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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.10891">arXiv:2502.10891</a> <span> [<a href="https://arxiv.org/pdf/2502.10891">pdf</a>, <a href="https://arxiv.org/format/2502.10891">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> AquaScope: Reliable Underwater Image Transmission on Mobile Devices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tian%2C+B">Beitong Tian</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+L">Lingzhi Zhao</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+B">Bo Chen</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+M">Mingyuan Wu</a>, <a href="/search/cs?searchtype=author&query=Zheng%2C+H">Haozhen Zheng</a>, <a href="/search/cs?searchtype=author&query=Vasisht%2C+D">Deepak Vasisht</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Nahrstedt%2C+K">Klara Nahrstedt</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.10891v1-abstract-short" style="display: inline;"> Underwater communication is essential for both recreational and scientific activities, such as scuba diving. However, existing methods remain highly constrained by environmental challenges and often require specialized hardware, driving research into more accessible underwater communication solutions. While recent acoustic-based communication systems support text messaging on mobile devices, their… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10891v1-abstract-full').style.display = 'inline'; document.getElementById('2502.10891v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.10891v1-abstract-full" style="display: none;"> Underwater communication is essential for both recreational and scientific activities, such as scuba diving. However, existing methods remain highly constrained by environmental challenges and often require specialized hardware, driving research into more accessible underwater communication solutions. While recent acoustic-based communication systems support text messaging on mobile devices, their low data rates severely limit broader applications. We present AquaScope, the first acoustic communication system capable of underwater image transmission on commodity mobile devices. To address the key challenges of underwater environments -- limited bandwidth and high transmission errors -- AquaScope employs and enhances generative image compression to improve compression efficiency, and integrates it with reliability-enhancement techniques at the physical layer to strengthen error resilience. We implemented AquaScope on the Android platform and demonstrated its feasibility for underwater image transmission. Experimental results show that AquaScope enables reliable, low-latency image transmission while preserving perceptual image quality, across various bandwidth-constrained and error-prone underwater conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10891v1-abstract-full').style.display = 'none'; document.getElementById('2502.10891v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">15 pages, 26 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.11447">arXiv:2412.11447</a> <span> [<a href="https://arxiv.org/pdf/2412.11447">pdf</a>, <a href="https://arxiv.org/format/2412.11447">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Zeal: Rethinking Large-Scale Resource Allocation with "Decouple and Decompose" </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+Z">Zhiying Xu</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+M">Minlan Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.11447v1-abstract-short" style="display: inline;"> Resource allocation is fundamental for cloud systems to ensure efficient resource sharing among tenants. However, the scale of such optimization problems has outgrown the capabilities of commercial solvers traditionally employed in production. To scale up resource allocation, prior approaches either tailor solutions to specific problems or rely on assumptions tied to particular workloads. In this… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.11447v1-abstract-full').style.display = 'inline'; document.getElementById('2412.11447v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.11447v1-abstract-full" style="display: none;"> Resource allocation is fundamental for cloud systems to ensure efficient resource sharing among tenants. However, the scale of such optimization problems has outgrown the capabilities of commercial solvers traditionally employed in production. To scale up resource allocation, prior approaches either tailor solutions to specific problems or rely on assumptions tied to particular workloads. In this work, we revisit real-world resource allocation problems and uncover a common underlying structure: a vast majority of these problems are inherently separable, i.e., they optimize the aggregate utility of individual resource and demand allocations, under separate constraints for each resource and each demand. Building on this insight, we develop DeDe, a general, scalable, and theoretically grounded framework for accelerating resource allocation through a "decouple and decompose" approach. DeDe systematically decouples entangled resource and demand constraints, thereby decomposing the overall optimization into alternating per-resource and per-demand allocations, which can then be solved efficiently and in parallel. We have implemented DeDe as a library extension to an open-source solver, maintaining a familiar user interface. Experimental results across three prominent resource allocation tasks -- traffic engineering, cluster scheduling, and load balancing -- demonstrate DeDe's substantial speedups and robust allocation quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.11447v1-abstract-full').style.display = 'none'; document.getElementById('2412.11447v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03339">arXiv:2410.03339</a> <span> [<a href="https://arxiv.org/pdf/2410.03339">pdf</a>, <a href="https://arxiv.org/format/2410.03339">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Tarzan: Passively-Learned