Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2022-5-23 10:30'''
|time='''2025-09-25 10:30'''
|addr=4th Research Building A527-B
|addr=4th Research Building A518
|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]].
}}
}}


===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract = As intelligence is moving from data centers to the edges, intelligent edge devices such as smartphones, drones, robots, and smart IoT devices are equipped with the capability to altogether train a deep learning model on the devices from the data collected by themselves. Despite its considerable value, the key bottleneck of making on-device distributed training practically useful in realworld deployments is that they consume a significant amount of training time under wireless networks with constrained bandwidth. To tackle this critical bottleneck, we present Mercury, an importance sampling-based framework that enhances the training efficiency of on-device distributed training without compromising the accuracies of the trained models. The key idea behind the design of Mercury is to focus on samples that provide more important information in each training iteration. In doing this, the training efficiency of each iteration is improved. As such, the total number of iterations can be considerably reduced so as to speed up the overall training process. We implemented Mercury and deployed it on a self-developed testbed. We demonstrate its effectiveness and show that Mercury consistently outperforms two status quo frameworks on six commonly used datasets across tasks in image classification, speech recognition, and natural language processing.  
|abstract = Distributed Edge Computing (DEC) has emerged as a novel paradigm, owing to its superior performance in communication latency, parallel computing efficiency, and energy consumption. With the surge of tasks in generative artificial intelligence, DEC faces higher demands for parallel computing efficiency. Scheduling multiple tasks for simultaneous processing, rather than one-by-one handling, could enhance parallel efficiency. Multiple tasks have multi-dependencies, i.e., sequence dependency, attribute similarity, and attribute correlation. Utilizing the bidirectional edges of traditional graphs to represent multi-dependencies can lead to an explosion in quantity. A hypergraph, with its hyperedges capable of connecting any number of vertices, can significantly solve the above problem. However, the multi-dependencies are rarely studied in the current research, posing the challenges, including incapable representing and unable capturing of multi-dependency hypergraph. In this work, we introduce a Joint communication and computation scheduling for hypErgraph Tasks in DEC, namely HypeJet, To effectively represent multi-dependencies, we employ hypergraph construction to represent task attributes and utilize hypergraph partitioning to clarify and refine task attribute correlations, enhancing parallel efficiency. In response to the challenge of capturing multi-dependencies, we employ a scheduling mechanism with the hypergraph neural network that efficiently acquires higher-order attribute correlated information among convolution matrices, providing enriched contextual information on multi-dependencies that supports decision-making in scheduling tasks. The evaluations using real-world traces demonstrate an 18.07% improvement in parallel efficiency of task scheduling.
|confname= SenSys 2021
|confname =INFOCOM'25
|link=https://www.egr.msu.edu/~mizhang/papers/2021_SenSys_Mercury.pdf
|link = https://ieeexplore.ieee.org/abstract/document/11044587
|title=Mercury: Efficient On-Device Distributed DNN Training via Stochastic Importance Sampling
|title= HyperJet: Joint Communication and Computation Scheduling for Hypergraph Tasks in Distributed Edge Computing
|speaker=Jiajun
|speaker= Yi Zhou
|date=2025-9-26
}}{{Latest_seminar
|abstract = Localization of networked nodes is an essential problem in emerging applications, including first-responder navigation, automated manufacturing lines, vehicular and drone navigation, asset tracking, Internet of Things, and 5G communication networks. In this paper, we present Locate3D, a novel system for peer-to-peer node localization and orientation estimation in large networks. Unlike traditional range-only methods, Locate3D introduces angle-of-arrival (AoA) data as an added network topology constraint. The system solves three key challenges: it uses angles to reduce the number of measurements required by 4× and jointly uses range and angle data for location estimation. We develop a spanning-tree approach for fast location updates, and to ensure the output graphs are rigid and uniquely realizable, even in occluded or weakly connected areas. Locate3D cuts down latency by up to 75% without compromising accuracy, surpassing standard range-only solutions. It has a 0.86 meter median localization error for building-scale multi-floor networks (32 nodes, 0 anchors) and 12.09 meters for large-scale networks (100,000 nodes, 15 anchors).
|confname =NSDI'25
|link = https://www.usenix.org/conference/nsdi25/presentation/garg
|title= Large Network UWB Localization: Algorithms and Implementation
|speaker=Bangguo
|date=2025-9-26
}}
}}
{{Latest_seminar
|abstract = Many datacenters and clouds manage storage systems separately from computing services for better manageability and resource utilization. These existing disaggregated storage systems use hard disks or SSDs as storage media. Recently, the technology of persistent memory (PM) has matured and seen initial adoption in several datacenters. Disaggregating PM could enjoy the same benefits of traditional disaggregated storage systems, but it requires new designs because of its memory-like performance and byte addressability. In this paper, we explore the design of disaggregating PM and managing them remotely from compute servers, a model we call passive disaggregated persistent memory, or pDPM. Compared to the alternative of managing PM at storage servers, pDPM significantly lowers monetary and energy costs and avoids scalability bottlenecks at storage servers. We built three key-value store systems using the pDPM model. The first one lets all compute nodes directly access and manage storage nodes. The second uses a central coordinator to orchestrate the communication between compute and storage nodes. These two systems have various performance and scalability limitations. To solve these problems, we built Clover, a pDPM system that separates the location, communication mechanism, and management strategy of the data plane and the metadata/control plane. Compute nodes access storage nodes directly for data operations, while one or few global metadata servers handle all metadata/control operations. From our extensive evaluation of the three pDPM systems, we found Clover to be the best-performing pDPM system. Its performance under common datacenter workloads is similar to non-pDPM remote in-memory key-value store, while reducing CapEx and OpEx by 1.4× and 3.9×.
|confname= ATC 2020
|link=https://www.usenix.org/system/files/atc20-tsai.pdf
|title=Disaggregating Persistent Memory and Controlling Them Remotely: An Exploration of Passive Disaggregated Key-Value Stores
|speaker=Silence
}}
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 21:23, 25 September 2025

