Difference between revisions of "Resource:Seminar"

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===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=Versatile Internet of Things (IoT) applications call for re-configurable IoT devices that can easily extend new functionality on demand. However, the heterogeneity of functional chips brings difficulties in device customization, leading to inadequate flexibility. In this paper, we propose LEGO, a novel architecture for chip-level re-configurable IoT devices that supports plug-and-play with Commercial Off-The-Shelf (COTS) chips. To combat the heterogeneity of functional chips, we first design a novel Unified Chip Description Language (UCDL) with meta-operation and chip specifications to access various types of functional chips uniformly. Then, to achieve chips plug-and-play, we build up a novel platform and shift all chip control logic to the gateway, which makes IoT devices entirely decoupled from specific applications and does not need to make any changes when plugging in new functional chips. Finally, to handle communications overheads, we built up a novel orchestration architecture for gateway instructions, which minimizes instruction transmission frequency in remote chip control. We implement the prototype and conduct extensive evaluations with 100+ types of COTS functional chips. The results show that new functional chips can be automatically accessed by the system within 0.13 seconds after being plugged in, and only bringing 0.53 kb of communication load on average, demonstrating the efficacy of LEGO design.
|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.
|confname=ASPLOS '23
|confname=TMC '20
|link=https://dl.acm.org/doi/10.1145/3582016.3582050
|link=https://ieeexplore.ieee.org/document/8708935
|title=LEGO: Empowering Chip-Level Functionality Plug-and-Play for Next-Generation IoT Devices
|title=SmartVLC: Co-Designing Smart Lighting and Communication for Visible Light Networks
|speaker=Pengfei
|speaker=Mengyu
|date=2023-11-09}}
|date=2023-11-16}}
{{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 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.
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|title=A Fast, Reliable, Opportunistic Broadcast Scheme With Mitigation of Internal Interference in VANETs
|title=A Fast, Reliable, Opportunistic Broadcast Scheme With Mitigation of Internal Interference in VANETs
|speaker=Luwei
|speaker=Luwei
|date=2023-11-09}}
|date=2023-11-16}}
{{Latest_seminar
{{Latest_seminar
|abstract=With the explosive increment of computation requirements, the multiaccess edge computing (MEC) paradigm appears as an effective mechanism. Besides, as for the Internet of Things (IoT) in disasters or remote areas requiring MEC services, unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) are available to provide aerial computing services for these IoT devices. In this article, we develop the hierarchical aerial computing framework composed of HAPs and UAVs, to provide MEC services for various IoT applications. In particular, the problem is formulated to maximize the total IoT data computed by the aerial MEC platforms, restricted by the delay requirement of IoT and multiple resource constraints of UAVs and HAPs, which is an integer programming problem and intractable to solve. Due to the prohibitive complexity of the exhaustive search, we handle the problem by presenting the matching game theory-based algorithm to deal with the offloading decisions from IoT devices to UAVs, as well as a heuristic algorithm for the offloading decisions between UAVs and HAPs. The external effect affected by the interplay of different IoT devices in the matching is tackled by the externality elimination mechanism. Besides, an adjustment algorithm is also proposed to make the best of aerial resources. The complexity of proposed algorithms is analyzed and extensive simulation results verify the efficiency of the proposed algorithms, and the system performances are also analyzed by the numerical results.
|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.
|confname=IoTJ '23
|confname=INFOCOM '23
|link=https://ieeexplore.ieee.org/document/9714482?denied=
|link=https://ieeexplore.ieee.org/document/10228990
|title=Hierarchical Aerial Computing for Internet of Things via Cooperation of HAPs and UAVs
|title=ResMap: Exploiting Sparse Residual Feature Map for Accelerating Cross-Edge Video Analytics
|speaker=Kun Wang
|speaker=Xianyang
|date=2023-11-09}}
|date=2023-11-16}}
{{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=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.
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|title=Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computing
|title=Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computing
|speaker=Mengfan
|speaker=Mengfan
|date=2023-11-09}}
|date=2023-11-16}}
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 20:19, 13 November 2023

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

Latest

  1. [TMC '20] SmartVLC: Co-Designing Smart Lighting and Communication for Visible Light Networks, Mengyu
    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.
  2. [TMC '23] A Fast, Reliable, Opportunistic Broadcast Scheme With Mitigation of Internal Interference in VANETs, Luwei
    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.
  3. [INFOCOM '23] ResMap: Exploiting Sparse Residual Feature Map for Accelerating Cross-Edge Video Analytics, Xianyang
    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.
  4. [NSDI '23] Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computing, Mengfan
    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.

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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