Performance Methods for Distributed and Cloud Software

分布式和云软件的性能方法

• 批准号: RGPIN-2016-06274
• 负责人: Woodside, Murray
• 金额: $ 1.89万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634941
• 关键词: Performance; Methods; Distributed; Cloud; Software

Performance models of distributed software systems can be used in many ways: (1) to guide the software design or refactoring, (2) to plan the system deployment, (3) for optimization of resource use. Layered queueing network models (LQNs) are ideal for this analysis since they attach resource usage to objects directly corresponding to software artifacts, and because they describe contention for logical as well as physical resources. All three uses require models that are quickly and easily created and calibrated to track the product development, changes in the deployment environment or usage levels. For example, DevOps (development with continuous releases) requires continuous re-modeling, which is provided by the proposer's existing methods to generate models automatically from software designs (students Dorin Petriu, Nariman Mani etc). Features of the deployment environment are included as “performance completions” (work with student Adnan Faisal and Prof Dorina Petriu). To adapt a model the proposer has applied nonlinear regression techniques to quickly calibrate LQN models, and statistical tracking filters to track dynamic changes in program behaviour. To exploit the models for cloud management he has applied linear and integer programming to optimize deployment. The proposed research will be based on these capabilities.

分布式软件系统的性能模型可以用于许多方面：（1）指导软件设计或重构，（2）规划系统部署，（3）优化资源使用。分层嵌入网络模型（LQN）是这种分析的理想选择，因为它们将资源使用与直接对应于软件工件的对象相关联，并且因为它们描述了逻辑和物理资源的争用。所有这三种用途都需要快速、轻松地创建和校准模型，以跟踪产品开发、部署环境或使用水平的变化。例如，DevOps（持续发布的开发）需要持续的重新建模，这是由提议者的现有方法提供的，以从软件设计自动生成模型（学生Dorin Petriu，Nariman Mani等）。部署环境的功能包括作为“性能完成”（与学生Adnan Faisal和Dorina Petriu教授合作）。为了适应模型，提议者应用了非线性回归技术来快速校准LQN模型，并应用统计跟踪过滤器来跟踪程序行为的动态变化。为了利用云管理模型，他应用了线性和整数规划来优化部署。这项研究将以这些能力为基础。