Difference between revisions of "Resource:Seminar"

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===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=Visible Light Communication (VLC) based on LEDs has been a hot topic investigated for over a decade. However, most of the research efforts assume the intensity of LED light is constant. This hypothesis is not true when Smart Lighting is introduced to VLC, which requires LEDs to adapt their brightness based on the intensity of natural ambient light. Smart lighting saves power consumption and improves user comfort. However, intensity adaptation severely affects the throughput performance of data communication. In this paper, we propose SmartVLC, a system that can maximize the throughput (benefit communication) while still maintaining the LEDs' illumination function (benefit smart lighting). A novel Adaptive Multiple Pulse Position Modulation (AMPPM) scheme is proposed to support fine-grained dimming levels to avoid flickering while maximizing the throughput under each dimming level. SmartVLC is implemented on off-the-shelf commodity hardware. Several real-life challenges in both hardware and software are addressed to make it a robust real-time system. Comprehensive experiments are carried out to evaluate the system performance under multifaceted scenarios. Experimental results demonstrate that SmartVLC supports a communication distance up to 3.6m, and improves the throughput achieved with two state-of-the-art approaches by 40 and 12 percent on average, respectively, without bringing any flickering to users.
|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.
|confname=TMC '20
|confname=TMC '23
|link=https://ieeexplore.ieee.org/document/8708935
|link=https://ieeexplore.ieee.org/abstract/document/10209220
|title=SmartVLC: Co-Designing Smart Lighting and Communication for Visible Light Networks
|title=F3VeTrac: Enabling Fine-grained, Fully-road-covered, and Fully-individual penetrative Vehicle Trajectory Recovery
|speaker=Mengyu
|speaker=Zhenguo
|date=2023-11-16}}
|date=2023-11-30}}
{{Latest_seminar
{{Latest_seminar
|abstract=In VANETs, it is important to support fast and reliable multi-hop broadcast for safety-related applications. The performance of multi-hop broadcast schemes is greatly affected by relay selection strategies. However, the relationship between the relay selection strategies and the expected broadcast performance has not been fully characterized yet. Furthermore, conventional broadcast schemes usually attempt to minimize the waiting time difference between adjacent relay candidates to reduce the waiting time overhead, which makes the relay selection process vulnerable to internal interference, occurring due to retransmissions from previous forwarders and transmissions from redundant relays. In this paper, we jointly take both of the relay selection and the internal interference mitigation into account and propose a fast, reliable, opportunistic multi-hop broadcast scheme, in which we utilize a novel metric called the expected broadcast speed in relay selection and propose a delayed retransmission mechanism to mitigate the adverse effect of retransmissions from previous forwarders and an expected redundancy probability based mechanism to mitigate the adverse effect of redundant relays. The performance evaluation results show that the proposed scheme yields the best broadcast performance among the four schemes in terms of the broadcast coverage ratio and the end-to-end delivery latency.
|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.
|confname=TMC '23
|confname=SIGCOMM '23
|link=https://ieeexplore.ieee.org/document/9566795
|link=https://dl.acm.org/doi/pdf/10.1145/3603269.3604819
|title=A Fast, Reliable, Opportunistic Broadcast Scheme With Mitigation of Internal Interference in VANETs
|title=ZGaming: Zero-Latency 3D Cloud Gaming by Image Prediction
|speaker=Luwei
|speaker=Wenjie
|date=2023-11-16}}
|date=2023-11-30}}
{{Latest_seminar
{{Latest_seminar
|abstract=Deploying deep convolutional neural network (CNN) to perform video analytics at edge poses a substantial system challenge, as running CNN inference incurs a prohibitive cost in computational resources. Model partitioning, as a promising approach, splits CNNs and distributes them to multiple edge devices in closer proximity to each other for serial inferences, however, it causes considerable cross-edge delay for transmitting intermediate feature maps. To overcome this challenge, we present ResMap, a new edge video analytics framework that significantly improves the cross-edge transmission and flexibly partitions the CNNs. Briefly, by exploiting the sparsity of the intermediate raw or residual feature map, ResMap effectively removes the redundant transmission, thereby decreasing the cross-edge transmission delay. In addition, ResMap incorporates an Online Data-Aware Scheduler to regularly update the CNN partitioning scheme so as to adapt to the time-varying edge runtime and video content. We have implemented ResMap fully based on COTS hardware, and the experimental results show that ResMap reduces the intermediate feature map volume by 14.93-46.12% and improves the average processing time by 17.43-30.6% compared to other alternative designs.
|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.
|confname=INFOCOM '23
|confname=MobiCom '23
|link=https://ieeexplore.ieee.org/document/10228990
|link=https://dl.acm.org/doi/abs/10.1145/3570361.3592522
|title=ResMap: Exploiting Sparse Residual Feature Map for Accelerating Cross-Edge Video Analytics
|title=CoreKube: An Efficient, Autoscaling and Resilient Mobile Core System
|speaker=Xianyang
|speaker=Qinyong
|date=2023-11-16}}
|date=2023-11-30}}
{{Latest_seminar
{{Latest_seminar
|abstract=Serverless applications are typically composed of function workflows in which multiple short-lived functions are triggered to exchange data in response to events or state changes. Current serverless platforms coordinate and trigger functions by following high-level invocation dependencies but are oblivious to the underlying data exchanges between functions. This design is neither efficient nor easy to use in orchestrating complex workflows – developers often have to manage complex function interactions by themselves, with customized implementation and unsatisfactory performance. In this paper, we argue that function orchestration should follow a data-centric approach. In our design, the platform provides a data bucket abstraction to hold the intermediate data generated by functions. Developers can use a rich set of data trigger primitives to control when and how the output of each function should be passed to the next functions in a workflow. By making data consumption explicit and allowing it to trigger functions and drive the workflow, complex function interactions can be easily and efficiently supported. We present Pheromone – a scalable, low-latency serverless platform following this data-centric design. Compared to well-established commercial and open-source platforms, Pheromone cuts the latencies of function interactions and data exchanges by orders of magnitude, scales to large workflows, and enables easy implementation of complex applications.
|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.
|confname=NSDI '23
|confname=NeurIPS '20
|link=https://www.usenix.org/conference/nsdi23/presentation/yu
|link=https://proceedings.neurips.cc/paper/2020/hash/7250eb93b3c18cc9daa29cf58af7a004-Abstract.html
|title=Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computing
|title=Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
|speaker=Mengfan
|speaker=Jiahui
|date=2023-11-16}}
|date=2023-11-30}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 17:02, 27 November 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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