With faster wireless networks and server GPUs, offloading high-accuracy but compute-intensive AR tasks implemented in Deep Neural Networks (DNNs) to edge servers offers a promising way to support high-QoE Augmented/Mixed Reality (AR/MR) applications. A cost-effective way for AR app vendors to deploy such edge-assisted AR apps to support a large user base is to use commercial Machine-Learning-as-a-Service (MLaaS) deployed at the edge cloud. To maximize cost-effectiveness, such an MLaaS provider faces a key design challenge, i.e., how to maximize the number of clients concurrently served by each GPU server in its cluster while meeting per-client AR task accuracy SLAs. The above AR offloading inference serving problem differs from generic inference serving or video analytics serving in one fundamental way: due to the use of local tracking which reuses the last server-returned inference result to derive results for the current frame, the offloading frequency and end-to-end latency of each AR client directly affect its AR task accuracy (for all the frames).

For its stringent QoE requirement, augmented reality (AR) has been widely hailed as a representative of ultra-high bandwidth and ultra-low latency apps that will be enabled by 5G networks/edge clouds. Such a portrait of AR by the telco and cloud industry raises an important research question - can 5G enable latency-critical applications such as (edge-assisted) AR? In this paper, we conduct to our knowledge the first in-depth measurement study of whether 5G mmWave in combination with in-network edge cloud can support the baseline edge-assisted object detection. After we discover 5G mmWave is unlikely to achieve the level of uplink network performance needed to support a baseline edge-assisted object detection implementation in the near future, we quantify the performance benefits in retrofitting app-level optimizations developed in the pre-5G era on top of baseline edge-assisted object detection, as well as the performance benefits from hardware upgrade on the edge. We find that these optimizations can significantly boost object detection performance over both LTE and 5G mmWave; however, the improvement with 5G mmWave over LTE is marginal, and 5G mmWave still fails to provide satisfactory performance in all scenarios under consideration. Overall, we conclude that today's 5G mmWave deployment is not a deciding factor in enabling edge-assisted object detection.

Immersive applications such as Augmented Reality (AR) and Mixed Reality (MR) often need to perform multiple latency-critical tasks on every frame captured by the camera, which all require results to be available within the current frame interval. While such tasks are increasingly supported by Deep Neural Networks (DNNs) offloaded to edge servers due to their high accuracy but heavy computation, prior work has largely focused on offloading one task at a time. Compared to offloading a single task, where more frequent offloading directly translates into higher task accuracy, offloading of multiple tasks competes for shared edge server resources, and hence faces the additional challenge of balancing the offloading frequencies of different tasks to maximize the overall accuracy and hence app QoE.

5G networks are being deployed rapidly across the world and have opened door to many uplink-oriented, bandwidth-intensive applications such as Augmented Reality and Connected Autonomous Vehicles. However, the roll-out is still in the early phase and the nature of deployment also varies across different geographic regions of the world. In this demo, we present NextG-UP, an Android-based tool designed to help understand the performance and evolution of 5G networks around the world. The crowd-sourcing mobile app collects various cellular network metrics and runs a short uplink throughput/latency test.

5G networks are being deployed rapidly across the world and have opened door to many uplink-oriented, bandwidth-intensive applications such as Augmented Reality and Connected Autonomous Vehicles (CAV). However, the roll-out is still in the early phase and the nature of deployment also varies across different geographic regions of the world. In this demo, we present NextG-UP, an open-source Android-based tool designed to help understand the performance and evolution of 5G networks around the world. The crowd-sourcing mobile app collects various cellular network metrics and runs a short uplink throughput/latency test.

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these “deep” parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app's energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps.