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。第一个广泛的领域，也是建议的大部分，涉及模型的创建，以及选择模型结构的基本未解决的问题。系统知识通常提供一个复杂的模型，其中包含对性能评估不重要的元素，难以理解和校准，并且需要很长的解决时间。目前的研究（学生Farhana Islam）正在开发简化复杂模型的程序。提出的研究解决了给定系统的简化如何根据系统使用和用户目标而变化，使用了解决给定性能问题的模型效用的新概念。随着系统的变化而调整简化后的模型，不仅修改了系统的参数，也修改了简化后的结构，将之前的参数跟踪工作扩展为“结构跟踪”。第二个广泛的领域考虑了模型的使用。模型效用的研究将对性能问题进行一般性的描述。一个重要的新问题是在多个服务中心或多云上优化大型系统的部署，这些系统具有大量的中心间网络延迟。我们针对单个云（学生Jim Li）的解决方案并没有推广到延迟问题，但是学生Ravneet Kaur针对简单情况提出的启发式方法解决了这个问题。它们涉及图形划分与粗化和精炼，结合装箱。实际问题将需要延长。

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.The first broad area, and the majority of the proposal, addresses the creation of models, and the fundamental unsolved problem of choosing the model structure. System knowledge often provides a complex model with elements that are not essential for performance evaluation, difficult to understand and to calibrate and with long solution times. Current research (student Farhana Islam) is developing procedures to simplify a complex model. The proposed research addresses how the simplification of a given system changes depending on system usage and user goals, using a novel concept of the utility of a model for solving a given performance problem. Adapting the simplified model as the system changes will modify its parameters and also revise the simplified structure, extending previous work in parameter tracking to "structure tracking".The second broad area considers the use of the models. The study of model utility will characterize performance problems in a general way. A significant emerging problem is optimization of the deployment of large systems over multiple service centers or multi-clouds, with substantial intercenter network latencies. Our solutions for a single cloud (student Jim Li) do not generalize to the latency, but heuristics by student Ravneet Kaur for simple cases solve this problem. They involve graph partitioning with coarsening and refining, combined with bin-packing. Practical problems will require extension.