Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-10-08 8:40
|time=2021-10-15 8:40
|addr=Main Building B1-612
|addr=Main Building B1-612
|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]].
Line 7: Line 7:
===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=Task-based distributed frameworks (e.g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving. As more data-intensive applications move to run on top of task-based systems, collective communication efficiency has become an important problem. Unfortunately, traditional collective communication libraries (e.g., MPI, Horovod, NCCL) are an ill fit, because they require the communication schedule to be known before runtime and they do not provide fault tolerance. We design and implement Hoplite, an efficient and fault-tolerant collective communication layer for task-based distributed systems. Our key technique is to compute data transfer schedules on the fly and execute the schedules efficiently through fine-grained pipelining. At the same time, when a task fails, the data transfer schedule adapts quickly to allow other tasks to keep making progress. We apply Hoplite to a popular task-based distributed framework, Ray. We show that Hoplite speeds up asynchronous stochastic gradient descent, reinforcement learning, and serving an ensemble of machine learning models that are difficult to execute efficiently with traditional collective communication by up to 7.8x, 3.9x, and 3.3x, respectively. Video: https://www.youtube.com/watch?v=pHLIrkNj4w0
|abstract=Low Power Wide Area Networks (LPWAN) such as Long Range (LoRa) show great potential in emerging aquatic IoT applications. However, our deployment experience shows that the floating LPWAN suffer significant performance degradation, compared to the static terrestrial deployments. Our measurement results reveal the reason behind this is due to the polarization and directivity of the antenna. The dynamic attitude of a floating node incurs varying signal strength losses, which is ignored by the attitude-oblivious link model adopted in most of the existing methods. When accessing the channel at a misaligned attitude, packet errors can happen. In this paper, we propose an attitude-aware link model that explicitly quantifies the impact of node attitude on link quality. Based on the new model, we propose PolarTracker, a novel channel access method for floating LPWAN. PolarTracker tracks the node attitude alignment state and schedules the transmissions into the aligned periods with better link quality. We implement a prototype of PolarTracker on commercial LoRa platforms and extensively evaluate its performance in various real-world environments. The experimental results show that PolarTracker can efficiently improve the packet reception ratio by 48.8%, compared with ALOHA in LoRaWAN.
|confname=SIGCOMM 2021
|confname=INFOCOM 2021
|link=https://dl.acm.org/doi/pdf/10.1145/3452296.3472897
|link=https://ieeexplore.ieee.org/document/9488714
|title=Hoplite: efficient and fault-tolerant collective communication for task-based distributed systems
|title=PolarTracker: Attitude-aware Channel Access for Floating Low Power Wide Area Networks
|speaker=Xianyang
|speaker=Wenliang
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract=This paper re-evaluates the performance of the EPaxos consensus protocol for geo-replication and proposes an enhancement that uses synchronized clocks to reduce operation latency. The benchmarking approach used for the original EPaxos evaluation does not trigger or measure the full impact of conflict behavior on system performance. Our re-evaluation confirms the original claim that EPaxos provides optimal median commit latency in a WAN, but it shows much worse tail latency than previously reported (more than 4x worse than Multi-Paxos). Furthermore, performance is highly sensitive to application workloads, particularly at the tail. In addition, we show how synchronized clocks can be used to reduce conflicts in geo-replication. By imposing intentional delays on message processing, we can achieve roughly in-order deliveries to multiple replicas. When applied to EPaxos, this technique reduced conflicts by at least 50% without introducing additional overhead, decreasing mean latency by up to 7.5%. Video: https://www.usenix.org/conference/nsdi21/presentation/tollman
|abstract=Due to the limited computing capacity in mobile devices, device-to-device (D2D) computation offloading has been proposed as a promising solution to improving the quality of service in the Internet of things (IoT) networks, by allowing mobile devices to exploit spare computing resources in nearby user devices. However, a major challenge to realizing this new paradigm is how to effectively motivate user devices to participate as computation providers (CPs) for computation requesters (CRs), which is further exacerbated by the fact that user incentives are usually coupled with information asymmetry between the network operator and user devices. This has not been sufficiently studied for D2D computation offloading. In this paper, we propose a signaling-based incentive mechanism that leverages contract theory to address information asymmetry for D2D computation offloading. Based on the proposed contract-based incentive mechanism, we also solve the many-to-many CP-CR pairing problem by devising a polynomial-complexity matching scheme. Simulation results show that our proposed algorithm can effectively motivate user devices to participate in D2D computation offloading and select the most appropriate CPs to perform the computation tasks for corresponding CRs.
|confname=NSDI 2021
|confname=IoTJ 2021
|link=https://www.usenix.org/system/files/nsdi21-tollman.pdf
|link=https://ieeexplore.ieee.org/abstract/document/9523573
|title=EPaxos Revisited
|title=Signaling-based Incentive Mechanism for D2D Computation Offloading
|speaker=Jianfei
|speaker=Wenjie
}}
}}


=== History ===
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 14:39, 13 October 2021

Time: 2021-10-15 8:40
Address: Main Building B1-612
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [INFOCOM 2021] PolarTracker: Attitude-aware Channel Access for Floating Low Power Wide Area Networks, Wenliang
    Abstract: Low Power Wide Area Networks (LPWAN) such as Long Range (LoRa) show great potential in emerging aquatic IoT applications. However, our deployment experience shows that the floating LPWAN suffer significant performance degradation, compared to the static terrestrial deployments. Our measurement results reveal the reason behind this is due to the polarization and directivity of the antenna. The dynamic attitude of a floating node incurs varying signal strength losses, which is ignored by the attitude-oblivious link model adopted in most of the existing methods. When accessing the channel at a misaligned attitude, packet errors can happen. In this paper, we propose an attitude-aware link model that explicitly quantifies the impact of node attitude on link quality. Based on the new model, we propose PolarTracker, a novel channel access method for floating LPWAN. PolarTracker tracks the node attitude alignment state and schedules the transmissions into the aligned periods with better link quality. We implement a prototype of PolarTracker on commercial LoRa platforms and extensively evaluate its performance in various real-world environments. The experimental results show that PolarTracker can efficiently improve the packet reception ratio by 48.8%, compared with ALOHA in LoRaWAN.
  2. [IoTJ 2021] Signaling-based Incentive Mechanism for D2D Computation Offloading, Wenjie
    Abstract: Due to the limited computing capacity in mobile devices, device-to-device (D2D) computation offloading has been proposed as a promising solution to improving the quality of service in the Internet of things (IoT) networks, by allowing mobile devices to exploit spare computing resources in nearby user devices. However, a major challenge to realizing this new paradigm is how to effectively motivate user devices to participate as computation providers (CPs) for computation requesters (CRs), which is further exacerbated by the fact that user incentives are usually coupled with information asymmetry between the network operator and user devices. This has not been sufficiently studied for D2D computation offloading. In this paper, we propose a signaling-based incentive mechanism that leverages contract theory to address information asymmetry for D2D computation offloading. Based on the proposed contract-based incentive mechanism, we also solve the many-to-many CP-CR pairing problem by devising a polynomial-complexity matching scheme. Simulation results show that our proposed algorithm can effectively motivate user devices to participate in D2D computation offloading and select the most appropriate CPs to perform the computation tasks for corresponding CRs.

History

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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