Resource: Seminar

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Time: 2024-03-22 10:30-12:00
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [INFOCOM 2023] Achieving Resilient and Performance-Guaranteed Routing in Space-Terrestrial Integrated Networks, Luwei
    Abstract: Satellite routers in emerging space-terrestrial integrated networks (STINs) are operated in a failure-prone, intermittent and resource-constrained space environment, making it very critical but challenging to cope with various network failures effectively. Existing resilient routing approaches either suffer from continuous re-convergences with low network reachability, or involve prohibitive pre-computation and storage overhead due to the huge amount of possible failure scenarios in STINs.This paper presents StarCure, a novel resilient routing mechanism for futuristic STINs. StarCure aims at achieving fast and efficient routing restoration, while maintaining the low-latency, high-bandwidth service capabilities in failure-prone space environments. First, StarCure incorporates a new network model, called the topology-stabilizing model (TSM) to eliminate topological uncertainty by converting the topology variations caused by various failures to traffic variations. Second, StarCure adopts an adaptive hybrid routing scheme, collaboratively combining a constraint optimizer to efficiently handle predictable failures, together with a location-guided protection routing strategy to quickly deal with unexpected failures. Extensive evaluations driven by realistic constellation information show that, StarCure can protect routing against various failures, achieving close-to-100% reachability and better performance restoration with acceptable system overhead, as compared to other existing resilience solutions.
  2. [INFOCOM 2023] Communication-aware DNN pruning, Shuhong
    Abstract: We propose a Communication-aware Pruning (CaP) algorithm, a novel distributed inference framework for distributing DNN computations across a physical network. Departing from conventional pruning methods, CaP takes the physical network topology into consideration and produces DNNs that are communication-aware, designed for both accurate and fast execution over such a distributed deployment. Our experiments on CIFAR-10 and CIFAR-100, two deep learning benchmark datasets, show that CaP beats state of the art competitors by up to 4% w.r.t. accuracy on benchmarks. On experiments over real-world scenarios, it simultaneously reduces total execution time by 27%–68% at negligible performance decrease (less than 1%).

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

Template loop detected: Resource:Previous Seminars

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