Live streaming that supports real-time interaction has become increasingly popular. To support the ensuing requirements on low end-to-end latency, RTM, the state-of-the-art live streaming system at Douyin, replaces the HTTP-FLV streaming protocol with WebRTC. To tailor the WebRTC stack to the live streaming scenario, we focus on optimizing first-frame delay, startup video rebuffering, audio-to-video drift, and per-session CPU usage. Those are the top-priority metrics identified from an importance analysis with respect to two user engagement metrics, i.e., viewer penetration and viewing time. To date, WebRTC-based streaming in RTM has been in operation for 4 years, and serves billions of viewer sessions every day. It dramatically optimizes QoE metrics (e.g., end-to-end latency reduced by 54.5%), and delivers statistically significant user engagement gains (e.g., number of paid orders increased by 0.8%). In this paper, we report our deployment experiences comprehensively.

With the rapid growth of IoT devices, the traditional cloud computing scheme is inefficient for many IoT based applications, mainly due to network data flood, long latency, and privacy issues. To this end, the edge computing scheme is proposed to mitigate these problems. However, in an edge computing system, the application development becomes more complicated as it involves increasing levels of edge nodes. Although some efforts have been introduced, existing edge computing frameworks still have some limitations in various application scenarios. To overcome these limitations, we propose a new programming model called Edge-Stream. It is a simple and programmer-friendly model, which can cover typical scenarios in edge-computing. Besides, we address several new issues, such as data sharing and area awareness, in this model. We also implement a prototype of edge-computing framework based on the Edge-Stream model. A comprehensive evaluation is provided based on the prototype. Experimental results demonstrate the effectiveness of the model.

Many Internet applications need high bandwidth but are not time sensitive. This motivates a congestion control "scavenger" that voluntarily yields to higher-priority applications, thus improving overall user experience. However, the existing scavenger protocol, LEDBAT, often fails to yield, has performance shortcomings, and requires a codebase separate from other transport protocols.

Auctions are believed to be effective methods to solve the problem of wireless spectrum allocation. Existing spectrum auction mechanisms are all centralized and suffer from several critical drawbacks of the centralized systems, which motivates the design of distributed spectrum auction mechanisms. However, extending a centralized spectrum auction to a distributed one broadens the strategy space of agents from one dimension (bid) to three dimensions (bid, communication, and computation), and thus cannot be solved by traditional approaches from mechanism design. In this paper, we propose two distributed spectrum auction mechanisms, namely distributed VCG and FAITH. Distributed VCG implements the celebrated Vickrey-Clarke-Groves mechanism in a distributed fashion to achieve optimal social welfare, at the cost of exponential communication overhead. In contrast, FAITH achieves sub-optimal social welfare with tractable computation and communication overhead. We prove that both of the two proposed mechanisms achieve faithfulness, i.e., the agents' individual utilities are maximized, if they follow the intended strategies. Besides, we extend FAITH to adapt to dynamic scenarios where agents can arrive or depart at any time, without violating the property of faithfulness. We implement distributed VCG and FAITH, and evaluate their performance in various setups. Evaluation results show that distributed VCG results in optimal allocation, while FAITH is more efficient in computation and communication.

Processing-in-Memory (PIM) has been proposed as a solution to accelerate data-intensive applications, such as real-time Big Data processing and neural networks. The acceleration of data processing using a PIM relies on its high internal memory bandwidth, which always comes with the cost of high power consumption. Consequently, it is important to have a comprehensive quantitative study of the power modeling and power management for such PIM architectures. In this work, we first model the relationship between the power consumption and the internal bandwidth of PIM. This model not only provides a guidance for PIM designs but also demonstrates the potential of power management via bandwidth throttling. Based on bandwidth throttling, we propose three techniques, Power-Aware Subtask Throttling (PAST), Processing Unit Boost (PUB), and Power Sprinting (PS), to improve the energy efficiency and performance. In order to demonstrate the universality of the proposed methods, we applied them to two kinds of popular PIM designs. Evaluations show that the performance of PIM can be further improved if the power consumption is carefully controlled. Targeting at the same performance, the peak power consumption of HMC-based PIM can be reduced from 20W to 15W. The proposed power management schemes improve the speedup of prior RRAM-based PIM from 69 × to 273 ×, after pushing the power usage from about 1W to 10W safely. The model also shows that emerging RRAM is more suitable for large processing-in-memory designs, due to its low power cost to store the data.

