aws

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 work addresses two critical challenges in Ethereum infrastructure: the limited elasticity and operational complexity of self-hosted nodes, and the lack of protocol-layer observability in managed blockchain services. To resolve these, we propose a hybrid cloud architecture built atop AWS Managed Blockchain, integrating a custom EC2-based observation node instrumented with Web3.py and JSON-RPC interfaces. Leveraging AWS IAM least-privilege policies and CDK-driven infrastructure-as-code deployment, our approach enables fine-grained, protocol-level monitoring. It is the first solution to support real-time collection of over 1,000 metrics—including mempool dynamics, transaction latency, and gas consumption—within a managed blockchain environment, with visualization via Amazon CloudWatch. The architecture balances cloud elasticity, security compliance, and protocol transparency, facilitating reproducible academic research and enterprise-grade on-chain monitoring. This work establishes a novel paradigm for observable, cloud-native decentralized infrastructure deployment.

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 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 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 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.

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.

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.

This study addresses the opacity and complexity of pricing models in mainstream serverless Function-as-a-Service (FaaS) platforms, which hinder users from making cost-efficient deployment decisions. The authors systematically analyze the features and billing mechanisms of AWS Lambda, Azure Functions, and Google Cloud Functions, and present the first empirical cost evaluation by deploying representative workloads across multiple geographic regions. Their findings reveal that AWS consistently offers the lowest execution costs, while Azure incurs the highest, with significant price variations observed even within the same provider across different regions. By establishing a cross-platform and cross-regional cost benchmark, this work not only fills a critical gap in empirical FaaS cost analysis but also highlights substantial opportunities for optimizing current cloud pricing strategies.