CAREER: Learning-Based Hardware and Software Techniques for Quality-of-Service-Aware Cloud Microservices

职业：基于学习的硬件和软件技术，用于服务质量感知的云微服务

• 批准号: 2326182
• 负责人: Christina Delimitrou
• 金额: $ 50万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326182&HistoricalAwards=false
• 关键词: CAREER; Learning; Based; Hardware; Software

Datacenters support a large and ever-increasing fraction of the world's digital computation power, including search engines, social networks, and machine learning analytics. As modern cloud services grow in popularity, their design shifts from supporting complex monolithic applications, to supporting collections of specialized, loosely-coupled microservices. Such microservices impact resource requirements by requiring fast network processing and low-latency memory accesses to achieve their quality-of-service (QoS) constraints. Dependencies among microservices also complicate compute cluster management, and can cause cascading QoS violations, hurting availability and service reliability. Guaranteeing the responsiveness expected from cloud services while using datacenters efficiently requires instead a joint hardware-software approach. This project takes a holistic view towards designing a system stack for interactive cloud microservices running on large-scale datacenters that is QoS-aware, and resource-efficient. By pursuing automated, learning-based techniques, this project highlights the value of leveraging practical machine learning techniques to better navigate the increasing complexity of the cloud, as more datacenter services switch to this new application model.At the hardware level, this project first quantifies the implications microservices have on server design, and second, explores their potential for hardware acceleration. At the software level, this work is developing a new cluster manager that accounts for the dependencies among microservices in an automated and transparent-to-the-user way, and guarantees end-to-end performance. Finally, to eliminate the cascading effects of QoS violations between microservices, this project includes a data-driven, online performance forecasting system. This system leverages the massive amount of monitoring data collected by cloud systems to anticipate upcoming QoS violations, and act on them before they degrade performance. By innovating in both hardware and software, this work will achieve performance and efficiency gains that neither hardware- nor software-only approaches can provide.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据中心支持世界数字计算能力的一个巨大且不断增长的部分，包括搜索引擎、社交网络和机器学习分析。随着现代云服务越来越受欢迎，其设计从支持复杂的单体应用程序转变为支持专门的松散耦合微服务集合。这些微服务通过要求快速网络处理和低延迟内存访问来实现其服务质量（QoS）约束，从而影响资源需求。微服务之间的重复性也使计算集群管理复杂化，并可能导致级联QoS违规，从而损害可用性和服务可靠性。在高效使用云计算中心的同时，确保云服务的响应速度，需要采用软硬件联合的方法。该项目从整体上设计了一个系统堆栈，用于在大规模网络中心上运行的交互式云微服务，该系统堆栈具有QoS感知和资源效率。随着越来越多的数据中心服务转向这种新的应用模式，该项目通过追求基于学习的自动化技术，强调了利用实用机器学习技术更好地驾驭云计算日益复杂性的价值。在硬件层面，该项目首先量化了微服务对服务器设计的影响，其次，探索了它们在硬件加速方面的潜力。在软件层面，这项工作正在开发一个新的集群管理器，以自动化和对用户的兼容性的方式解释微服务之间的依赖关系，并保证端到端的性能。最后，为了消除微服务之间QoS违规的级联效应，该项目包括一个数据驱动的在线性能预测系统。该系统利用云系统收集的大量监控数据来预测即将发生的QoS违规，并在它们降低性能之前对其采取行动。通过在硬件和软件方面的创新，这项工作将实现单纯的硬件和软件方法都无法提供的性能和效率提升。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。