DiPerF：一个自动化的分布式性能测试框架
DiPerF: an automated DIstributed PERformance testing Framework
摘要：
我们提出了DiPerF，分布式性能测试框架，旨在简化和自动化服务性能评估。DiPerF协调测试目标服务的机器池，收集和聚合性能指标，并生成性能统计数据。收集的聚合数据提供有关服务吞吐量、并发服务多个客户端时的服务“公平性”以及网络延迟对服务性能的影响的信息。此外，使用该数据，可以建立预测模型，该预测模型在给定服务负载的情况下估计服务性能。我们已经在Grid3和PlanetLab两个测试平台上的100多台机器上测试了DiPerF，并探索了Globus Toolkit 3.2中包含的作业提交服务（前WS版本和WS版本）的性能。
英文摘要：
We present DiPerF, a distributed performance testing framework, aimed at simplifying and automating service performance evaluation. DiPerF coordinates a pool of machines that test a target service, collects and aggregates performance metrics, and generates performance statistics. The aggregate data collected provide information on service throughput, on service "fairness" when serving multiple clients concurrently, and on the impact of network latency on service performance. Furthermore, using this data, it is possible to build predictive models that estimate a service performance given the service load. We have tested DiPerF on 100+ machines on two testbeds, Grid3 and PlanetLab, and explored the performance of job submission services (pre WS GRAM and WS GRAM) included with Globus Toolkit 3.2.
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。
