aws

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 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.

Serverless computing presents dual complexities in function resource configuration—platform opacity and conflicting resource coupling models (e.g., commercial providers linearly scale CPU/bandwidth with memory) versus decoupled resource allocation in open-source frameworks—making it challenging for developers to simultaneously satisfy performance constraints and cost efficiency. Method: We systematically analyze key configuration factors affecting performance and cost in FaaS environments, conduct a comprehensive literature review, and comparatively examine configuration mechanisms across major cloud platforms (AWS Lambda, Azure Functions) and open-source frameworks. Contribution/Results: We propose the first multidimensional taxonomy for function resource configuration, uncovering critical research gaps in dynamic optimization, cross-platform adaptability, and independent resource control. Our structured classification model identifies automated configuration, fine-grained performance prediction, and joint cost-performance optimization as essential future research directions.

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.

This paper addresses performance instability and opaque cost structures in serverless cloud systems for large-scale data processing. We propose Skyrise, an evaluation platform that integrates micro-benchmarks with end-to-end workloads (e.g., Join, Aggregation) to quantitatively characterize performance variability boundaries of AWS serverless networking and storage—marking the first such analysis. It further establishes a compute-storage cost breakeven model. Key contributions include: (1) systematic identification of network/I/O performance degradation patterns in Lambda under high concurrency; (2) precise delineation of applicability boundaries—serverless outperforms VM-based solutions for medium-to-low-concurrency, bursty workloads; and (3) a reusable, cost-performance co-optimization decision framework. Empirical results validate the feasibility and economic viability of serverless architectures for specific data-intensive scenarios.

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.

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 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.

Serverless computing has matured into an effective execution model for edge cloud environments, enabling function level decomposition, demand driven scaling, and workflow execution across stable, well provisioned infrastructure. This success motivates extending it to the edge cloud space continuum, where Low Earth Orbit (LEO) constellations are increasingly explored as distributed compute substrates. However, existing serverless orchestration is not directly applicable in this setting, where LEO systems impose time varying contact graphs, intermittent link availability, and strict feasibility constraints on energy, memory, communication, and operational cost. This article identifies ten broken assumptions in existing serverless orchestration and organizes them into three core challenges: spatiotemporal execution over dynamic graphs, constraint aware function placement and scaling, and correctness and progress under decentralized and delayed state. It then proposes an architecture that enables robust and efficient serverless execution across the continuum, grounded in these challenges and demonstrated through a representative flood response use case.

To address the challenges of prolonged CI pipeline deployment cycles, error-prone manual configuration, and poor cross-project consistency, this paper proposes an automated pipeline configuration framework grounded in Infrastructure-as-Code (IaC) principles and templated configuration. The framework enables declarative definition and one-click generation of CI/CD pipelines via reusable YAML templates, a parameterized pipeline engine, and an integrated automation toolchain. Compared to conventional manual approaches, our method reduces average pipeline deployment time by 72% and decreases human configuration errors by 91%, while substantially improving consistency in build logic and execution environments across projects. Empirical validation across six open-source projects demonstrates the framework’s engineering practicality and methodological generality. It provides a reusable implementation model and actionable methodology for CI/CD automation, advancing scalable, maintainable, and reproducible software delivery practices.