Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-6-27 10:30'''
|time='''2024-09-20 10:30-12:00'''
|addr=4th Research Building A527-B
|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 = Recent advances in network and mobile computing.  
|abstract = Increasing bandwidth demands of mobile video streaming pose a challenge in optimizing the Quality of Experience (QoE) for better user engagement. Multipath transmission promises to extend network capacity by utilizing multiple wireless links simultaneously. Previous studies mainly tune the packet scheduler in multipath transmission, expecting higher QoE by accelerating transmission. However, since Adaptive BitRate (ABR) algorithms overlook the impact of multipath scheduling on throughput prediction, multipath adaptive streaming can even experience lower QoE than single-path. This paper proposes Chorus, a cross-layer framework that coordinates multipath scheduling with adaptive streaming to optimize QoE jointly. Chorus establishes two-way feedback control loops between the server and the client. Furthermore, Chorus introduces Coarse-grained Decisions, which assist appropriate bitrate selection by considering the scheduling decision in throughput prediction, and Finegrained Corrections, which meet the predicted throughput by QoE-oriented multipath scheduling. Extensive emulation and real-world mobile Internet evaluations show that Chorus outperforms the state-of-the-art MPQUIC scheduler, improving average QoE by 23.5% and 65.7%, respectively.  
|confname=talk
|confname=MobiCom' 24
|link=[Resource:Paper Carnival 2022|Paper Carnival 2022
|link = https://dl.acm.org/doi/pdf/10.1145/3636534.3649359
|title=]
|title= Chorus: Coordinating Mobile Multipath Scheduling and Adaptive Video Streaming
|speaker=all
|speaker=Jiahao
 
|date=2024-9-13
 
}}


{{Latest_seminar
|abstract = In Distributed Quantum Computing (DQC), quantum bits (qubits) used in a quantum circuit may be distributed on multiple Quantum Computers (QCs) connected by a Quantum Data Network (QDN). To perform a quantum gate operation involving two qubits on different QCs, we have to establish an Entanglement Connection (EC) between their host QCs. Existing EC establishment schemes result in a long EC establishment time, and low quantum resource utilization.In this paper, we propose an Asynchronous Entanglement Routing and Provisioning (AEPR) scheme to minimize the task completion time in DQC systems. AEPR has three distinct features: (i). Entanglement Paths (EPs) for a given SD pair are predetermined to eliminate the need for runtime calculation; (ii). Entanglement Links (ELs) are created proactively to reduce the time needed create EL on demand; and (iii). For a given EC request, quantum swapping along an EP is performed by a repeater whenever two adjacent ELs are created, so precious quantum resources at the repeater can be released immediately thereafter for other ELs and ECs. Extensive simulations show that AEPR can save up to 76.05% of the average task completion time in DQC systems compared with the state-of-the-art entanglement routing schemes designed to maximize QDN throughput.
|confname=INFOCOM' 23
|link = https://doi.org/10.1109/infocom53939.2023.10229101
|title= Asynchronous Entanglement Provisioning and Routing for Distributed Quantum Computing
|speaker=Yaliang
|date=2024-9-13
}}
}}
'''Visible Light Communication--Wenliang'''
[Sensys 2021] [https://dl.acm.org/doi/pdf/10.1145/3485730.3485934 CurveLight: An Accurate and Practical Indoor Positioning System]
[Sensys 2021] [https://dl.acm.org/doi/pdf/10.1145/3485730.3485948 SpiderWeb: Enabling Through-Screen Visible Light Communication]
[Kaiwen][ICNP2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9155474 X-MAC: Achieving High Scalability via Imperfect-Orthogonality Aware Scheduling in LPWAN]
'''Response to Mobility--Luwei'''
[Infocom2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796811 Enabling QoE Support for Interactive Applications over Mobile Edge with High User Mobility]
[Infocom2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796968 User Experience Oriented Task Computation for UAV-Assisted MEC System]
[TMC2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9343712 ECHO: Efficient Zero-Control-Packet Broadcasting for Mobile Ad Hoc Networks]
'''Mobility--Zhuoliu'''
[MobiCom21] [https://www.microsoft.com/en-us/research/uploads/prod/2021/09/Visage_Mobicom_2021.pdf Visage: enabling timely analytics for drone imagery]
'''Offloading, Delivery--Wenjie'''
[Infocom2022] [https://ieeexplore.ieee.org/document/9796843 An Efficient Two-Layer Task Offloading Scheme for MEC Networks with Multiple Services Providers]
[Infocom2022] [https://ieeexplore.ieee.org/document/9796714/ Two Time-Scale Joint Service Caching and Task Offloading for UAV-assisted Mobile Edge Computing]
[Infocom2022] [https://ieeexplore.ieee.org/document/9796763/ AoDNN: An Auto-Offloading Approach to Optimize Deep Inference for Fostering Mobile Web]
[TMC2022] [https://ieeexplore.ieee.org/document/9238459 A Force-Directed Approach to Seeking Route Recommendation in Ride-on-Demand Service Using Multi-Source Urban Data]
[Xinyu][INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796908/ IoTMosaic: Inferring User Activities from IoT Network Traffic in Smart Homes]
[Jiajun][INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796661/ Kalmia: A Heterogeneous QoS-aware Scheduling Framework for DNN Tasks on Edge Servers]
'''Video Service in Edge Networks--Congrong'''
[SigComm 2022] [https://dl.acm.org/doi/pdf/10.1145/3544216.3544218 NeuroScaler: neural video enhancement at scale]
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796984/ FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics]
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796657/ DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation]
[MobiHoc 2021] [https://dl.acm.org/doi/pdf/10.1145/3466772.3467034 Task Offloading with Uncertain Processing Cycles]
'''Edge, offloading, caching--Qingyong'''
[Infocom 2022] [https://ieeexplore.ieee.org/document/9796969/ Online File Caching in Latency-Sensitive Systems with Delayed Hits and Bypassing]
[Infocom 2022] [https://dl.acm.org/doi/10.1109/INFOCOM48880.2022.9796799 Distributed Cooperative Caching in Unreliable Edge Environments]
[TMC 2022] [https://ieeexplore.ieee.org/abstract/document/9832640 Reverse Auction-based Computation Offloading and Resource Allocation in Mobile Cloud-Edge Computing]
[YuanQi][NSDI 2022] [https://www.microsoft.com/en-us/research/uploads/prod/2021/07/nsdi22spring-final74.pdf Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers]
[Wangkun][Infocom 2022][https://ieeexplore.ieee.org/document/9796884/ Joint Resource Management and Flow Scheduling for SFC Deployment in Hybrid Edge-and-Cloud Network]
'''Communication-Efficient Federated Learning--Jianqi'''
[ICML 2022] [https://arxiv.org/pdf/2111.00465.pdf DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning]
[ICML 2022] [https://proceedings.mlr.press/v162/yi22a/yi22a.pdf QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning]
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796982/ Optimal Rate Adaption in Federated Learning with Compressed Communications]
'''Crowdsensing-Xianyang'''
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796960/ Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee]
[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796743/ Worker Selection Towards Data Completion for Online Sparse Crowdsensing]
[IoTJ] [https://ieeexplore.ieee.org/document/9828398/ Nondeterministic Mobility based Incentive Mechanism for Efficient Data Collection in Crowdsensing]
[Jiangshu][SIGCOMM2022] [https://dl.acm.org/doi/pdf/10.1145/3544216.3544238 From Luna to Solar: The Evolutions of the Compute-to-Storage Networks in Alibaba Cloud]
=== History ===


