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With rapid evolution of mobile core network (MCN) architectures, large-scale control-plane traffic (CPT) traces are critical to studying MCN design and performance optimization by the R&D community. The prior-art control-plane traffic generator SMM heavily relies on domain knowledge which requires re-design as the domain evolves. In this work, we study the feasibility of developing a high-fidelity MCN control plane traffic generator by leveraging generative ML models. We identify key challenges in synthesizing high-fidelity CPT including generic (to data-plane) requirements such as multimodality feature relationships and unique requirements such as stateful semantics and long-term (time-of-day) data variations. We show state-of-the-art, generative adversarial network (GAN)-based approaches shown to work well for data-plane traffic cannot meet these fidelity requirements of CPT, and develop a transformer-based model, CPT-GPT, that accurately captures complex dependencies among the samples in each traffic stream (control events by the same UE) without the need for GAN. Our evaluation of CPT-GPT on a large-scale control-plane traffic trace shows that (1) it does not rely on domain knowledge yet synthesizes control-plane traffic with comparable fidelity as SMM; (2) compared to the prior-art GAN-based approach, it reduces the fraction of streams that violate stateful semantics by two orders of magnitude, the max y-distance of sojourn time distributions of streams by 16.0%, and the transfer learning time in deriving new hourly models by 3.36×. 

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. In this paper, we formulate this accuracy-centric multitask offloading problem, and present a framework that dynamically schedules the offloading of multiple DNN tasks from a mobile device to an edge server while optimizing the overall accuracy across tasks. Our design employs two novel ideas: (1) task-specific lightweight models that predict offloading accuracy drop as a function of offloading frequency and frame content, and (2) a general two-level control feedback loop that concurrently balances offloading among tasks and adapts between offloading and using local algorithms for each task. Evaluation results show that our framework improves the overall accuracy significantly in jointly offloading two core tasks in AR — depth estimation and odometry — by on average 7.6%–14.3% over the best baselines under different accuracy weight ratios. 

We revisit the performance of a canonical system design for edge-assisted AR that simply combines off-the-shelf H.264 video encoding with a standard object tracking technique. Our experimental analysis shows that the simple canonical design for edge-assisted object detection can achieve within 3.07%/1.51% of the accuracy of ideal offloading (which assumes infinite network bandwidth and the total network transmission time of a single RTT) under LTE/5G mmWave networks. Our findings suggest that recent trend towards sophisticated system architecture design for edge-assisted AR appears unnecessary. We provide insights for why video compression plus on-device object tracking is so effective in edge-assisted object detection, draw implications to edge-assisted AR research, and pose open problems that warrant further investigation into this surprise finding. 

Edge-assisted Augmented Reality (AR) which offloads computeintensive Deep Neural Network (DNN)-based AR tasks to edge servers faces an important design challenge: how to pick the DNN model out of many choices proposed for each AR task for offloading. For each AR task, e.g., depth estimation, many DNN-based models have been proposed over time that vary in accuracy and complexity. In general, more accurate models are also more complex; they are larger and have longer inference time. Thus choosing a larger model in offloading can provide higher accuracy for the offloaded frames but also incur longer turnaround time, during which the AR app has to reuse the estimation result from the last offloaded frame, which can lead to lower average accuracy. In this paper, we experimentally study this design tradeoff using depth estimation as a case study. We design optimal offloading schedule and further consider the impact of numerous factors such as on-device fast tracking, frame downsizing and available network bandwidth. Our results show that for edge-assisted monocular depth estimation, with proper frame downsizing and fast tracking, compared to small models, the improved accuracy of large models can offset its longer turnaround time to provide higher average estimation accuracy across frames under both LTE and 5G mmWave. 

In this paper, we study how to support high-quality immer- sive multiplayer VR on commodity mobile devices. First, we perform a scaling experiment that shows simply replicating the prior-art 2-layer distributed VR rendering architecture to multiple players cannot support more than one player due to the linear increase in network bandwidth requirement. Second, we propose to exploit the similarity of background environment (BE) frames to reduce the bandwidth needed for prefetching BE frames from the server, by caching and reusing similar frames. We nd that there is often little sim- ilarly between the BE frames of even adjacent locations in the virtual world due to a “near-object” e ect. We propose a novel technique that splits the rendering of BE frames between the mobile device and the server that drastically enhances the similarity of the BE frames and reduces the network load from frame caching. Evaluation of our imple- mentation on top of Unity and Google Daydream shows our new VR framework, Coterie, reduces per-player network requirement by 10.6X-25.7X and easily supports 4 players for high-resolution VR apps on Pixel 2 over 802.11ac, with 60 FPS and under 16ms responsiveness.