In the problem of routing in multi-hop wireless networks, to achieve high end-to-end throughput, it is crucial to find the “best” path from the source node to the destination node. Although a large number of routing protocols have been proposed to find the path with minimum total transmission count/time for delivering a single packet, such transmission count/time minimizing protocols cannot be guaranteed to achieve maximum end-to-end throughput. In this paper, we argue that by carefully considering spatial reusability of the wireless communication media, we can tremendously improve the end-to-end throughput in multi-hop wireless networks. To support our argument, we propose spatial reusability-aware single-path routing (SASR) and anypath routing (SAAR) protocols, and compare them with existing single-path routing and anypath routing protocols, respectively. Our evaluation results show that our protocols significantly improve the end-to-end throughput compared with existing protocols. Specifically, for single-path routing, the median throughput gain is up to 60 percent, and for each source-destination pair, the throughput gain is as high as 5.3x; for anypath routing, the maximum per-flow throughput gain is 71.6 percent, while the median gain is up to 13.2 percent.

In mobile wireless networks, the emerging proximity-based applications have led to the need for highly effective and energy-efficient neighbor discovery protocols. However, existing works cannot realize the optimal worst-case latency in the symmetric case, and their performances with asymmetric duty cycles can still be improved. In this paper, we investigate asynchronous neighbor discovery through a code-based approach, including the symmetric and asymmetric cases. We derive the tight worst-case latency bound in the case of symmetric duty cycle. We design a novel class of symmetric patterns called Diff-Codes, which is optimal when the Diff-Code can be extended from a perfect difference set. We further consider the asymmetric case and design ADiff-Codes. To evaluate (A)Diff-Codes, we conduct both simulations and testbed experiments. Both simulation and experiment results show that (A)Diff-Codes significantly outperform existing neighbor discovery protocols in both the median case and worst case. Specifically, in the symmetric case, the maximum worst-case improvement is up to 50%; in both symmetric and asymmetric cases, the median case gain is as high as 30%.

The surge of proximity-based applications on mobile devices has promoted the need for effective neighbor discovery protocols in mobile wireless networks. In contrast to existing works, which can achieve energy efficient neighbor discovery with bounded latency only in the scenario without strong interference, we aim at designing techniques for practical and robust neighbor discovery. We propose ReCorder to achieve robust neighbor discovery in mobile wireless networks despite the "noisy" communication media. Specifically, we exploit the cross-correlation property of pseudo-random sequences to eliminate the necessity of beacon decoding in existing neighbor discovery protocols. In ReCorder, a neighbor discovery message can be detected through cross-correlation on an RCover preamble, and contains a ReCord identity signature, which is unique for each of the nodes. We also design algorithms for RCover detection and ReCord recognization. The performance of ReCorder has been evalueated using the USRP-N210 testbed. Our evaluation results show that ReCorder can achieve robust neighbor discovery at an SINR lower than the existing beaconing and decoding based neighbor discovery protocols by almost 10dB. Furthermore, ReCorder can avoid degrading the decoding of background IEEE 802.11a/g transmissions with BPSK modulation, which is important for its co-existence with concurrent wireless streams.

In this paper, we argue that by carefully considering spatial reusability of the wireless communication media, we can tremendously improve the end-to-end throughput in multi-hop wireless networks. To support our argument, we propose spatial reusability-aware single-path routing (SASR) and anypath routing (SAAR) protocols, and compare them with existing single-path routing and anypath routing protocols, respectively. Our evaluation results show that our protocols significantly improve the end-to-end throughput compared with existing protocols. Specifically, for single-path routing, the throughput gain is up to 2.9× for anypath routing, the throughput gain is up to 62.7%.

In mobile wireless networks, the emerging proximity-based applications have led to needs for highly effective and energy-efficient neighbor discovery protocols. However, existing works cannot realize the optimal worst-case latency in symmetric case, and their performances with asymmetric duty cycles can still be improved. In this work, we investigate asynchronous neighbor discovery through a code-based approach, including the symmetric and asymmetric cases. We derive the tight worst-case latency bound in the case of symmetric duty cycle. We design a novel class of symmetric patterns called Diff-Codes, which is optimal when the Diff-Code can be extended from a perfect difference set. We further consider the asymmetric case, and design ADiff-Codes. To evaluate (A)Diff-Codes, we conduct both simulations and testbed experiments. Both simulation and experiment results show that (A)Diff-Codes significantly outperform existing neighbor discovery protocols in both the median case and worst-case. Specifically, in symmetric case, the maximum worst-case improvement is up to 50%; in both symmetric and asymmetric cases, the median case gain is as high as 30%.