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{{SemNote
{{SemNote
|time='''2023-02-13 9:30'''
|time='''2025-04-11 10:30-12:00'''
|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 = Mobile crowd sensing (MCS) is a popular sensing paradigm that leverages the power of massive mobile workers to perform various location-based sensing tasks. To assign workers with suitable tasks, recent research works investigated mobility prediction methods based on probabilistic and statistical models to estimate the worker’s moving behavior, based on which the allocation algorithm is designed to match workers with tasks such that workers do not need to deviate from their daily routes and tasks can be completed as many as possible. In this paper, we propose a new multi-task allocation method based on mobility prediction, which differs from the existing works by (1) making use of workers’ historical trajectories more comprehensively by using the fuzzy logic system to obtain more accurate mobility prediction and (2) designing a global heuristic searching algorithm to optimize the overall task completion rate based on the mobility prediction result, which jointly considers workers’ and tasks’ spatiotemporal features. We evaluate the proposed prediction method and task allocation algorithm using two real-world datasets. The experimental results validate the effectiveness of the proposed methods compared against baselines.
|abstract = While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i.e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance in resource-constrained environments. We propose a new paradigm, CACTUS, for scalable and efficient context-aware classification where a micro-classifier recognizes a small set of classes relevant to the current context and, when context change happens (e.g., a new class comes into the scene), rapidly switches to another suitable micro-classifier. CACTUS features several innovations, including optimizing the training cost of context-aware classifiers, enabling on-the-fly context-aware switching between classifiers, and balancing context switching costs and performance gains via simple yet effective switching policies. We show that CACTUS achieves significant benefits in accuracy, latency, and compute budget across a range of datasets and IoT platforms.
|confname=Mobicom 2022
|confname = Mobisys'24
|link=https://dl.acm.org/doi/pdf/10.1145/3495243.3560544
|link = https://dl.acm.org/doi/abs/10.1145/3643832.3661888
|title=BSMA: Scalable LoRa networks using full duplex gateways
|title= CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference
|speaker=Kaiwen}}
|speaker= Zhenhua
|date=2025-04-18
}}
{{Latest_seminar
{{Latest_seminar
|abstract = On-device deep neural network (DNN) training holds the potential to enable a rich set of privacy-aware and infrastructure-independent personalized mobile applications. However, despite advancements in mobile hardware, locally training a complex DNN is still a nontrivial task given its resource demands. In this work, we show that the limited memory resources on mobile devices are the main constraint and propose Sage as a framework for efficiently optimizing memory resources for on-device DNN training. Specifically, Sage configures a flexible computation graph for DNN gradient evaluation and reduces the memory footprint of the graph using operator- and graph-level optimizations. In run-time, Sage employs a hybrid of gradient checkpointing and micro-batching techniques to dynamically adjust its memory use to the available system memory budget. Using implementation on off-the-shelf smartphones, we show that Sage enables local training of complex DNN models by reducing memory use by more than 20-fold compared to a baseline approach. We also show that Sage successfully adapts to run-time memory budget variations, and evaluate its energy consumption to show Sage's practical applicability.
|abstract = Nowadays, volumetric videos have emerged as an attractive multimedia application providing highly immersive watching experiences since viewers could adjust their viewports at 6 degrees-of-freedom. However, the point cloud frames composing the video are prohibitively large, and effective compression techniques should be developed. There are two classes of compression methods. One suggests exploiting the conventional video codecs (2D-based methods) and the other proposes to compress the points in 3D space directly (3D-based methods). Though the 3D-based methods feature fast coding speeds, their compression ratios are low since the failure of leveraging inter-frame redundancy. To resolve this problem, we design a patch-wise compression framework working in the 3D space. Specifically, we search rigid moves of patches via the iterative closest point algorithm and construct a common geometric structure, which is followed by color compensation. We implement our decoder on a GPU platform so that real-time decoding and rendering are realized. We compare our method with GROOT, the state-of-the-art 3D-based compression method, and it reduces the bitrate by up to 5.98×. Moreover, by trimming invisible content, our scheme achieves comparable bandwidth demand of V-PCC, the representative 2D-based method, in FoV-adaptive streaming.
|confname=MobiSys 2022
|confname = TC'24
|link=https://dl.acm.org/doi/pdf/10.1145/3498361.3539765
|link = https://ieeexplore.ieee.org/document/10360355
|title=Memory-efficient DNN Training on Mobile Devices
|title= A GPU-Enabled Real-Time Framework for Compressing and Rendering Volumetric Videos
|speaker=Wenjie}}
|speaker=Mengfan
{{Latest_seminar
|date=2025-04-18
|abstract = We characterize production workloads of serverless DAGs at a major cloud provider. Our analysis highlights two major factors that limit performance: (a) lack of efficient communication methods between the serverless functions in the DAG, and (b) stragglers when a DAG stage invokes a set of parallel functions that must complete before starting the next DAG stage. To address these limitations, we propose WISEFUSE, an automated approach to generate an optimized execution plan for serverless DAGs for a user-specified latency objective or budget. We introduce three optimizations: (1) Fusion combines in-series functions together in a single VM to reduce the communication overhead between cascaded functions. (2) Bundling executes a group of parallel invocations of a function in one VM to improve resource sharing among the parallel workers to reduce skew. (3) Resource Allocation assigns the right VM size to each function or function bundle in the DAG to reduce the E2E latency and cost. We implement WISEFUSE to evaluate it experimentally using three popular serverless applications with different DAG structures, memory footprints, and intermediate data sizes. Compared to competing approaches and other alternatives, WISEFUSE shows significant improvements in E2E latency and cost. Specifically, for a machine learning pipeline, WISEFUSE achieves P95 latency that is 67% lower than Photons, 39% lower than Faastlane, and 90% lower than SONIC without increasing the cost.
}}
|confname=Proceedings of the ACM on Measurement and Analysis of Computing Systems 2022
|link=https://dl.acm.org/doi/pdf/10.1145/3530892
|title=WiseFuse: Workload Characterization and DAG Transformation for Serverless Workflows
|speaker=Qinyong}}
 
