Software performance engineering by models and measurement

软件性能工程（SPE）工具不足，导致严重的长期和 在提供具有适当性能（响应时间或吞吐量）的软件方面仍然存在问题。 例子比比皆是：微软灾难性的Vista版本就是一个。关于Web服务系统开发中的问题的文献记录了许多类型的性能问题。软件性能是一个具有巨大经济价值的科学难题， 最近，提议者及其同事提出， - 使用测量的流行方法， - 最近在性能建模方面取得的重大（但主要是学术性的）进展。 为实现这一愿景，本提案包括： 1.性能模型（Pmodels）的半自动构建和维护， 2.利用Pmodel来评估设计，创建高效的性能测试，进行容量规划，以及配置和操作高效的部署系统。 该建议包括通过转换软件的UML规范提取分层性能模型，并带有标准性能注释;估计和跟踪性能模型的参数，简化模型以提高对云中超大型系统的分析的可扩展性;在云中优化大型应用程序的部署和配置（在最优成本或能量消耗的意义上，受到模型计算的性能约束），以及对LQNS的改进，LQNS是用于求解分层性能模型的工具。

Software performance engineering (SPE) has inadequate tools, resulting in serious long-standing and continuing problems in delivering software with adequate performance (response time or throughput capacity). Examples abound: Microsoft's disastrous Vista release is one. The literature on problems in web service system development documents many kinds of performance problems. Software performance is a difficult scientific problem with large economic value, Recently the proposer and colleagues proposed integrating - the prevalent approach using measurements, with - the considerable (but largely academic) recent progress in performance modeling. To achieve this vision, this proposal includes: 1. semi-automatic construction and maintenance of performance models (Pmodels), 2. exploitation of Pmodels to evaluate designs, to create efficient performance tests, for capacity planning, and to configure and operate efficient deployed systems. The proposal includes extraction of layered performance models by transformation of UML specifications of software, with standard performance annotations; estimation and tracking of the parameters of performance models, simplification of models to improve scalability of the analysis to very large systems in clouds; optimal deployment and configuration of large applications in clouds (in the sense of optimal cost or energy consumption, subject to performance constraints computed by a model), and improvements to LQNS, the tool used to solve layered performance models.