Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|date=2023-11-30
|time='''Thursday 16:20-18:00'''
|time='''Thursday 16:20-18:00'''
|addr=4th Research Building A518
|addr=4th Research Building A518
Line 12: Line 13:
|title=F3VeTrac: Enabling Fine-grained, Fully-road-covered, and Fully-individual penetrative Vehicle Trajectory Recovery
|title=F3VeTrac: Enabling Fine-grained, Fully-road-covered, and Fully-individual penetrative Vehicle Trajectory Recovery
|speaker=Zhenguo
|speaker=Zhenguo
|date=2023-11-30}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract=In cloud gaming, interactive latency is one of the most important factors in users' experience. Although the interactive latency can be reduced through typical network infrastructures like edge caching and congestion control, the interactive latency of current cloud-gaming platforms is still far from users' satisfaction. This paper presents ZGaming, a novel 3D cloud gaming system based on image prediction, in order to eliminate the interactive latency in traditional cloud gaming systems. To improve the quality of the predicted images, we propose (1) a quality-driven 3D-block cache to reduce the "hole" artifacts, (2) a server-assisted LSTM-predicting algorithm to improve the prediction accuracy of dynamic foreground objects, and (3) a prediction-performance-driven adaptive bitrate strategy which optimizes the quality of predicted images. The experiment on the real-world cloud gaming network conditions shows that compared with existing methods, ZGaming reduces the interactive latency from 23 ms to 0 ms when providing the same video quality, or improves the video quality by 5.4 dB when keeping the interactive latency as 0 ms.
|abstract=In cloud gaming, interactive latency is one of the most important factors in users' experience. Although the interactive latency can be reduced through typical network infrastructures like edge caching and congestion control, the interactive latency of current cloud-gaming platforms is still far from users' satisfaction. This paper presents ZGaming, a novel 3D cloud gaming system based on image prediction, in order to eliminate the interactive latency in traditional cloud gaming systems. To improve the quality of the predicted images, we propose (1) a quality-driven 3D-block cache to reduce the "hole" artifacts, (2) a server-assisted LSTM-predicting algorithm to improve the prediction accuracy of dynamic foreground objects, and (3) a prediction-performance-driven adaptive bitrate strategy which optimizes the quality of predicted images. The experiment on the real-world cloud gaming network conditions shows that compared with existing methods, ZGaming reduces the interactive latency from 23 ms to 0 ms when providing the same video quality, or improves the video quality by 5.4 dB when keeping the interactive latency as 0 ms.
Line 19: Line 20:
|title=ZGaming: Zero-Latency 3D Cloud Gaming by Image Prediction
|title=ZGaming: Zero-Latency 3D Cloud Gaming by Image Prediction
|speaker=Wenjie
|speaker=Wenjie
|date=2023-11-30}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract=Given the central role mobile core plays in supporting mobile network operations, the efficiency, cost-effective dynamic scalability and resilience of the core control plane are paramount. Achieving these goals, however, presents two main challenges: (i) decoupling core network state from processing; (ii) decoupling control plane processing in the core from its interface to the radio access network (RAN). To overcome them, we present CoreKube, a novel message focused and cloud-native mobile core system design, which features truly stateless workers (processing units) that interface with a common database (to hold the core network state) and with the RAN through a frontend. The fully stateless and generic nature of the workers to process any control plane message enables efficient message handling. Orchestration of containerized CoreKube components using Kubernetes, allows leveraging the latter's autoscaling and self-healing properties. We develop 4G and 5G standard-compliant CoreKube implementations, exploiting the agile development methodology enabled by CoreKube's message focused design. Results from our extensive experimental evaluations over the Powder platform relative to prior art show that CoreKube efficiently processes control plane messages, scales dynamically while using minimal compute resources and recovers seamlessly from failures.