Real-Time Rate Control for Video Conferencing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Agarwal%2C+N">Neil Agarwal</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+R">Rui Pan</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Netravali%2C+R">Ravi Netravali</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.03339v1-abstract-short" style="display: inline;"> Rate control algorithms are at the heart of video conferencing platforms, determining target bitrates that match dynamic network characteristics for high quality. Recent data-driven strategies have shown promise for this challenging task, but the performance degradation they introduce during training has been a nonstarter for many production services, precluding adoption. This paper aims to bolste… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03339v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03339v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03339v1-abstract-full" style="display: none;"> Rate control algorithms are at the heart of video conferencing platforms, determining target bitrates that match dynamic network characteristics for high quality. Recent data-driven strategies have shown promise for this challenging task, but the performance degradation they introduce during training has been a nonstarter for many production services, precluding adoption. This paper aims to bolster the practicality of data-driven rate control by presenting an alternative avenue for experiential learning: leveraging purely existing telemetry logs produced by the incumbent algorithm in production. We observe that these logs contain effective decisions, although often at the wrong times or in the wrong order. To realize this approach despite the inherent uncertainty that log-based learning brings (i.e., lack of feedback for new decisions), our system, Tarzan, combines a variety of robust learning techniques (i.e., conservatively reasoning about alternate behavior to minimize risk and using a richer model formulation to account for environmental noise). Across diverse networks (emulated and real-world), Tarzan outperforms the widely deployed GCC algorithm, increasing average video bitrates by 15-39% while reducing freeze rates by 60-100%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03339v1-abstract-full').style.display = 'none'; document.getElementById('2410.03339v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.19867">arXiv:2409.19867</a> <span> [<a href="https://arxiv.org/pdf/2409.19867">pdf</a>, <a href="https://arxiv.org/format/2409.19867">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Balancing Generalization and Specialization: Offline Metalearning for Bandwidth Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gottipati%2C+A">Aashish Gottipati</a>, <a href="/search/cs?searchtype=author&query=Khairy%2C+S">Sami Khairy</a>, <a href="/search/cs?searchtype=author&query=Hosseinkashi%2C+Y">Yasaman Hosseinkashi</a>, <a href="/search/cs?searchtype=author&query=Mittag%2C+G">Gabriel Mittag</a>, <a href="/search/cs?searchtype=author&query=Gopal%2C+V">Vishak Gopal</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Cutler%2C+R">Ross Cutler</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.19867v1-abstract-short" style="display: inline;"> User experience in real-time video applications requires continuously adjusting video encoding bitrates to match available network capacity, which hinges on accurate bandwidth estimation (BWE). However, network heterogeneity prevents a one-size-fits-all solution to BWE, motivating the demand for personalized approaches. Although personalizing BWE algorithms offers benefits such as improved adaptab… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19867v1-abstract-full').style.display = 'inline'; document.getElementById('2409.19867v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.19867v1-abstract-full" style="display: none;"> User experience in real-time video applications requires continuously adjusting video encoding bitrates to match available network capacity, which hinges on accurate bandwidth estimation (BWE). However, network heterogeneity prevents a one-size-fits-all solution to BWE, motivating the demand for personalized approaches. Although personalizing BWE algorithms offers benefits such as improved adaptability to individual network conditions, it faces the challenge of data drift -- where estimators degrade over time due to evolving network environments. To address this, we introduce Ivy, a novel method for BWE that leverages offline metalearning to tackle data drift and maximize end-user Quality of Experience (QoE). Our key insight is that dynamically selecting the most suitable BWE algorithm for current network conditions allows for more effective adaption to changing environments. Ivy is trained entirely offline using Implicit Q-learning, enabling it to learn from individual network conditions without a single, live videoconferencing interaction, thereby reducing deployment complexity and making Ivy more practical for real-world personalization. We implemented our method in a popular videoconferencing application and demonstrated that Ivy can enhance QoE by 5.9% to 11.2% over individual BWE algorithms and by 6.3% to 11.4% compared to existing online meta heuristics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19867v1-abstract-full').style.display = 'none'; document.getElementById('2409.19867v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, in review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.10897">arXiv:2409.10897</a> <span> [<a href="https://arxiv.org/pdf/2409.10897">pdf</a>, <a href="https://arxiv.org/format/2409.10897">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> </div> </div> <p class="title is-5 mathjax"> AutoSpec: Automated Generation of Neural Network Specifications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jin%2C+S">Shuowei Jin</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Tan%2C+C">Cheng Tan</a>, <a href="/search/cs?searchtype=author&query=Kalia%2C+A">Anuj Kalia</a>, <a