Performance Methods for Distributed and Cloud Software

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。分层排队网络模型（lqn）是这种分析的理想选择，因为它们将资源使用附加到直接对应于软件工件的对象上，并且因为它们描述了对逻辑资源和物理资源的争用。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，这是由提案人现有的方法提供的，可以从软件设计中自动生成模型（学生Dorin Petriu， Nariman Mani等）。部署环境的特性包括作为“性能完成”（与学生Adnan Faisal和Dorina Petriu教授合作）。为了适应模型，作者应用非线性回归技术来快速校准LQN模型，并使用统计跟踪滤波器来跟踪程序行为的动态变化。为了开发云管理模型，他应用了线性和整数规划来优化部署。拟议的研究将以这些能力为基础。

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.