Real-time video analytics on high-resolution cameras has become a popular technology for various intelligent services like traffic control and crowd monitoring. While extensive work has been done on improving analytics accuracy with timing guarantees, virtually all of them target static viewpoint cameras. In this paper, we present Uirapuru, a novel framework for real-time, edge-based video analytics on high-resolution steerable cameras. The actuation performed by those cameras brings significant dynamism to the scene, presenting a critical challenge to existing popular approaches such as frame tiling. To address this problem, Uirapuru incorporates a comprehensive understanding of camera actuation into the system design paired with fast adaptive tiling at a per-frame level. We evaluate Uirapuru on a high-resolution video dataset, augmented by pan-tilt-zoom (PTZ) movements typical for steerable cameras and on real-world videos collected from an actual PTZ camera. Our experimental results show that Uirapuru provides up to 1.45× improvement in accuracy while respecting specified latency budgets or reaches up to 4.53× inference speedup with on-par accuracy compared to state-of-the-art static camera approaches.

The vision of sustainable IoT constructed from battery-free devices has attracted ample interest in the research community. Yet, efficient device discovery and synchronization—a fundamental problem in IoT systems—remains a critical challenge mainly due to the uncertain ambient energy availability across battery-free devices. We argue that bringing in a small level of certainty is necessary for facilitating communication in battery-free IoT. We propose Pulsar where we introduce a small number of battery-powered devices, serving as the communication coordinator for a large number of battery-free devices. We develop two communication schemes, namely one-to-one, and all-to-all, for Pulsar. Our results based on simulations and prototype-based experiments show that Pulsar achieves consistently good performance across different scenarios while requiring no special hardware or environmental conditions.

The rise of deep learning has led to various successful attempts to apply deep neural networks (DNNs) for important networking tasks such as intrusion detection. Yet, running DNNs in the network control plane, as typically done in existing proposals, suffers from high latency that impedes the practicality of such approaches. This paper introduces NetNN, a novel DNN-based intrusion detection system that runs completely in the network data plane to achieve low latency. NetNN adopts raw packet information as input, avoiding complicated feature engineering. NetNN mimics the DNN dataflow execution by mapping DNN parts to a network of programmable switches, executing partial DNN computations on individual switches, and generating packets carrying intermediate execution results between these switches. We implement NetNN in P4 and demonstrate the feasibility of such an approach. Experimental results show that NetNN can improve the intrusion detection accuracy to 99% while meeting the real-time requirement.

Abstract While high accuracy is of paramount importance for deep learning (DL) inference, serving inference requests on time is equally critical but has not been carefully studied especially when the request has to be served over a dynamic wireless network at the edge. In this paper, we propose Jellyfish—a novel edge DL inference serving system that achieves soft guarantees for end-to-end inference latency service-level objectives (SLO). Jellyfish handles the network variability by utilizing both data and deep neural network (DNN) adaptation to conduct tradeoffs between accuracy and latency. Jellyfish features a new design that enables collective adaptation policies where the decisions for data and DNN adaptations are aligned and coordinated among multiple users with varying network conditions. We propose efficient algorithms to continuously map users and adapt DNNs at runtime, so that we fulfill latency SLOs while maximizing the overall inference accuracy. We further investigate dynamic DNNs, i.e., DNNs that encompass multiple architecture variants, and demonstrate their potential benefit through preliminary experiments. Our experiments based on a prototype implementation and real-world WiFi and LTE network traces show that Jellyfish can meet latency SLOs at around the 99th percentile while maintaining high accuracy.

While high accuracy is of paramount importance for deep learning (DL) inference, serving inference requests on time is equally critical but has not been carefully studied especially when the request has to be served over a dynamic wireless network at the edge. In this paper, we propose Jellyfish—a novel edge DL inference serving system that achieves soft guarantees on end-to-end inference latency often specified as a service-level objective (SLO). To handle the network variability, Jellyfish exploits both data and deep neural network (DNN) adaptation to conduct tradeoffs between accuracy and latency. Jellyfish features a new design that enables collective adaptation policies where the decisions for data and DNN adaptations are aligned and coordinated among multiple users with varying network conditions. We propose efficient algorithms to dynamically adapt DNNs and map users, so that we fulfill latency SLOs while maximizing the overall inference accuracy. Our experiments based on a prototype implementation and real-world WiFi and LTE network traces show that Jellyfish can meet latency SLOs at around the 99th percentile while maintaining high accuracy.

Edge video analytics based on deep learning has become an important building block for many modern intelligent applications such as mobile augmented reality and autonomous driving. Various mechanisms have been developed to handle dynamic wireless networks, compute resource availability, and achieve high analytics accuracy via filtering, DNN compression, pruning, and adaptation. So far, limited attention has been paid to timeliness---providing strict service-level objectives (SLO) for edge video analytics pipelines, which is essential for the usability of user-interactive and mission-critical intelligent applications. In this paper, we analyze the challenges in achieving SLO for edge video analytics and present a system design for timely edge video analytics over the air leveraging a simple yet effective idea---feedback control. Our preliminary evaluation based on a system prototype and real-world network traces shows the potential of our design. We also discuss the limitations, calling for future work.

Deep learning (DL) has shown promising results on complex computer vision tasks for video stream analytics recently. However, DL-based analytics typically requires intensive computation, which imposes challenges to the current computing infrastructure. In particular, cloud-only solutions struggle to maintain stable real-time performance due to the streaming over the best-effort Internet, while edge-only solutions require the DL model to be optimized (e.g., pruned or quantized) carefully to fit on resource-constrained devices, affecting the analytics quality. In this paper, we propose Clownfish, a framework for efficient video stream analytics that achieves symbiosis of the edge and the cloud. Clownfish deploys a lightweight optimized DL model at the edge for fast response and a complete DL model at the cloud for high accuracy. By exploiting the temporal correlation in video content, Clownfish sends only a subset of video frames intermittently to the cloud and enhances the analytics quality by fusing the results from the cloud model with these from the edge model. Our evaluation based on a system prototype shows that Clownfish always runs in real time and is able to achieve analytics quality comparable to that of cloud-only solutions, even under highly variable network conditions. Clownfish is generally applicable to all video stream analytics tasks that can leverage temporal correlations.

Use-after-free vulnerabilities due to dangling pointers are an important and growing threat to systems security. While various solutions exist to address this problem, none of them is sufficiently practical for real-world adoption. Some can be bypassed by attackers, others cannot support complex multithreaded applications prone to dangling pointers, and the remainder have prohibitively high overhead. One major source of overhead is the need to synchronize threads on every pointer write due to pointer tracking.