Monitoring and Debugging of High Performance Distributed Heterogeneous Cloud Applications

高性能分布式异构云应用的监控和调试

• 批准号: 554158-2020
• 负责人: Dagenais, MichelMR
• 金额: $ 24.98万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754157
• 关键词: Monitoring; Debugging; Performance; Distributed; Heterogeneous

The communication and computing infrastructure is getting ever more sophisticated at an extremely rapid pace. Recent applications include 5G connected mobile devices, autonomous cars, smart robots and intelligent digital assistants powered by Machine Learning. These advances are made possible by a number of technological developments at the hardware and software levels, such as computer central processing units with tens of cores, coprocessors for graphics and intensive computations (GPGPU) with thousands of cores and over 18 billion logic elements, 5G low latency high speed networking, and Cloud based infrastructures that execute their requests in parallel.As a result, even a simple operation such as initiating a phone call, making a Web search, routing a packet or displaying a video frame, can involve many parallel cores on more than one processing unit, possibly on several servers. Moreover, the same operation, a few seconds later, may be served in a different way by different cores and physical servers in the Cloud. Therefore, understanding the performance of these operations has become extremely difficult and the tools for that purpose are severely lacking. In this project, the tracing, profiling, debugging and monitoring tools for High Performance Distributed Systems will be extended to efficiently extract information from all units in all layers, from the hardware to the applications, and cope with the large number of cores and computers. The project has a specific focus on Cloud applications connecting to mobile and Internet of Things devices through Edge servers and 5G networks, High Performance Computing exploiting the new generation of shared memory GPGPUs, Machine Learning applications, and a new modular architecture for more integrated software development tools. As a result, the designers and operators of High Performance Distributed Systems will have the tools in hand to quickly analyse their system performance, automatically or manually find problems, and optimise operations.

通信和计算基础设施正以极快的速度变得越来越复杂。最近的应用包括5G互联移动设备、自动驾驶汽车、智能机器人和由机器学习提供支持的智能数字助理。硬件和软件层面的许多技术发展使这些进步成为可能，例如具有数十个核心的计算机中央处理器、具有数千个核心和超过180亿个逻辑元素的图形和密集计算(GPGPU)、5G低延迟高速网络以及并行执行其请求的基于云的基础设施。因此，即使是发起电话、进行Web搜索、路由分组或显示视频帧这样的简单操作，也可能涉及多个处理单元上的许多并行核心，可能在几个服务器上。此外，同样的操作，几秒钟后，云中不同的核心和物理服务器可能会以不同的方式提供服务。因此，了解这些行动的执行情况变得极其困难，而且严重缺乏实现这一目的的工具。在本项目中，将对高性能分布式系统的跟踪、分析、调试和监控工具进行扩展，以便从硬件到应用的各个层次有效地提取信息，并应对大量的内核和计算机。该项目特别关注通过边缘服务器和5G网络连接到移动和物联网设备的云应用程序、利用新一代共享内存GPGPU的高性能计算、机器学习应用程序，以及用于更集成的软件开发工具的新的模块化架构。因此，高性能分布式系统的设计者和操作员将拥有工具，以快速分析其系统性能，自动或手动发现问题，并优化操作。