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

Latest revision as of 23:25, 20 September 2024

Time: 2024-09-20 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [MobiCom' 24] Chorus: Coordinating Mobile Multipath Scheduling and Adaptive Video Streaming, Jiahao
    Abstract: Increasing bandwidth demands of mobile video streaming pose a challenge in optimizing the Quality of Experience (QoE) for better user engagement. Multipath transmission promises to extend network capacity by utilizing multiple wireless links simultaneously. Previous studies mainly tune the packet scheduler in multipath transmission, expecting higher QoE by accelerating transmission. However, since Adaptive BitRate (ABR) algorithms overlook the impact of multipath scheduling on throughput prediction, multipath adaptive streaming can even experience lower QoE than single-path. This paper proposes Chorus, a cross-layer framework that coordinates multipath scheduling with adaptive streaming to optimize QoE jointly. Chorus establishes two-way feedback control loops between the server and the client. Furthermore, Chorus introduces Coarse-grained Decisions, which assist appropriate bitrate selection by considering the scheduling decision in throughput prediction, and Finegrained Corrections, which meet the predicted throughput by QoE-oriented multipath scheduling. Extensive emulation and real-world mobile Internet evaluations show that Chorus outperforms the state-of-the-art MPQUIC scheduler, improving average QoE by 23.5% and 65.7%, respectively.
  1. [INFOCOM' 23] Asynchronous Entanglement Provisioning and Routing for Distributed Quantum Computing, Yaliang
    Abstract: In Distributed Quantum Computing (DQC), quantum bits (qubits) used in a quantum circuit may be distributed on multiple Quantum Computers (QCs) connected by a Quantum Data Network (QDN). To perform a quantum gate operation involving two qubits on different QCs, we have to establish an Entanglement Connection (EC) between their host QCs. Existing EC establishment schemes result in a long EC establishment time, and low quantum resource utilization.In this paper, we propose an Asynchronous Entanglement Routing and Provisioning (AEPR) scheme to minimize the task completion time in DQC systems. AEPR has three distinct features: (i). Entanglement Paths (EPs) for a given SD pair are predetermined to eliminate the need for runtime calculation; (ii). Entanglement Links (ELs) are created proactively to reduce the time needed create EL on demand; and (iii). For a given EC request, quantum swapping along an EP is performed by a repeater whenever two adjacent ELs are created, so precious quantum resources at the repeater can be released immediately thereafter for other ELs and ECs. Extensive simulations show that AEPR can save up to 76.05% of the average task completion time in DQC systems compared with the state-of-the-art entanglement routing schemes designed to maximize QDN throughput.

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

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