This article proposes a random‐forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud‐ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single‐precision (SP) floating‐point operations (FLOPs), double‐precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud‐ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application‐to‐Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application‐specific random forests (ARF) and application‐class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application‐class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real‐world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

中文翻译：

---

A2Cloud-RF：一种基于随机森林的统计框架，用于指导云上高性能科学计算的资源选择

本文提出了一个基于随机森林的 A2Cloud 框架，将科学应用程序与云提供商及其实例相匹配，以实现高性能。该框架利用四个引擎来完成这项任务：PERF 引擎、云跟踪引擎、A2Cloud-ext 引擎和随机森林分类器 (RFC) 引擎。PERF 引擎分析应用程序以获得性能特征，包括单精度 (SP) 浮点运算 (FLOP)、双精度 (DP) FLOP、x87 运算、内存访问和磁盘访问的数量。云跟踪引擎获取所选云实例的相应性能特征，包括：每秒SP浮点操作数（FLOPS）、DP FLOPS、每秒x87操作数、内存带宽和磁盘带宽。A2Cloud-ext 引擎使用应用程序和云实例特性来生成代表应用程序到云匹配的客观分数。RFC 引擎使用这些客观分数生成两种类型的随机森林，以帮助用户进行快速分析：特定于应用程序的随机森林 (ARF) 和基于应用程序类别的随机森林。ARF 仅考虑输入应用程序的特征来生成随机森林并为选定的云实例提供数字评级。为了生成基于应用程序类的随机森林，RFC 引擎下载应用程序配置文件和先前测试过的应用程序的分数，这些应用程序的性能类似于输入应用程序。使用这些数据，RFC 引擎为实例推荐创建一个随机森林。我们使用来自不同云提供商的 12 个实例中的 8 个实际应用程序详尽地测试了这个框架。我们的测试表明，框架给出的实例评级与通过实际云执行获得的评级之间存在显着的统计一致性。