amazon web services

Autoscaling in cloud-native environments faces challenges including intricate microservice dependencies, highly dynamic and heterogeneous workloads, and poor cross-environment adaptability. This paper systematically surveys representative works published since 2020 and proposes a five-dimensional taxonomy—spanning infrastructure, architecture, scaling mechanisms, optimization objectives, and behavioral modeling—to enable fine-grained technical comparison and scenario-specific applicability analysis. We identify three emerging frontiers: large language model–driven autoscaling, microservice dependency–aware scheduling, and meta-learning–enhanced generalization—thereby bridging critical gaps in dynamic workload modeling and cross-platform adaptive scaling. By integrating performance profiling, workload feature extraction, anomaly detection, and dependency analysis, our work provides academia with a clear evolutionary roadmap and delivers to industry a practical, service-quality–aware technology selection framework that jointly optimizes resource efficiency and QoS guarantees.

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 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.

To address the high communication overhead and poor scalability of serverless architectures in machine learning–intensive, data-heavy workloads, this paper proposes a high-performance computing (HPC)-inspired serverless framework. Methodologically, it introduces a NAT-traversal direct communication mechanism based on TCP hole punching, implements a lightweight serverless communicator, and integrates the Cylon distributed dataframe library with an FMI-inspired heuristic communication scheduling model. This design enables decentralized, low-latency, high-throughput distributed data processing within cloud-native environments—without centralized coordination. Experimental results demonstrate that the framework achieves over 99% end-to-end performance improvement compared to conventional serverless approaches. Its strong scaling efficiency closely matches that of EC2 instances and dedicated HPC clusters. Notably, this work is the first to achieve near-HPC communication efficiency and scalability in a serverless setting.

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 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.

Scientific computing in heterogeneous environments faces significant challenges in simultaneously achieving high performance, cost efficiency, scalability, and accessibility. This work proposes a hybrid cloud architecture tailored for scientific computing that integrates grid and cloud platforms—such as SLURM, OpenPBS, OpenStack, and Kubernetes—with workflow systems including Nextflow, Snakemake, and Common Workflow Language (CWL). By leveraging federated computing, multi-cloud orchestration, and a unified governance framework, the architecture enables seamless cross-platform resource scheduling and task coordination. The approach substantially enhances infrastructure interoperability and sustainability, with validation in life sciences demonstrating its practical efficacy. It has already facilitated integration and large-scale adoption within the ELIXIR and European Open Science Cloud (EOSC) ecosystems.

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.

Existing FaaS workflow platforms lack systematic empirical evaluation; their scaling behavior, inter-function data exchange, and billing models exhibit non-intuitive characteristics heavily influenced by platform design, dataflow patterns, and workload properties. Method: We conduct the first large-scale empirical study of AWS Step Functions, Azure Durable Functions, and Logic Apps, evaluating 25 microbenchmarks and real-world workflows across 132,000 invocations. We quantify performance using multidimensional metrics—including end-to-end latency, cold-start overhead, resource utilization, and cost. Contribution/Results: Our analysis reveals substantial disparities across platforms in performance, scalability, and cost-efficiency, exposing significant gaps between developer expectations and observed behavior. The findings provide empirically grounded guidance for configuration optimization and identify key research directions to enhance transparency, predictability, and efficiency of FaaS workflow systems.

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.