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The emerging volumetric videos offer a fully immersive, six degrees of freedom (6DoF) viewing experience, at the cost of extremely high bandwidth demand. In this paper, we design, implement, and evaluate Vues, an edge-assisted transcoding system that delivers high-quality volumetric videos with low bandwidth requirement, low decoding overhead, and high quality of experience (QoE) on mobile devices. Through an IRB-approved user study, we build a f irst-of-its-kind QoE model to quantify the impact of various factors introduced by transcoding volumetric content into 2D videos. Motivated by the key observations from this user study, Vues employs a novel multiview approach with the overarching goal of boosting QoE. The Vues edge server adaptively transcodes a volumetric video frame into multiple 2D views with the help of a few lightweight machine learning models and strategically balances the extra bandwidth consumption of additional views and the improved QoE, indicated by our QoE model. The client selects the view that optimizes the QoE among the delivered candidates for display. Comprehensive evaluations using a prototype implementation indicate that Vues dramatically outperforms existing approaches. On average, it improves the QoE by 35% (up to 85%), compared to single-view transcoding schemes, and reduces the bandwidth consumption by 95%, compared to the state-of-the-art that directly streams volumetric videos. 

5G aims to offer not only significantly higher throughput than previous generations of cellular networks, but also promises millisecond (ms) and sub-millisecond (ultra-)low latency support at the 5G physical (PHY) layer for future applications. While prior measurement studies have confirmed that commercial 5G deployments can achieve up to several Gigabits per second (Gbps) throughput (especially with the mmWave 5G radio), are they able to deliver on the (sub) millisecond latency promise? With this question in mind, we conducted to our knowledge the first in-depth measurement study of commercial 5G mmWave PHY latency using detailed physical channel events and messages. Through carefully designed experiments and data analytics, we dissect various factors that influence 5G PHY latency of both downlink and uplink data transmissions, and explore their impacts on end-to-end delay. We find that while in the best cases, the 5G (mmWave) PHY-layer is capable of delivering ms/sub-ms latency (with a minimum of 0.09 ms for downlink and 0.76 ms for uplink), these happen rarely. A variety of factors such as channel conditions, re-transmissions, physical layer control and scheduling mechanisms, mobility, and application (edge) server placement can all contribute to increased 5G PHY latency (and thus end-to-end (E2E) delay). Our study provides insights to 5G vendors, carriers as well as application developers/content providers on how to better optimize or mitigate these factors for improved 5G latency performance. 

In this paper, we consider the challenges that arise from the need to scale virtualized network functions (VNFs) at 100 Gbps line speed and beyond. Traditional VNF designs are monolithic in state management and scheduling: internally maintaining all states and operations associated with them. Without proper design considerations, it suffers from limitations when scaling at 100 Gbps link speed and beyond: the inability of efficient utilization of the cache because of the contention due to the frequent control plane activities, computational/memory-intensive tasks taking up CPU times, shares states causing the synchronization among the cores. We address these limitations by arguing for the need to granularly decompose a VNF into data/control components that are co-located within a server but can be independently scaled among the cores. To realize the approach, we design a "serverless" programming framework with novel abstraction to optimize the data components that must process packets at the line speed, reduce the contention of the data states and enable run-time scheduling of different components for improved resource utilization. The abstractions, combined with the runtime system that we design, help NFV developers focus on the logic and correctness of VNF programming without worrying about how VNFs may be scaled in or out. We evaluate our platform by comparing it with monolithic approaches using different workloads and by analyzing its advantages of separation on scalability, performance determinism, and feature velocity. 

Machine learning datasets have elicited concerns about privacy, bias, and unethical applications, leading to the retraction of prominent datasets such as DukeMTMC, MS-Celeb-1M, and Tiny Images. In response, the machine learning community has called for higher ethical standards in dataset creation. To help inform these efforts, we studied three influential but ethically problematic face and person recognition datasets—Labeled Faces in the Wild (LFW), MS-Celeb-1M, and DukeMTMC— by analyzing nearly 1000 papers that cite them. We found that the creation of derivative datasets and models, broader technological and social change, the lack of clarity of licenses, and dataset management practices can introduce a wide range of ethical concerns. We conclude by suggesting a distributed approach to harm mitigation that considers the entire life cycle of a dataset.