XPS: FULL: A Cross-Layer Approach Toward Low-Latency Data-Parallel Applications in Rack-Scale Computing

尽管许多现代应用，例如，探索性分析和科学可视化具有严格的延迟要求，但如今的内存和横向扩展解决方案通常只提供尽力而为的服务。不可预测性的根本原因在于最小化I/O操作的传统设计原则。然而，随着机架级计算中更快的存储和网络的出现，I/O可能不再稀缺。这个项目重新审视了在这种新兴环境中横向扩展、低延迟应用程序的权衡和设计原则。有限的响应时间将减少过度配置并促进新的应用程序（例如，商业智能、机器人技术和重症监护病房）。项目研究结果将被整合到本科和研究生课程中，软件工件将面向学术界和工业界更广泛的社区开放。 该项目旨在利用新硬件功能的涌入，使应用程序基于有限的响应时间作为其主要设计标准。具体来说，该项目利用近似、推测和调度来屏蔽延迟敏感应用程序中的可变性。实现这一愿景的关键技术挑战在于做出一系列不同于常规的权衡：（i）该项目不是努力减少I/O，而是将I/O交换为更好的内存局部性，并积极推测以减少响应时间;（ii）在需要时，它采用近似技术来限制响应时间;最后，（iii）开发新的近似和推测感知的编码器以提高资源效率。该项目还研究了近似和推测处理的理论和经验边界，以及机架规模计算中的新时空调度技术。

Although many modern applications, e.g., exploratory analytics and scientific visualization, come with stringent latency requirements, today's in-memory and scale-out solutions often provide only best-effort services. A root cause of unpredictability lies in the traditional design principle of minimizing I/O operations. With the advent of faster storage and networks in rack-scale computing, however, I/O may no longer be scarce anymore. This project revisits the tradeoffs and design principles of scale-out, low-latency applications in this emerging context. Bounded response times will reduce over-provisioning and foster new applications (e.g., business intelligence, robotics, and intensive care units) that require consistent performance. Project findings will be integrated into undergraduate and graduate curricula, and software artifacts will be open-sourced for the wider community across academia and industry. This project aims to leverage the influx of new hardware capabilities to enable applications based on bounded response times as their primary design criteria. Specifically, the project leverages approximation, speculation, and scheduling to mask variabilities in latency-sensitive applications. The key technical challenge in realizing this vision lie in making a set of tradeoffs different from the norm: (i) rather than striving for less I/O, this project trades I/O off for better memory locality and aggressively speculate to reduce response times; (ii) when needed, it resorts to approximation techniques for bounded response times; and finally, (iii) it develops new approximation- and speculation-aware schedulers to increase resource efficiency. The project also investigates theoretical and empirical boundaries of approximate and speculative processing as well as new spatiotemporal scheduling techniques in rack-scale computing.