提升云端服务评估的效能--使用Benchmark Suite的最后一公里
Boosting Metrics for Cloud Services Evaluation -- The Last Mile of Using Benchmark Suites
摘要：
基准套件对于从整体角度评估云服务的各个方面非常重要。然而，在使用基准测试套件和实现被评估的云服务的整体印象之间仍然存在差距。大多数云服务评估工作旨在报告单个基准测试结果，而不提供摘要度量。因此，拥有此类评估报告的客户仍然很难从全球角度了解经过评估的云服务。受机器学习的提升方法的启发，我们提出了提升方法的概念，以表示能够集成一套基准测试结果的所有潜在方法。本文介绍了两种类型的初步提升指标，并演示了如何提升指标可以用来补充个别云服务功能的主要措施。特别是，提升指标可以在将实验设计应用于云服务评估时发挥汇总响应作用。虽然Boosting的概念是基于我们在云计算领域的工作进行改进的，但我们相信它可以很容易地适应其他计算范例的评估工作。
英文摘要：
Benchmark suites are significant for evaluating various aspects of Cloud services from a holistic view. However, there is still a gap between using benchmark suites and achieving holistic impression of the evaluated Cloud services. Most Cloud service evaluation work intended to report individual benchmarking results without delivering summary measures. As a result, it could be still hard for customers with such evaluation reports to understand an evaluated Cloud service from a global perspective. Inspired by the boosting approaches to machine learning, we proposed the concept Boosting Metrics to represent all the potential approaches that are able to integrate a suite of benchmarking results. This paper introduces two types of preliminary boosting metrics, and demonstrates how the boosting metrics can be used to supplement primary measures of individual Cloud service features. In particular, boosting metrics can play a summary Response role in applying experimental design to Cloud services evaluation. Although the concept Boosting Metrics was refined based on our work in the Cloud Computing domain, we believe it can be easily adapted to the evaluation work of other computing paradigms.
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。