href="/search/cs?searchtype=author&query=Foukas%2C+X">Xenofon Foukas</a>, <a href="/search/cs?searchtype=author&query=Mao%2C+Z+M">Z. Morley Mao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.10897v2-abstract-short" style="display: inline;"> The increasing adoption of neural networks in learning-augmented systems highlights the importance of model safety and robustness, particularly in safety-critical domains. Despite progress in the formal verification of neural networks, current practices require users to manually define model specifications -- properties that dictate expected model behavior in various scenarios. This manual process… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10897v2-abstract-full').style.display = 'inline'; document.getElementById('2409.10897v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10897v2-abstract-full" style="display: none;"> The increasing adoption of neural networks in learning-augmented systems highlights the importance of model safety and robustness, particularly in safety-critical domains. Despite progress in the formal verification of neural networks, current practices require users to manually define model specifications -- properties that dictate expected model behavior in various scenarios. This manual process, however, is prone to human error, limited in scope, and time-consuming. In this paper, we introduce AutoSpec, the first framework to automatically generate comprehensive and accurate specifications for neural networks in learning-augmented systems. We also propose the first set of metrics for assessing the accuracy and coverage of model specifications, establishing a benchmark for future comparisons. Our evaluation across four distinct applications shows that AutoSpec outperforms human-defined specifications as well as two baseline approaches introduced in this study. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10897v2-abstract-full').style.display = 'none'; document.getElementById('2409.10897v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.01617">arXiv:2404.01617</a> <span> [<a href="https://arxiv.org/pdf/2404.01617">pdf</a>, <a href="https://arxiv.org/format/2404.01617">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Designing Network Algorithms via Large Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=He%2C+Z">Zhiyuan He</a>, <a href="/search/cs?searchtype=author&query=Gottipati%2C+A">Aashish Gottipati</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+L">Lili Qiu</a>, <a href="/search/cs?searchtype=author&query=Luo%2C+X">Xufang Luo</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+K">Kenuo Xu</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yuqing Yang</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.01617v2-abstract-short" style="display: inline;"> We introduce NADA, the first framework to autonomously design network algorithms by leveraging the generative capabilities of large language models (LLMs). Starting with an existing algorithm implementation, NADA enables LLMs to create a wide variety of alternative designs in the form of code blocks. It then efficiently identifies the top-performing designs through a series of filtering techniques… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.01617v2-abstract-full').style.display = 'inline'; document.getElementById('2404.01617v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.01617v2-abstract-full" style="display: none;"> We introduce NADA, the first framework to autonomously design network algorithms by leveraging the generative capabilities of large language models (LLMs). Starting with an existing algorithm implementation, NADA enables LLMs to create a wide variety of alternative designs in the form of code blocks. It then efficiently identifies the top-performing designs through a series of filtering techniques, minimizing the need for full-scale evaluations and significantly reducing computational costs. Using adaptive bitrate (ABR) streaming as a case study, we demonstrate that NADA produces novel ABR algorithms -- previously unknown to human developers -- that consistently outperform the original algorithm in diverse network environments, including broadband, satellite, 4G, and 5G. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.01617v2-abstract-full').style.display = 'none'; document.getElementById('2404.01617v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.06324">arXiv:2403.06324</a> <span> [<a href="https://arxiv.org/pdf/2403.06324">pdf</a>, <a href="https://arxiv.org/format/2403.06324">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> ACM MMSys 2024 Bandwidth Estimation in Real Time Communications Challenge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khairy%2C+S">Sami Khairy</a>, <a href="/search/cs?searchtype=author&query=Mittag%2C+G">Gabriel Mittag</a>, <a href="/search/cs?searchtype=author&query=Gopal%2C+V">Vishak Gopal</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Niu%2C+Z">Zhixiong Niu</a>, <a href="/search/cs?searchtype=author&query=Ameri%2C+E">Ezra Ameri</a>, <a href="/search/cs?searchtype=author&query=Inglis%2C+S">Scott Inglis</a>, <a href="/search/cs?searchtype=author&query=Golestaneh%2C+M">Mehrsa Golestaneh</a>, <a href="/search/cs?searchtype=author&query=Cutler%2C+R">Ross Cutler</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.06324v2-abstract-short" style="display: inline;"> The quality of experience (QoE) delivered by video conferencing systems to end users depends in part on correctly estimating the capacity of the bottleneck link between the sender and the receiver over time. Bandwidth estimation for real-time communications (RTC) remains a significant challenge, primarily due to the continuously evolving heterogeneous network architectures and technologies. From t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06324v2-abstract-full').style.display = 'inline'; document.getElementById('2403.06324v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.06324v2-abstract-full" style="display: none;"> The quality of experience (QoE) delivered