cloud services integration

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.

This study addresses the practical disparities and co-evolution between high-performance computing (HPC) and edge computing architectures within the cloud continuum. It presents the first large-scale empirical analysis based on 396 real-world, production-grade AWS architectures. Methodologically, we propose a multidimensional, data-driven framework encompassing service topology identification, storage type classification, architectural complexity quantification, and ML service integration statistics. Results reveal systematic differences—and complementary patterns—between HPC and edge architectures across four dimensions: core service composition (e.g., EC2 versus Greengrass/Lambda), storage design paradigms (parallel file systems versus distributed lightweight caches), complexity distributions, and ML embedding strategies. This work delivers the first industry-scale architectural benchmark for the cloud continuum, providing empirically grounded insights and methodological foundations for cross-domain architecture design, resource optimization, and cloud-native convergence of HPC and edge computing.

This study addresses the lack of systematic optimization in cloud data pipelines with respect to cost, execution time, and resource utilization, particularly in multi-tenant and industrial settings where research remains limited. Through a comprehensive systematic literature review, the work establishes a unified classification framework for optimization objectives that encompasses both single- and multi-cloud environments as well as batch and stream processing paradigms. The analysis synthesizes existing approaches and identifies critical research gaps, including insufficient support for multi-tenancy, inadequate multi-cloud coordination, and a scarcity of real-world deployment validation. By clarifying the core objectives and technical pathways for optimizing cloud data pipelines, this paper provides a theoretical foundation and clear direction for future research in this domain.

To address the reduced service reusability and constrained energy efficiency caused by early binding of cloud design patterns in data mesh architectures, this paper proposes a non-intrusive, late-binding cloud pattern integration framework. The framework enables on-demand, dynamic injection of cloud design patterns—including circuit breakers, retries, and rate limiting—at deployment or runtime without modifying service source code, thereby preserving high reusability while optimizing energy consumption. Built on Kubernetes, it supports containerized orchestration, automated pattern injection, fine-grained runtime energy monitoring, multi-pipeline coordinated deployment, and adaptive decision-making. Experimental evaluation demonstrates that the framework improves service reuse rate by 32% while reducing average energy consumption by 19.7%, significantly enhancing both energy awareness and architectural flexibility of data-sharing pipelines.

This study addresses the interoperability and migration challenges enterprises face when deploying workloads across AWS and Alibaba Cloud. Through a systematic comparison of architectural designs, service offerings, and operational policies between the two platforms, the research conducts an exploratory case study on migrating IoT workloads using both native and open-source Infrastructure-as-Code (IaC) tools. It reveals critical technical trade-offs inherent in cross-cloud co-deployment for the first time, distills best practices for secure, resilient, and vendor-lock-in-mitigated multicloud deployments, and proposes a multicloud interoperability framework tailored for global enterprises. The findings offer methodological support for empirically grounded multicloud strategies.

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 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 challenge of migrating monolithic backends to serverless architectures, a process typically requiring extensive manual effort. The authors propose the first automated migration pipeline that integrates lightweight static analysis with multi-agent collaboration. By constructing call graphs and identifying asynchronous behaviors, the approach orchestrates four specialized LLM agents—Architect, Developer, SAM Engineer, and Consistency Validator—to jointly generate end-to-end deployable applications compliant with AWS SAM specifications. Evaluated on six real-world benchmarks comprising over 10,000 lines of code and 76 endpoints, the method achieves a 100% deployment success rate, 66.1% end-to-end correctness, and a 98.7% F1 score for API coverage, substantially outperforming existing commercial solutions.

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 study addresses the problem of Kubernetes infrastructure drift, where runtime states deviate from architectural intent, by proposing an editable living architecture model. This model explicitly maps runtime facts to architectural designs, supports bidirectional synchronization between textual and graphical views, and enables non-intrusive Architecture-as-Code management through periodic consistency checks. The proposed approach is implemented using a subset of the Archer KDL, a VS Code-based prototype tool, and snapshot restoration techniques. Experimental evaluations conducted on three representative applications validate the feasibility of the method, demonstrating strong performance in both snapshot restoration accuracy and inconsistency detection recall.