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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681618
• 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.**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. ** **

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。本研究将以这些能力为基础。**第一个广泛的领域，也是该提案的大部分内容，涉及模型的创建，以及选择模型结构的基本未解决问题。系统知识通常提供一个复杂的模型，其中的元素对性能评估来说不是必不可少的，难以理解和校准，解决问题的时间很长。目前的研究（学生Farhana Islam）正在开发简化复杂模型的程序。拟议的研究解决了如何简化一个给定的系统的变化取决于系统的使用情况和用户的目标，使用一个新的概念的效用模型来解决一个给定的性能问题。随着系统的变化，对简化模型进行调整将修改其参数，并修改简化的结构，将以前在参数跟踪方面的工作扩展到“结构跟踪”。第二大领域考虑模型的使用。模型效用的研究将以一般的方式描述性能问题。一个重要的新兴问题是在多个服务中心或多云上优化大型系统的部署，其中大量的中心间网络延迟。我们针对单个云的解决方案（学生Jim Li）并没有推广到延迟，但是学生Ravneet Kaur针对简单情况的算法解决了这个问题。它们涉及粗化和细化的图分区，以及装箱。实际问题将需要扩展。** **