by video conferencing systems to end users depends in part on correctly estimating the capacity of the bottleneck link between the sender and the receiver over time. Bandwidth estimation for real-time communications (RTC) remains a significant challenge, primarily due to the continuously evolving heterogeneous network architectures and technologies. From the first bandwidth estimation challenge which was hosted at ACM MMSys 2021, we learned that bandwidth estimation models trained with reinforcement learning (RL) in simulations to maximize network-based reward functions may not be optimal in reality due to the sim-to-real gap and the difficulty of aligning network-based rewards with user-perceived QoE. This grand challenge aims to advance bandwidth estimation model design by aligning reward maximization with user-perceived QoE optimization using offline RL and a real-world dataset with objective rewards which have high correlations with subjective audio/video quality in Microsoft Teams. All models submitted to the grand challenge underwent initial evaluation on our emulation platform. For a comprehensive evaluation under diverse network conditions with temporal fluctuations, top models were further evaluated on our geographically distributed testbed by using each model to conduct 600 calls within a 12-day period. The winning model is shown to deliver comparable performance to the top behavior policy in the released dataset. By leveraging real-world data and integrating objective audio/video quality scores as rewards, offline RL can therefore facilitate the development of competitive bandwidth estimators for RTC. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06324v2-abstract-full').style.display = 'none'; document.getElementById('2403.06324v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.12333">arXiv:2305.12333</a> <span> [<a href="https://arxiv.org/pdf/2305.12333">pdf</a>, <a href="https://arxiv.org/format/2305.12333">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> GRACE: Loss-Resilient Real-Time Video through Neural Codecs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cheng%2C+Y">Yihua Cheng</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Ziyi Zhang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Hanchen Li</a>, <a href="/search/cs?searchtype=author&query=Arapin%2C+A">Anton Arapin</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yue Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Q">Qizheng Zhang</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yuhan Liu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+X">Xu Zhang</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Mazumdar%2C+A">Amrita Mazumdar</a>, <a href="/search/cs?searchtype=author&query=Feamster%2C+N">Nick Feamster</a>, <a href="/search/cs?searchtype=author&query=Jiang%2C+J">Junchen Jiang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.12333v4-abstract-short" style="display: inline;"> In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements. To counter packet losses without retransmission, two primary strategies are employed -- encoder-based forward error correction (FEC) and decoder-based error concealment. The former encodes data with redundancy before transmission, yet determining the optimal re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.12333v4-abstract-full').style.display = 'inline'; document.getElementById('2305.12333v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.12333v4-abstract-full" style="display: none;"> In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements. To counter packet losses without retransmission, two primary strategies are employed -- encoder-based forward error correction (FEC) and decoder-based error concealment. The former encodes data with redundancy before transmission, yet determining the optimal redundancy level in advance proves challenging. The latter reconstructs video from partially received frames, but dividing a frame into independently coded partitions inherently compromises compression efficiency, and the lost information cannot be effectively recovered by the decoder without adapting the encoder. We present a loss-resilient real-time video system called GRACE, which preserves the user's quality of experience (QoE) across a wide range of packet losses through a new neural video codec. Central to GRACE's enhanced loss resilience is its joint training of the neural encoder and decoder under a spectrum of simulated packet losses. In lossless scenarios, GRACE achieves video quality on par with conventional codecs (e.g., H.265). As the loss rate escalates, GRACE exhibits a more graceful, less pronounced decline in quality, consistently outperforming other loss-resilient schemes. Through extensive evaluation on various videos and real network traces, we demonstrate that GRACE reduces undecodable frames by 95% and stall duration by 90% compared with FEC, while markedly boosting video quality over error concealment methods. In a user study with 240 crowdsourced participants and 960 subjective ratings, GRACE registers a 38% higher mean opinion score (MOS) than other baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.12333v4-abstract-full').style.display = 'none'; document.getElementById('2305.12333v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.12180">arXiv:2212.12180</a> <span> [<a href="https://arxiv.org/pdf/2212.12180">pdf</a>, <a href="https://arxiv.org/format/2212.12180">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zibo Wang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+P">Pinghe Li</a>, <a href="/search/cs?searchtype=author&query=Liang%2C+C+M">Chieh-Jan Mike Liang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+F">Feng Wu</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.12180v5-abstract-short" style="display: inline;"> Achieving resource efficiency while preserving end-user experience is non-trivial for cloud application operators. As cloud applications progressively adopt microservices, resource managers are faced with two distinct levels of system behavior: