Extended reality (XR) devices must render high-quality 3D graphics at low latency to deliver truly immersive experiences. However, XR devices are severely power- and resource-constrained, limiting the quality of on-device (local) rendering. Offloading rendering to a powerful remote machine can enhance graphics quality, but network latency can degrade the overall experience. To mask latency, XR systems reproject the rendered frame to compensate for user motion since the rendered pose. Traditional reprojection, known as TimeWarp, uses a lightweight mechanism to compensate for latency in rotational motion, but not translational motion. Compensating for translational motion is more expensive, but is increasingly important at higher latencies.

Power consumption, and the resulting limitation to computational load, is a first-order constraint in designing comfortable all-day-wear extended reality (XR) devices that can provide rich immersive experiences. This paper concerns reducing XR device power consumption by offloading head tracking, one of the top CPU and power consumers, to a remote server. We present RemoteVIO, the first open-source end-to-end XR system that offloads head tracking (visual inertial odometry or VIO) to a remote server. Our work distinguishes itself from past studies on computation offloading in XR by properly addressing two under-explored but critical aspects: 1) a comprehensive evaluation of user experience in a complete end-to-end XR system and 2) a quantification of the net power savings on real hardware.

Interactive networked applications require high throughput, low latency, and high reliability from the network to provide a seamless user experience. While meeting these three requirements simultaneously is difficult, there has been an emergence of heterogeneous virtual channels (HVCs) which support some subset of them at the expense of the others. For instance, URLLC sacrifices throughput to achieve low latency and reliability in 5G NR, and Wi-Fi 7 and other novel Internet architectures provide similar disparate types of service. Prior work either focuses on aggregating the bandwidth of these channels whilst neglecting their unique properties or fails to generalize in the sense of achieving high performance across different applications and channels. To utilize HVCs to their fullest, we argue that there are challenges and opportunities across the network, transport and application layers, and the application-transport interface of the network stack. In this work, we explore the trade-offs of these architectural choices in the context of web browsing and real-time video, and identify the constituting principles of a design that is general, performant, and deployable.

Modern software systems are often equipped with hundreds to thousands of configurations, many of which greatly affect performance. Unfortunately, properly setting these configurations is challenging for developers due to the complex and dynamic nature of system workload and environment. In this paper, we first conduct an empirical study to understand performance-sensitive configurations and the challenges of setting them in the real-world. Guided by our study, we design a systematic and general control-theoretic framework, SmartConf, to automatically set and dynamically adjust performance-sensitive configurations to meet required operating constraints while optimizing other performance metrics. Evaluation shows that SmartConf is effective in solving real-world configuration problems, often providing better performance than even the best static configuration developers can choose under existing configuration systems.