基于云的零售点-of-销售系统成本-性能分析：Google云平台与微软Azure的比较研究
Cost-Performance Analysis of Cloud-Based Retail Point-of-Sale Systems: A Comparative Study of Google Cloud Platform and Microsoft Azure
AI总结：
本文通过实测API端点和开源基准代码，比较了Google Cloud Platform和Microsoft Azure在零售工作负载上的性能与成本，发现GCP在基线负载下响应时间快23%，Azure在稳态操作中成本效率高71.9%。
摘要：
尽管关于平台特定性能的研究较少，但零售业的数字化转型加速了云点-of-销售系统（POS）的采用。本文系统地比较了Google Cloud Platform（GCP）和Microsoft Azure上的POS工作负载部署，使用实时API端点和开源基准代码进行可重复的测试。通过免费云资源，我们提供了一种透明的POS工作负载评估方法，供小型零售商和研究人员使用。我们的方法测量了响应延迟、吞吐量和可扩展性等关键性能指标，并基于实际资源使用和当前公共云定价估算运营成本，因为免费层级下没有直接计费。本研究中所有表格和图表均直接从代码输出生成，确保实验数据和报告结果的一致性。我们的分析显示，GCP在基线负载下响应时间快23.0%，而Azure在稳态操作中成本效率高71.9%。我们探讨了导致这些差异的架构组件，并为考虑云点-of-销售实施的商家提供了一个有用的框架。本研究建立了一个强大的、开放的基准测试方法，为零售云应用提供了一个强有力的参考，并提供了首个全面的、基于代码的比较，用于比较不同云平台上的点-of-销售系统特有的工作负载。
英文摘要：
Althoughthereislittleempiricalresearchonplatform-specific performance for retail workloads, the digital transformation of the retail industry has accelerated the adoption of cloud-based Point-of-Sale (POS) systems. This paper presents a systematic, repeatable comparison of POS workload deployments on Google Cloud Platform (GCP) and Microsoft Azure using real-time API endpoints and open-source benchmarking code. Using free-tier cloud resources, we offer a transparent methodology for POS workload evaluation that small retailers and researchers can use. Our approach measures important performance metrics like response latency, throughput, and scalability while estimating operational costs based on actual resource usage and current public cloud pricing because there is no direct billing under free-tier usage. All the tables and figures in this study are generated directly from code outputs, ensuring that the experimental data and the reported results are consistent. Our analysis shows that GCP achieves 23.0% faster response times at baseline load, while Azure shows 71.9% higher cost efficiency for steady-state operations. We look at the architectural components that lead to these differences and provide a helpful framework for merchants considering cloud point-of-sale implementation. This study establishes a strong, open benchmarking methodology for retail cloud applications and offers the first comprehensive, code-driven comparison of workloads unique to point-of-sale systems across leading cloud platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
