cloud computing

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 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.

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 systematically evaluates CPU performance and cost-effectiveness of running HPC-style OpenMP workloads in virtualized environments across major cloud platforms—AWS, Azure, GCP, and OCI. Using the SPEC ACCEL benchmark suite, we conduct a cross-platform, multi-architecture analysis spanning Intel, AMD, and ARM processors, under both on-demand and one-year reserved pricing models. Our methodology integrates rigorous performance measurement with detailed TCO modeling to enable joint performance–cost assessment. Results reveal substantial inter-cloud variation in architectural offerings and pricing strategies; workload characteristics—not vendor branding—should drive instance selection. AWS delivers the highest absolute performance (especially on ARM), but at the highest cost per unit performance. OCI achieves the best overall cost–performance ratio. GCP shows marked performance gains on AMD instances but suffers from poor ARM performance and low cost-efficiency. This work provides an evidence-based, quantitative decision framework for HPC resource provisioning in cloud environments.

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 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 widespread adoption of cloud computing by small and medium-sized enterprises (SMEs) in critical infrastructure, where deployment models and associated security and reliability risks remain poorly understood. Focusing specifically on multi-cloud strategies in this context, the research employs a systematic review of academic, industry, governmental, and online sources, complemented by content analysis, to holistically assess current deployment practices and risk characteristics. Findings reveal that while SMEs exhibit high levels of cloud adoption, their risk management practices significantly lag behind, highlighting an urgent need for targeted policy guidance and practical frameworks. The results provide empirical evidence and actionable insights for both regulatory bodies and enterprise decision-makers to better navigate the complexities of secure and resilient cloud deployment in critical sectors.

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 challenges of unpredictable costs and single-region constraints associated with Spot instances in cloud services, which stem from dynamic regional pricing, variable resource availability, and interruption risks. To overcome these limitations, the authors propose an AI-driven, multi-region Spot fleet provisioning approach that integrates real-time monitoring with machine learning–based cost prediction models. Leveraging the AWS EC2 Spot Fleet API, the method enables accurate cross-region cost estimation and optimal resource allocation prior to deployment. As the first solution supporting both cross-region Spot cost forecasting and deployment optimization, this work transcends the inherent EC2 Spot restrictions of single-region operation and lack of cost predictability. Evaluated at a scale of 1,500 vCPUs, the approach achieves 99.79% cost prediction accuracy and realizes up to 64% cost savings by exploiting inter-regional price differentials.