NeTS: Small: Collaborative Research: Enabling Application-Level Performance Predictability in Public Clouds

当今数据中心和计算云中最先进的资源共享机制与应用程序级性能要求无关，导致性能不可预测。对于网络来说尤其如此。与CPU、内存或磁盘不同，云运营商不为网络提供任何保证。许多租户依赖于过度配置和静态分配来实现性能隔离，这会导致低利用率以及增加的成本和环境影响。该项目旨在建立一套解决方案，以实现短期和长期的性能可预测性，高资源利用率。目标是使来自不同租户的共存应用程序能够满足各种性能目标，包括获得及时响应和最小化连续响应的差异，同时遵守各个租户的组织层次结构。该项目的关键技术挑战包括开发短期和长期资源分配算法，准确的需求估计以及快速有效的执行，所有这些都是由网络的多资源和共享性质所组成的。两个关键技术指导拟议的工作：（i）时间调度，确保可预测的性能，通过短期和长期的性能隔离，和（ii）空间布局，确保更高的利用率，通过初始放置和定期迁移租户的虚拟机。可预测的、高效的数据分析将产生重大的社会经济影响。它还将启用关键任务应用程序，例如，异常检测、欺诈保护、自动驾驶汽车和机器人技术--这些都需要高度一致和可靠的性能水平，才能与不太敏感的设备共存。该项目的算法和软件将被整合到现有的开源大数据堆栈中，供公众重用。通过利用与行业的持续关系，该项目的工件将以快速的方式从研究转化为实践。该项目具有重要的教育和外联内容，包括根据该项目的成果在研究生和本科生两级开设新课程，以及安排针对高中学生的云计算靴子训练营，并让妇女和代表性不足的少数群体参加。

State-of-the-art resource sharing mechanisms in today's datacenters and compute clouds are agnostic to application-level performance requirements, resulting in unpredictable performance. This is especially true for the network. Unlike, CPU, memory, or disk, cloud operators do not provide any guarantees for the network. Many tenants rely on over-provisioning and static allocation for performance isolation, which results in low utilization and increased cost and environmental impacts. This project aims to build a set of solutions to achieve short- and long-term performance predictability with high resource utilization. The goal is to enable coexisting applications from different tenants to meet a variety of performance objectives including obtaining timely responses and minimizing variance of successive responses, while adhering to organizational hierarchies of individual tenants. The key technical challenges in this project include developing short- and long-term resource allocation algorithms, accurate demand estimation, as well as fast and efficient enforcement, all of which are compounded by the multi-resource and shared nature of the network. Two key techniques guide the proposed work: (i) temporal scheduling ensures predictable performance through short- and long-term performance isolation, and (ii) spatial placement ensures higher utilization through initial placement and periodic migration of tenants' virtual machines. Predictable, efficient data analytics will have significant socio-economic ramifications. It will also enable mission-critical applications, e.g., anomaly detection, fraud protection, autonomous vehicles, and robotics-- that require a highly consistent and reliable level of performance to coexist with the less sensitive ones. Algorithms and software from the project will be incorporated into existing open-source big data stacks for public reuse. By leveraging ongoing relationships with the industry, artifacts from this project will be converted from research into practice in a fast manner. The project has significant educational and outreach components, which include introducing new courses at both graduate and undergraduate levels based on the outcomes of this project as well as arranging cloud computing boot camps aimed at students from high schools and involving women and under-represented minorities.