Skyplane：使用云感知覆盖优化传输成本和吞吐量
Skyplane: Optimizing Transfer Cost and Throughput Using Cloud-Aware Overlays
摘要：
云应用程序越来越多地跨多个地区和云提供商分发数据。不幸的是，广域批量数据传输通常是缓慢的，需要检查的应用程序。我们证明，它是可以显着提高区域间云批量传输吞吐量，通过适应网络覆盖到云设置-也就是说，通过在应用程序层的间接路径路由数据。然而，在这种情况下直接应用网络覆盖可能会导致云出口价格不可接受的增加。我们提出了Skyplane，一个用于云对象存储之间批量数据传输的系统，该系统使用云感知网络覆盖来优化价格和性能之间的权衡。Skyplane的规划器使用混合整数线性规划来确定数据传输的最佳覆盖路径和资源分配，并受用户提供的价格或性能约束。Skyplane的性能优于公共云传输服务，在一个云中传输高达$4.6\times$，跨云传输高达$5.0\times$。
英文摘要：
Cloud applications are increasingly distributing data across multiple regions and cloud providers. Unfortunately, wide-area bulk data transfers are often slow, bottlenecking applications. We demonstrate that it is possible to significantly improve inter-region cloud bulk transfer throughput by adapting network overlays to the cloud setting -- that is, by routing data through indirect paths at the application layer. However, directly applying network overlays in this setting can result in unacceptable increases in cloud egress prices. We present Skyplane, a system for bulk data transfer between cloud object stores that uses cloud-aware network overlays to optimally navigate the trade-off between price and performance. Skyplane's planner uses mixed-integer linear programming to determine the optimal overlay path and resource allocation for data transfer, subject to user-provided constraints on price or performance. Skyplane outperforms public cloud transfer services by up to $4.6\times$ for transfers within one cloud and by up to $5.0\times$ across clouds.
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。