end-to-end application latency and per-service resource usage. Translating between the two levels, however, is challenging because user requests traverse h… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.12180v5-abstract-full').style.display = 'inline'; document.getElementById('2212.12180v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.12180v5-abstract-full" style="display: none;"> Achieving resource efficiency while preserving end-user experience is non-trivial for cloud application operators. As cloud applications progressively adopt microservices, resource managers are faced with two distinct levels of system behavior: end-to-end application latency and per-service resource usage. Translating between the two levels, however, is challenging because user requests traverse heterogeneous services that collectively (but unevenly) contribute to the end-to-end latency. We present Autothrottle, a bi-level resource management framework for microservices with latency SLOs (service-level objectives). It architecturally decouples application SLO feedback from service resource control, and bridges them through the notion of performance targets. Specifically, an application-wide learning-based controller is employed to periodically set performance targets -- expressed as CPU throttle ratios -- for per-service heuristic controllers to attain. We evaluate Autothrottle on three microservice applications, with workload traces from production scenarios. Results show superior CPU savings, up to 26.21% over the best-performing baseline and up to 93.84% over all baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.12180v5-abstract-full').style.display = 'none'; document.getElementById('2212.12180v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by USENIX NSDI '24</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.13763">arXiv:2210.13763</a> <span> [<a href="https://arxiv.org/pdf/2210.13763">pdf</a>, <a href="https://arxiv.org/format/2210.13763">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Teal: Learning-Accelerated Optimization of WAN Traffic Engineering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+Z">Zhiying Xu</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+R">Rachee Singh</a>, <a href="/search/cs?searchtype=author&query=Chiu%2C+J+T">Justin T. Chiu</a>, <a href="/search/cs?searchtype=author&query=Rush%2C+A+M">Alexander M. Rush</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+M">Minlan Yu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.13763v4-abstract-short" style="display: inline;"> The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale. Existing acceleration strategies decompose TE optimization into concurrent subproblems but realize limited parallelism due to an inherent tradeoff between run time and allocation performance. We present Te… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.13763v4-abstract-full').style.display = 'inline'; document.getElementById('2210.13763v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.13763v4-abstract-full" style="display: none;"> The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale. Existing acceleration strategies decompose TE optimization into concurrent subproblems but realize limited parallelism due to an inherent tradeoff between run time and allocation performance. We present Teal, a learning-based TE algorithm that leverages the parallel processing power of GPUs to accelerate TE control. First, Teal designs a flow-centric graph neural network (GNN) to capture WAN connectivity and network flows, learning flow features as inputs to downstream allocation. Second, to reduce the problem scale and make learning tractable, Teal employs a multi-agent reinforcement learning (RL) algorithm to independently allocate each traffic demand while optimizing a central TE objective. Finally, Teal fine-tunes allocations with ADMM (Alternating Direction Method of Multipliers), a highly parallelizable optimization algorithm for reducing constraint violations such as overutilized links. We evaluate Teal using traffic matrices from Microsoft's WAN. On a large WAN topology with >1,700 nodes, Teal generates near-optimal flow allocations while running several orders of magnitude faster than the production optimization engine. Compared with other TE acceleration schemes, Teal satisfies 6--32% more traffic demand and yields 197--625x speedups. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.13763v4-abstract-full').style.display = 'none'; document.getElementById('2210.13763v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.05940">arXiv:2202.05940</a> <span> [<a href="https://arxiv.org/pdf/2202.05940">pdf</a>, <a href="https://arxiv.org/format/2202.05940">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Automatic Curriculum Generation for Learning Adaptation in Networking </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xia%2C+Z">Zhengxu Xia</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Y">Yajie Zhou</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Jiang%2C+J">Junchen Jiang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.05940v2-abstract-short" style="display: inline;"> As deep reinforcement learning (RL) showcases its strengths in networking and systems, its pitfalls also come to the public's attention--when trained to handle a wide range of network workloads and previously unseen deployment environments, RL policies often manifest suboptimal performance and poor generalizability. To tackle these problems, we present Genet, a new training framework for learnin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.05940v2-abstract-full').style.display = 'inline'; document.getElementById('2202.05940v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.05940v2-abstract-full" style="display: none;"> As deep reinforcement learning (RL) showcases its strengths in networking and systems, its pitfalls also come to the public's attention--when trained to handle a wide range of network workloads and previously unseen deployment environments, RL policies often manifest suboptimal