Root causing and failure localization are critical to maintain reliability in cloud network operations. When an incident is reported, network operators must review massive volumes of monitoring data and identify the root cause (i.e., error device) as fast as possible, making it extremely challenging even for experienced operators. Large language models (LLMs) have shown great potential in text understanding and reasoning. In this paper, we present BiAn, an LLM-based framework designed to assist operators in efficient incident investigation. BiAn processes monitoring data and generates error device rankings with detailed explanations. To date, BiAn has been deployed in our network infrastructure for 10 months and it has successfully assisted operators in identifying error devices more quickly, reducing time to root causing by 20.5% (55.2% for high-risk incidents). Extensive performance evaluations based on 17 months of real cases further demonstrate that BiAn achieves accurate and fast failure localization. It improves accuracy by 9.2% compared to the baseline approach.

This paper presents CellFusion, a system designed for high-quality, real-time video streaming from vehicles to the cloud. It leverages an innovative blend of multipath QUIC transport and network coding. Surpassing the limitations of individual cellular carriers, CellFusion uses a unique last-mile overlay that integrates multiple cellular networks into a single, unified cloud connection. This integration is made possible through the use of in-vehicle Customer Premises Equipment (CPEs) and edge-cloud proxy servers.

We present GSO-Simulcast, a new architecture designed for large-scale multi-party video-conferencing systems. GSO-Simulcast is currently deployed at full-scale in Alibaba's Dingtalk video conferencing that serves more than 500 million users. It marks a fundamental shift from today's Simulcast, where a media server locally decides how to switch and forward video streams based on a fragmented network view. Instead, GSO-Simulcast globally orchestrates the publishing, subscribing, as well as the resolution and bitrate of video streams for each participant using a centralized controller that is aware of all network constraints in a meeting. The controller automatically modifies stream configurations to meet the participants' real-time network changes and updates. In doing so, GSO-Simulcast achieves multiple goals: (1) reducing video and network mismatch, (2) less path congestion, and (3) automated stream policy management. With the deployment of GSO-Simulcast, we observed more than a 35% reduction in the average video stall, 50% reduction in the average voice stall, and 6% improvement in the average video framerate. We describe the principle, design, deployment, and lessons learned.

Current UHF RFID systems suffer from two long-standing problems: 1) miss-reading non-line-of-sight or misoriented tags and 2) cross-reading undesired, distant tags due to multi-path reflections. This paper proposes a novel system, NFC+, to overcome the fundamental challenges. NFC+ is a magnetic field reader, which can inventory standard NFC tagged objects with a reasonably long range and arbitrary orientation. NFC+ achieves this by leveraging physical and algorithmic techniques based on magnetic resonance engineering. We build a prototype of NFC+ and conduct extensive evaluations in a logistic network. Comparing to UHF RFID, we find that NFC+ can reduce the miss-reading rate from 23% to 0.03%, and cross-reading rate from 42% to 0, for randomly oriented objects. NFC+ demonstrates high robustness for RFID unfriendly media (e.g., water bottles and metal cans). It can reliably read commercial NFC tags at a distance of up to 3 meters which, for the first time, enables NFC to be directly applied to practical logistics network applications.

This paper presents PLoRa, an ambient backscatter design that enables long-range wireless connectivity for batteryless IoT devices. PLoRa takes ambient LoRa transmissions as the excitation signals, conveys data by modulating an excitation signal into a new standard LoRa "chirp" signal, and shifts this new signal to a different LoRa channel to be received at a gateway faraway. PLoRa achieves this by a holistic RF front-end hardware and software design, including a low-power packet detection circuit, a blind chirp modulation algorithm and a low-power energy management circuit. To form a complete ambient LoRa backscatter network, we integrate a light-weight backscatter signal decoding algorithm with a MAC-layer protocol that work together to make coexistence of PLoRa tags and active LoRa nodes possible in the network. We prototype PLoRa on a four-layer printed circuit board, and test it in various outdoor and indoor environments. Our experimental results demonstrate that our prototype PCB PLoRa tag can backscatter an ambient LoRa transmission sent from a nearby LoRa node (20 cm away) to a gateway up to 1.1 km away, and deliver 284 bytes data every 24 minutes indoors, or every 17 minutes outdoors. We also simulate a 28-nm low-power FPGA based prototype whose digital baseband processor achieves 220 μW power consumption.