|abstract=Given the central role mobile core plays in supporting mobile network operations, the efficiency, cost-effective dynamic scalability and resilience of the core control plane are paramount. Achieving these goals, however, presents two main challenges: (i) decoupling core network state from processing; (ii) decoupling control plane processing in the core from its interface to the radio access network (RAN). To overcome them, we present CoreKube, a novel message focused and cloud-native mobile core system design, which features truly stateless workers (processing units) that interface with a common database (to hold the core network state) and with the RAN through a frontend. The fully stateless and generic nature of the workers to process any control plane message enables efficient message handling. Orchestration of containerized CoreKube components using Kubernetes, allows leveraging the latter's autoscaling and self-healing properties. We develop 4G and 5G standard-compliant CoreKube implementations, exploiting the agile development methodology enabled by CoreKube's message focused design. Results from our extensive experimental evaluations over the Powder platform relative to prior art show that CoreKube efficiently processes control plane messages, scales dynamically while using minimal compute resources and recovers seamlessly from failures.
Line 26: Line 27:
|title=CoreKube: An Efficient, Autoscaling and Resilient Mobile Core System
|title=CoreKube: An Efficient, Autoscaling and Resilient Mobile Core System
|speaker=Qinyong
|speaker=Qinyong
|date=2023-11-30}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract=Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs). This problem is challenging as the targets usually move randomly but the coverage range of sensors is limited in angle and distance. Thus, it is required to coordinate sensors to get ideal target coverage with low power consumption, e.g. no missing targets or reducing redundant coverage. To realize this, we propose a Hierarchical Target-oriented Multi-Agent Coordination (HiT-MAC), which decomposes the target coverage problem into two-level tasks: targets assignment by a coordinator and tracking assigned targets by executors. Specifically, the coordinator periodically monitors the environment globally and allocates targets to each executor. In turn, the executor only needs to track its assigned targets. To effectively learn the HiT-MAC by reinforcement learning, we further introduce a bunch of practical methods, including a self-attention module, marginal contribution approximation for the coordinator, goal-conditional observation filter for the executor, etc. Empirical results demonstrate the advantage of HiT-MAC in coverage rate, learning efficiency, and scalability, comparing to baselines. We also conduct an ablative analysis on the effectiveness of the introduced components in the framework.
|abstract=Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs). This problem is challenging as the targets usually move randomly but the coverage range of sensors is limited in angle and distance. Thus, it is required to coordinate sensors to get ideal target coverage with low power consumption, e.g. no missing targets or reducing redundant coverage. To realize this, we propose a Hierarchical Target-oriented Multi-Agent Coordination (HiT-MAC), which decomposes the target coverage problem into two-level tasks: targets assignment by a coordinator and tracking assigned targets by executors. Specifically, the coordinator periodically monitors the environment globally and allocates targets to each executor. In turn, the executor only needs to track its assigned targets. To effectively learn the HiT-MAC by reinforcement learning, we further introduce a bunch of practical methods, including a self-attention module, marginal contribution approximation for the coordinator, goal-conditional observation filter for the executor, etc. Empirical results demonstrate the advantage of HiT-MAC in coverage rate, learning efficiency, and scalability, comparing to baselines. We also conduct an ablative analysis on the effectiveness of the introduced components in the framework.
Line 33: Line 34:
|title=Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
|title=Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
|speaker=Jiahui
|speaker=Jiahui
|date=2023-11-30}}
}}


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

Revision as of 20:21, 11 December 2023

Time: Thursday 16:20-18:00
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [TMC '23] F3VeTrac: Enabling Fine-grained, Fully-road-covered, and Fully-individual penetrative Vehicle Trajectory Recovery, Zhenguo
    Abstract: Obtaining urban-scale vehicle trajectories is essential to understand the urban mobility and benefits various downstream applications. The mobility knowledge obtained from existing vehicle trajectory sensing techniques is typically incomplete. To fill the gap, we propose F3VeTrac , an efficient deep-learning-based vehicle trajectory recovery system that utilizes complementary characteristics of the Camera Surveillance System and the Vehicle Tracking System to obtain fine-grained, fully-road-covered, and fully-individual-penetrative ( F3 ) trajectories. F3VeTrac utilizes five well-designed modules to model the co-occurrence relationships hidden in both coarse-grained and fine-grained trajectories from the two complementary sensing systems and fuse them to recover the coarse-grained trajectories. We implement and evaluate F3VeTrac with two real-world datasets from over 100 million regular vehicle trajectories and 16 million commercial vehicle trajectories in two cities of China, together with an on-field case study based on 251 regular vehicle trajectories collected by 17 volunteers, demonstrating its great advantages over six state-of-the-art alternative schemes. Source codes are available in https://github.com/UrbanComp-BUPT/F3VeTrac . Moreover, we present a downstream application of F3VeTrac for traffic condition estimation, which obtains obvious performance gains.