Time: 2025-09-25 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [INFOCOM'25] HyperJet: Joint Communication and Computation Scheduling for Hypergraph Tasks in Distributed Edge Computing, Yi Zhou
    Abstract: Distributed Edge Computing (DEC) has emerged as a novel paradigm, owing to its superior performance in communication latency, parallel computing efficiency, and energy consumption. With the surge of tasks in generative artificial intelligence, DEC faces higher demands for parallel computing efficiency. Scheduling multiple tasks for simultaneous processing, rather than one-by-one handling, could enhance parallel efficiency. Multiple tasks have multi-dependencies, i.e., sequence dependency, attribute similarity, and attribute correlation. Utilizing the bidirectional edges of traditional graphs to represent multi-dependencies can lead to an explosion in quantity. A hypergraph, with its hyperedges capable of connecting any number of vertices, can significantly solve the above problem. However, the multi-dependencies are rarely studied in the current research, posing the challenges, including incapable representing and unable capturing of multi-dependency hypergraph. In this work, we introduce a Joint communication and computation scheduling for hypErgraph Tasks in DEC, namely HypeJet, To effectively represent multi-dependencies, we employ hypergraph construction to represent task attributes and utilize hypergraph partitioning to clarify and refine task attribute correlations, enhancing parallel efficiency. In response to the challenge of capturing multi-dependencies, we employ a scheduling mechanism with the hypergraph neural network that efficiently acquires higher-order attribute correlated information among convolution matrices, providing enriched contextual information on multi-dependencies that supports decision-making in scheduling tasks. The evaluations using real-world traces demonstrate an 18.07% improvement in parallel efficiency of task scheduling.
  2. [NSDI'25] Large Network UWB Localization: Algorithms and Implementation, Bangguo
    Abstract: Localization of networked nodes is an essential problem in emerging applications, including first-responder navigation, automated manufacturing lines, vehicular and drone navigation, asset tracking, Internet of Things, and 5G communication networks. In this paper, we present Locate3D, a novel system for peer-to-peer node localization and orientation estimation in large networks. Unlike traditional range-only methods, Locate3D introduces angle-of-arrival (AoA) data as an added network topology constraint. The system solves three key challenges: it uses angles to reduce the number of measurements required by 4× and jointly uses range and angle data for location estimation. We develop a spanning-tree approach for fast location updates, and to ensure the output graphs are rigid and uniquely realizable, even in occluded or weakly connected areas. Locate3D cuts down latency by up to 75% without compromising accuracy, surpassing standard range-only solutions. It has a 0.86 meter median localization error for building-scale multi-floor networks (32 nodes, 0 anchors) and 12.09 meters for large-scale networks (100,000 nodes, 15 anchors).

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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