 
 
=== History ===


{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 10:54, 18 April 2025

Time: 2025-04-11 10:30-12:00
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [Mobisys'24] CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference, Zhenhua
    Abstract: While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i.e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance in resource-constrained environments. We propose a new paradigm, CACTUS, for scalable and efficient context-aware classification where a micro-classifier recognizes a small set of classes relevant to the current context and, when context change happens (e.g., a new class comes into the scene), rapidly switches to another suitable micro-classifier. CACTUS features several innovations, including optimizing the training cost of context-aware classifiers, enabling on-the-fly context-aware switching between classifiers, and balancing context switching costs and performance gains via simple yet effective switching policies. We show that CACTUS achieves significant benefits in accuracy, latency, and compute budget across a range of datasets and IoT platforms.
  2. [TC'24] A GPU-Enabled Real-Time Framework for Compressing and Rendering Volumetric Videos, Mengfan
    Abstract: Nowadays, volumetric videos have emerged as an attractive multimedia application providing highly immersive watching experiences since viewers could adjust their viewports at 6 degrees-of-freedom. However, the point cloud frames composing the video are prohibitively large, and effective compression techniques should be developed. There are two classes of compression methods. One suggests exploiting the conventional video codecs (2D-based methods) and the other proposes to compress the points in 3D space directly (3D-based methods). Though the 3D-based methods feature fast coding speeds, their compression ratios are low since the failure of leveraging inter-frame redundancy. To resolve this problem, we design a patch-wise compression framework working in the 3D space. Specifically, we search rigid moves of patches via the iterative closest point algorithm and construct a common geometric structure, which is followed by color compensation. We implement our decoder on a GPU platform so that real-time decoding and rendering are realized. We compare our method with GROOT, the state-of-the-art 3D-based compression method, and it reduces the bitrate by up to 5.98×. Moreover, by trimming invisible content, our scheme achieves comparable bandwidth demand of V-PCC, the representative 2D-based method, in FoV-adaptive streaming.

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

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