performance and poor generalizability. To tackle these problems, we present Genet, a new training framework for learning better RL-based network adaptation algorithms. Genet is built on the concept of curriculum learning, which has proved effective against similar issues in other domains where RL is extensively employed. At a high level, curriculum learning gradually presents more difficult environments to the training, rather than choosing them randomly, so that the current RL model can make meaningful progress in training. However, applying curriculum learning in networking is challenging because it remains unknown how to measure the "difficulty" of a network environment. Instead of relying on handcrafted heuristics to determine the environment's difficulty level, our insight is to utilize traditional rule-based (non-RL) baselines: If the current RL model performs significantly worse in a network environment than the baselines, then the model's potential to improve when further trained in this environment is substantial. Therefore, Genet automatically searches for the environments where the current model falls significantly behind a traditional baseline scheme and iteratively promotes these environments as the training progresses. Through evaluating Genet on three use cases--adaptive video streaming, congestion control, and load balancing, we show that Genet produces RL policies which outperform both regularly trained RL policies and traditional baselines in each context, not only under synthetic workloads but also in real environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.05940v2-abstract-full').style.display = 'none'; document.getElementById('2202.05940v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by SIGCOMM'22</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2011.09611">arXiv:2011.09611</a> <span> [<a href="https://arxiv.org/pdf/2011.09611">pdf</a>, <a href="https://arxiv.org/format/2011.09611">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Implementing BOLA-BASIC on Puffer: Lessons for the use of SSIM in ABR logic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Marx%2C+E">Emily Marx</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Winstein%2C+K">Keith Winstein</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2011.09611v1-abstract-short" style="display: inline;"> One ABR algorithm implemented on Puffer is BOLA-BASIC, the simplest variant of BOLA. BOLA finds wide use in industry, notably in the MPEG-DASH reference player used as the basis for video players at Akamai, BBC, Orange, and CBS. The overall goal of BOLA is to maximize each encoded chunk's video quality while minimizing rebuffering. To measure video quality, Puffer uses the structural similarity me… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.09611v1-abstract-full').style.display = 'inline'; document.getElementById('2011.09611v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2011.09611v1-abstract-full" style="display: none;"> One ABR algorithm implemented on Puffer is BOLA-BASIC, the simplest variant of BOLA. BOLA finds wide use in industry, notably in the MPEG-DASH reference player used as the basis for video players at Akamai, BBC, Orange, and CBS. The overall goal of BOLA is to maximize each encoded chunk's video quality while minimizing rebuffering. To measure video quality, Puffer uses the structural similarity metric SSIM, whereas BOLA and other ABR algorithms like BBA, MPC, and Pensieve are more commonly implemented using bitrate (or a variant of bitrate). While bitrate is frequently used, BOLA allows the video provider to define its own proxy of video quality as the algorithm's "utility" function. However, using SSIM as utility proved surprisingly complex for BOLA-BASIC, despite the algorithm's simplicity. Given the rising popularity of SSIM and related quality metrics, we anticipate that a growing number of Puffer-like systems will face similar challenges. We hope developers of such systems find our experiences informative as they implement algorithms designed with bitrate-based utility in mind. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2011.09611v1-abstract-full').style.display = 'none'; document.getElementById('2011.09611v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1906.01113">arXiv:1906.01113</a> <span> [<a href="https://arxiv.org/pdf/1906.01113">pdf</a>, <a href="https://arxiv.org/format/1906.01113">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Learning in situ: a randomized experiment in video streaming </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yan%2C+F+Y">Francis Y. Yan</a>, <a href="/search/cs?searchtype=author&query=Ayers%2C+H">Hudson Ayers</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+C">Chenzhi Zhu</a>, <a href="/search/cs?searchtype=author&query=Fouladi%2C+S">Sadjad Fouladi</a>, <a href="/search/cs?searchtype=author&query=Hong%2C+J">James Hong</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+K">Keyi Zhang</a>, <a href="/search/cs?searchtype=author&query=Levis%2C+P">Philip Levis</a>, <a href="/search/cs?searchtype=author&query=Winstein%2C+K">Keith Winstein</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1906.01113v2-abstract-short" style="display: inline;"> We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.01113v2-abstract-full').style.display = 'inline'; document.getElementById('1906.01113v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1906.01113v2-abstract-full" style="display: none;"> We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real deployment environment. To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1906.01113v2-abstract-full').style.display = 'none'; document.getElementById('1906.01113v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 September, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 June, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2019. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> USENIX NSDI (2020) 495-511 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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