross-domain coordination through interface contracts while preserving native control planes. The approach incorporates boundary testing, responsibility models, and seven integration facets, leveraging interface mapping, policy contexts, and operational evidence to guide integration design. The primary contribution lies in establishing a unified framework for evaluating interoperability, governance capabilities, and fault isolation, thereby delineating clear directions for future research.

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.

该研究比较了Docker容器与虚拟机在架构、性能、配置和安全方面的差异，分析了两者在隔离性与效率上的权衡，并提出混合架构作为解决方案。

Enterprise cloud environments are frequently exposed to security threats due to misconfigurations, excessive permissions, and fragmented security tooling, compounded by the absence of unified, coordinated protection across Kubernetes, OpenStack, and Infrastructure-as-Code (IaC) platforms. This work proposes the first open-source microservices-based security framework that uniquely integrates identity governance, multi-platform configuration auditing, runtime threat detection, and automated IaC remediation into a single closed-loop system. Designed with standardized REST/gRPC interfaces and scalable for medium-to-large deployments, the framework synergistically combines Falco, ELK, Terraform, Checkov, and OPA. In enterprise evaluations, it reduced vulnerability assessment time from 120 to 18 minutes, achieved a false positive rate below 5%, decreased security incidents by 62%, and lowered operational costs by approximately 40%, all while being released under the Apache 2.0 license.

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.