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需求无关，从而导致不可预测的性能。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。许多租户依赖过度配置和静态分配来实现性能隔离，这导致利用率低，成本和环境影响增加。本项目旨在构建一套解决方案，以实现高资源利用率的短期和长期性能可预测性。目标是使来自不同承租者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，同时坚持各个承租者的组织层次结构。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，所有这些都与网络的多资源和共享性质相结合。两项关键技术指导了所建议的工作：(i)时间调度通过短期和长期性能隔离确保可预测的性能，以及（ii）空间放置通过初始放置和租户虚拟机的定期迁移确保更高的利用率。可预测的、高效的数据分析将对社会经济产生重大影响。它还将支持关键任务应用，例如异常检测、欺诈保护、自动驾驶汽车和机器人，这些应用需要高度一致和可靠的性能水平，才能与不太敏感的应用共存。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，这个项目中的工件将以一种快速的方式从研究转化为实践。该项目具有重要的教育和推广组成部分，其中包括根据该项目的成果在研究生和本科阶段引入新课程，以及安排针对高中学生的云计算新兵训练营，其中包括妇女和代表性不足的少数民族。

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.