Resource: Seminar

From MobiNetS
Revision as of 16:53, 6 April 2022 by Wenliang (talk | contribs) (wenliang updates seminars)
Jump to: navigation, search

Time: 2022-4-8 10:20
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [INFOCOM 2022] Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach, Wenjie
    Abstract: Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short to handle task placement involving many coexisting users in a dynamic and uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system side information to enable a decentralized user-initiated service placement. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We show that DEBO can deduce the optimal user-server assignment, thereby achieving a close-to-optimal service performance and tight O(log T) offloading regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users’ offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards.
  2. [INFOCOM 2022] CASVA: Configuration-Adaptive Streaming for Live Video Analytics, Shiqi
    Abstract: The advent of high-accuracy and resource-intensive deep neural networks (DNNs) has fulled the development of live video analytics, where camera videos need to be streamed over the network to edge or cloud servers with sufficient computational resources. Although it is promising to strike a balance between available bandwidth and server-side DNN inference accuracy by adjusting video encoding configurations, the influences of f ine-grained network and video content dynamics on configuration performance should be addressed. In this paper, we propose CASVA, a Configuration-Adaptive Streaming framework designed for live Video Analytics. The design of CASVA is motivated by our extensive measurements on how video configuration affects its bandwidth requirement and inference accuracy. To handle the complicated dynamics in live video analytics streaming, CASVA trains a deep reinforcement learning model which does not make any assumptions about the environment but learns to make configuration choices through its experiences. A variety of real-world network traces are used to drive the evaluation of CASVA. The results on a multitude of video types and video analytics tasks show the advantages of CASVA over state-of-the-art solutions.

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

Instructions

请使用Latest_seminar和Hist_seminar模板更新本页信息.

    • 修改时间和地点信息
    • 将当前latest seminar部分的code复制到这个页面中
    • 将{{Latest_seminar... 修改为 {{Hist_seminar...，并增加对应的日期信息|date=
    • 填入latest seminar各字段信息
    • link请务必不要留空，如果没有link则填本页地址 https://mobinets.org/index.php?title=Resource:Seminar
  • 格式说明
    • Latest_seminar:

{{Latest_seminar
|confname=
|link=
|title=
|speaker=
}}

    • Hist_seminar

{{Hist_seminar
|confname=
|link=
|title=
|speaker=
|date=
}}

Retrieved from "http://mobinets.cn/site/index.php?title=Resource:Seminar&oldid=2362"