NeTS: Medium: SLATE: Service Layer Traffic Engineering

大多数人每天都在他们的手机和笔记本电脑上使用在线应用程序-网站，地图，视频会议，购物，消息传递等等。 这些应用程序托管在云中，这意味着其应用程序逻辑、数据库和存储的重要部分运行在大型数据中心的服务器上，通常位于全国或全球的多个站点。 这些应用程序通常涉及许多不同的软件组件，在后台执行数十或数百个小任务，以共同产生用户想要的结果，如网页或高质量视频流。由于这些云托管应用程序的复杂性以及它们从用户那里接收的不可预测的工作负载，因此很难优化它们的性能和资源使用。 如今，典型的结果是应用程序的资源被过度配置--这意味着，它们被分配了比必要的多得多的计算资源; 70%甚至更多的浪费是常见的。 目前处理这个问题的方法试图更好地规划长期的资源分配，但对短期的工作量变化没有帮助，对与网络相关的成本也没有帮助。 SLATE将帮助云托管应用程序优化任务性能，以更少的资源浪费优雅地处理短期工作负载波动，并优化云带宽和计算成本。在技术层面上，SLATE将为现代云托管应用程序提供易于使用的优化，并在应用程序下方提供新的优化层。 SLATE专注于基于微服务的应用程序：这些应用程序将每个任务拆分为许多小组件，这些小组件在不同的服务器上运行。 SLATE将扩展现有的开源服务网格，这些网格目前为基于微服务的应用程序提供网络功能。 然而，这涉及重大的新挑战。 首先，该项目将开发技术，自动优先考虑跨越多种资源类型（计算和网络容量）的瓶颈的请求。 其次，该项目将设计方法，在可能的服务器之间真实的时间路由请求，智能地权衡延迟，成本，带宽和多集群环境中的离群值考虑。 第三，该项目将开发一种基于理论的方法，将服务层流量工程问题分解为分散的设计，通过本地决策实现快速反应，并通过足够的全球协调实现最优，该项目将寻求通过显著改善云托管应用的性能、资源利用率和成本来实现更广泛的影响。 该项目还将支持扩大参与计算的活动，包括指导本科生（REU）学生的研究经验，吸引来自代表性不足的群体的申请人，并参加EECS新星研讨会。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Most people use online applications -- web sites, maps, videoconferencing, shopping, messaging, and more -- on their phones and laptops every day. These applications are hosted in clouds, meaning that important parts of their application logic, databases, and storage run on servers in large data centers, typically in multiple sites across the country or world. Such applications usually involve many distinct components of software performing dozens or hundreds of small tasks behind the scenes to collectively produce the result the user wants, like a web page or a high-quality video stream. Because of the complexity of these cloud-hosted applications and the unpredictable workload they receive from users, it is difficult to optimize their performance and resource usage. The typical result today is that applications' resources are overprovisioned -- meaning, they are allocated much more computing resources than necessary; 70% waste, or even more, is common. Current methods to deal with this problem try to plan better long-term resource allocations, but do not help with short-term workload changes and do not help with network-related costs.This project will design and develop a system called Service Layer Traffic Engineering (SLATE). SLATE will help cloud-hosted applications to optimize the performance of tasks, deal gracefully with short-term workload fluctuations with less resource waste, and optimize cloud bandwidth and computing costs. At a technical level, SLATE will provide easily usable optimization for modern cloud-hosted applications with a new optimization layer beneath the application. SLATE focuses on applications which are microservice-based: such applications split each task into many small components which run on different servers. SLATE will extend existing open-source service meshes, which today provide networking functionality for microservice-based applications. However, this involves significant new challenges. First, the project will develop techniques to prioritize requests automatically across bottlenecks spanning multiple resource types (computing and network capacity). Second, the project will design methods to route requests in real time among possible servers, intelligently trading off latency, cost, bandwidth, and outlier considerations in multi-cluster environments. Third, the project will develop a theoretically grounded approach to decompose the service layer traffic engineering problem into a decentralized design, with local decisions enabling fast reaction, and just enough global coordination to achieve optimality.The project will seek to achieve substantial broader impact by benefiting cloud-hosted applications with significant performance, resource utilization, and cost improvements. The project will also support activities to broaden participation in computing, including mentoring Research Experiences for Undergraduates (REU) students, attracting applicants from underrepresented groups, and participating in the EECS Rising Stars workshops.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.