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

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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.