Realtime interactive streaming, an emerging paradigm that enables instantaneous two-way interactions with diverse streaming traffic, introduces new network challenges. This paper identifies a unique characteristic of this paradigm — cross-directional dependency. By leveraging this dependency, we propose a novel prioritization-based approach to optimize realtime interactive streaming. Our design aligns application-layer demands with transport-layer behavior, prioritizes critical traffic to mitigate buffer overflows caused by streaming microbursts, and transforms chaotic congestion into a predictable process. We extend the QUIC priority system to accommodate unreliable QUIC datagrams and develop an end-to-end framework for evaluating this emerging streaming paradigm. Extensive Internet-scale experiments validate the effectiveness of our system, demonstrating up to 9.4 × reduction in motion-to-photon latency and 82 × reduction in freeze frame rates.

In recent years, content hyper-giants have increasingly deployed server infrastructure and services close to end-users within “eyeball” networks. Still, online streaming analytics has largely remained unaffected by this trend. This is despite the fact that most of the “big data” is received in real-time and is most valuable at the time of arrival. The inability to process data at the network edge is caused by a common setting where user profiles, necessary for analytics, are stored deep in the data center backends. This setting also carries privacy concerns as such user profiles are individually identifiable, yet the users are almost blind to what data is associated with their identities and how the data is analyzed. In this article, we revise this arrangement, and plant encrypted semantic cookies at the user end. By redesigning the cookie content without altering existing protocols, semantic cookies enable the capture and pre-processing of user data at edge ISPs or CDNs while preserving user anonymity. Additionally, lightweight cryptographic algorithms like partially homomorphic encryption can protect web providers’ proprietary data from CDNs. We present Snatch , a QUIC-based streaming analytics prototype that achieves up to 200x faster user analytics, with common-case improvements of 10-30x.

In recent years, we have witnessed a growing trend of content hyper-giants deploying server infrastructure and services close to end-users, in "eyeball" networks. Still, one of the services that remained largely unaffected by this trend is online streaming analytics. This is despite the fact that most of the "big data" is received in real time and is most valuable at the time of arrival. The inability to process requests at the network edge is caused by a common setting where user profiles, necessary for analytics, are stored deep in the data center back-ends. This setting also carries privacy concerns as such user profiles are individually identifiable, yet the users are almost blind to what data is associated with their identities and how the data is analyzed. In this paper, we revise this arrangement, and plant encrypted semantic cookies at the user end. Without altering any of the existing protocols, this enables capturing and analytically pre-processing user requests soon after they are generated, at edge ISPs or content providers' off-nets. In addition, it ensures user anonymity perseverance during the analytics. We design and implement Snatch, a QUIC-based streaming analytics prototype, and demonstrate that it speeds up user analytics by up to 200x, and by 10-30x in the common case.

Rapid development has been witnessed in optimizing the performance of data centers over the past decade. However, such advances thriving in private data centers are rarely deployed in public-facing data centers. A major challenge is synchronizing optimization signals—such as flow sizes, server assignments, and load information—with the traffic they are intended to optimize, especially across networks controlled by different entities. In this paper, we propose CloudCookie, a versatile signal carrier within Internet protocols that ensures bidirectional signal presence without client-side cooperation. To exemplify CloudCookie's benefits on public-facing data center traffic, we design a set of easy-to-deploy data center infrastructures, including load balancers and switches, to leverage application layer awareness and enable efficient flow packet scheduling and load balancing. Our evaluation shows that these advances synergistically optimize the 99thpercentile of flow completion time by up to 20 x for the majority of flows.

Serverless computing has greatly simplified cloud programming. It liberates cloud tenants from various system administration and resource management tasks, such as configuration and provisioning. Under this new cloud computing paradigm, a single monolithic application is divided into separate stateless functions, i.e., function-as-a-service (FaaS), which are then orchestrated together to support complex business logic. But there is a fundamental cost associated with this enhanced flexibility. Internal network connections between functions are now initiated frequently, to support serverless features such as agile autoscaling and function chains, raising communication latency. To alleviate this cost, current serverless providers sacrifice security for performance, keeping internal function communications unencrypted.

In serverless computing [3], cloud providers manage responsibility for all server-related tasks, including both hardware resource allocation and software runtime preparation. Cloud tenants are thus free to simply focus on designing discrete stateless functions and orchestrate them together for their high-level business logic.

This paper presents WiPose, the first 3D human pose construction framework using commercial WiFi devices. From the pervasive WiFi signals, WiPose can reconstruct 3D skeletons composed of the joints on both limbs and torso of the human body. By overcoming the technical challenges faced by traditional camera-based human perception solutions, such as lighting and occlusion, the proposed WiFi human sensing technique demonstrates the potential to enable a new generation of applications such as health care, assisted living, gaming, and virtual reality. WiPose is based on a novel deep learning model that addresses a series of technical challenges. First, WiPose can encode the prior knowledge of human skeleton into the posture construction process to ensure the estimated joints satisfy the skeletal structure of the human body. Second, to achieve cross environment generalization, WiPose takes as input a 3D velocity profile which can capture the movements of the whole 3D space, and thus separate posture-specific features from the static objects in the ambient environment. Finally, WiPose employs a recurrent neural network (RNN) and a smooth loss to enforce smooth movements of the generated skeletons. Our evaluation results on a real-world WiFi sensing testbed with distributed antennas show that WiPose can localize each joint on the human skeleton with an average error of 2.83cm, achieving a 35% improvement in accuracy over the state-of-the-art posture construction model designed for dedicated radar sensors.