  2. [SIGCOMM '23] ZGaming: Zero-Latency 3D Cloud Gaming by Image Prediction, Wenjie
    Abstract: In cloud gaming, interactive latency is one of the most important factors in users' experience. Although the interactive latency can be reduced through typical network infrastructures like edge caching and congestion control, the interactive latency of current cloud-gaming platforms is still far from users' satisfaction. This paper presents ZGaming, a novel 3D cloud gaming system based on image prediction, in order to eliminate the interactive latency in traditional cloud gaming systems. To improve the quality of the predicted images, we propose (1) a quality-driven 3D-block cache to reduce the "hole" artifacts, (2) a server-assisted LSTM-predicting algorithm to improve the prediction accuracy of dynamic foreground objects, and (3) a prediction-performance-driven adaptive bitrate strategy which optimizes the quality of predicted images. The experiment on the real-world cloud gaming network conditions shows that compared with existing methods, ZGaming reduces the interactive latency from 23 ms to 0 ms when providing the same video quality, or improves the video quality by 5.4 dB when keeping the interactive latency as 0 ms.
  3. [MobiCom '23] CoreKube: An Efficient, Autoscaling and Resilient Mobile Core System, Qinyong
    Abstract: Given the central role mobile core plays in supporting mobile network operations, the efficiency, cost-effective dynamic scalability and resilience of the core control plane are paramount. Achieving these goals, however, presents two main challenges: (i) decoupling core network state from processing; (ii) decoupling control plane processing in the core from its interface to the radio access network (RAN). To overcome them, we present CoreKube, a novel message focused and cloud-native mobile core system design, which features truly stateless workers (processing units) that interface with a common database (to hold the core network state) and with the RAN through a frontend. The fully stateless and generic nature of the workers to process any control plane message enables efficient message handling. Orchestration of containerized CoreKube components using Kubernetes, allows leveraging the latter's autoscaling and self-healing properties. We develop 4G and 5G standard-compliant CoreKube implementations, exploiting the agile development methodology enabled by CoreKube's message focused design. Results from our extensive experimental evaluations over the Powder platform relative to prior art show that CoreKube efficiently processes control plane messages, scales dynamically while using minimal compute resources and recovers seamlessly from failures.
  4. [NeurIPS '20] Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks, Jiahui
    Abstract: Maximum target coverage by adjusting the orientation of distributed sensors is an important problem in directional sensor networks (DSNs). This problem is challenging as the targets usually move randomly but the coverage range of sensors is limited in angle and distance. Thus, it is required to coordinate sensors to get ideal target coverage with low power consumption, e.g. no missing targets or reducing redundant coverage. To realize this, we propose a Hierarchical Target-oriented Multi-Agent Coordination (HiT-MAC), which decomposes the target coverage problem into two-level tasks: targets assignment by a coordinator and tracking assigned targets by executors. Specifically, the coordinator periodically monitors the environment globally and allocates targets to each executor. In turn, the executor only needs to track its assigned targets. To effectively learn the HiT-MAC by reinforcement learning, we further introduce a bunch of practical methods, including a self-attention module, marginal contribution approximation for the coordinator, goal-conditional observation filter for the executor, etc. Empirical results demonstrate the advantage of HiT-MAC in coverage rate, learning efficiency, and scalability, comparing to baselines. We also conduct an ablative analysis on the effectiveness of the introduced components in the framework.

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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