Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2023-10-08 16:20'''
|time='''2025-04-11 10:30-12:00'''
|addr=4th Research Building A518
|addr=4th Research Building A518
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|📚 Readling list]]; [[Resource:Seminar_schedules|📆 Schedules]]; [[Resource:Previous_Seminars|🧐 Previous seminars]].
}}
}}


===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=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. In order to effectively handle unstable cellular connections prone to intense burst losses and unexpected latency spikes as a vehicle moves, CellFusion introduces XNC. This innovative network coding-based transport solution enables efficient and resilient multipath transport. XNC aims to accomplish low latency, minimal traffic redundancy, and reduced computational complexity all at once. CellFusion is secure and transparent by nature and does not require modifications for vehicular apps connecting to it. We tested CellFusion on 100 self-driving vehicles for over six months with our cloud-native back-end running on 50 CDN PoPs. Through extensive road tests, we show that XNC reduced video packet delay by 71.53% at the 99th percentile versus 5G. At 30Mbps, CellFusion achieved 66.11% ~ 80.62% reduction in video stall ratio versus state-of-the-art multipath transport solutions with less than 10% traffic redundancy.
|abstract = 在AI革命汹涌来袭的当下连续创业者如何实现底层认知的进化？AI对技术的影响又如何影响到企业决策？报告人何仲潇系云起老和科技有限公司创始人/CEO，四川浙大校友会理事，浙大企业导师，成都市金熊猫B类人才。让我们跟随云起老和的视角感受AI浪潮中的创业进化历程！
|confname=SIGCOMM '23
|confname = 创新创业分享会
|link=https://dl.acm.org/doi/10.1145/3603269.3604832
|link = https://mobinets.cn/site/Resource:Seminar
|title=CellFusion: Multipath Vehicle-to-Cloud Video Streaming with Network Coding in the Wild
|title= AI革命浪潮中的进化--连续创业者的底层认知进化与创业选择
|speaker=Rong Cong
|speaker= 何仲潇
|date=2023-10-08}}
|date=2025-04-11
{{Latest_seminar
}}
|abstract=Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage devices, organized as independent failure-isolated components interconnected by a high-bandwidth network. A critical challenge, however, is the high performance penalty of accessing data from a remote memory module over the network. Addressing this challenge is difficult as disaggregated systems have high runtime variability in network latencies/bandwidth, and page migration can significantly delay critical path cache line accesses in other pages. This paper conducts a characterization analysis on different data movement strategies in fully disaggregated systems, evaluates their performance overheads in a variety of workloads, and introduces DaeMon, the first software-transparent mechanism to significantly alleviate data movement overheads in fully disaggregated systems. First, to enable scalability to multiple hardware components in the system, we enhance each compute and memory unit with specialized engines that transparently handle data migrations. Second, to achieve high performance and provide robustness across various network, architecture and application characteristics, we implement a synergistic approach of bandwidth partitioning, link compression, decoupled data movement of multiple granularities, and adaptive granularity selection in data movements. We evaluate DaeMon in a wide variety of workloads at different network and architecture configurations using a state-of-the-art simulator. DaeMon improves system performance and data access costs by 2.39× and 3.06×, respectively, over the widely-adopted approach of moving data at page granularity.
|confname=SigMetrics '23
|link=https://dl.acm.org/doi/abs/10.1145/3579445
|title=DaeMon: Architectural Support for Efficient Data Movement in Fully Disaggregated Systems
|speaker=Jiyi
|date=2023-10-08}}
{{Latest_seminar
|abstract=Realizing Digital Twins for Vehicular Networks: Towards Future Network Evolution
|confname=Tech. Talk
|link=#
|title=Rechargeable network
|speaker=Prof. Tang Liu
|date=2023-10-08}}
=== History ===


{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 09:27, 11 April 2025

Time: 2025-04-11 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [创新创业分享会] AI革命浪潮中的进化--连续创业者的底层认知进化与创业选择, 何仲潇
    Abstract: 在AI革命汹涌来袭的当下连续创业者如何实现底层认知的进化？AI对技术的影响又如何影响到企业决策？报告人何仲潇系云起老和科技有限公司创始人/CEO，四川浙大校友会理事，浙大企业导师，成都市金熊猫B类人才。让我们跟随云起老和的视角感受AI浪潮中的创业进化历程！

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

Instructions

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