The significantly increasing number of vehicles brings convenience to daily life while also introducing significant challenges to the transportation network and air pollution. It has been proved that platooning/clustering-based driving can significantly reduce road congestion and exhaust emissions and improve road capacity and energy efficiency. This paper aims to improve the stability of vehicle clustering to enhance the lifetime of cooperative driving. Specifically, we use a Graph Neural Network (GNN) model to learn effective node representations, which can help aggregate vehicles with similar patterns into stable clusters. To the best of our knowledge, this is the first generalized learnable GNN-based model for vehicular ad hoc network clustering. In addition, our centralized approach makes full use of the ubiquitous presence of the base stations and edge clouds. It is noted that a base station has a vantage view of the vehicle distribution within the coverage area as compared to distributed clustering approaches. Specifically, eNodeB-assisted clustering can greatly reduce the control message overhead during the cluster formation and offload to eNodeB the complex computations required for machine learning algorithms. We evaluated the performance of the proposed clustering algorithms on the open-source highD dataset. The experiment results demonstrate that the average cluster lifetime and cluster efficiency of our GNN-based clustering algorithm outperforms state-of-the-art baselines.
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DLion: Decentralized Distributed Deep Learning in Micro-Clouds
Deep learning (DL) is a popular technique for building models from large quantities of data such as pictures, videos, messages generated from edges devices at rapid pace all over the world. It is often infeasible to migrate large quantities of data from the edges to centralized data center(s) over WANs for training due to privacy, cost, and performance reasons. At the same time, training large DL models on edge devices is infeasible due to their limited resources. An attractive alternative for DL training distributed data is to use micro-clouds---small-scale clouds deployed near edge devices in multiple locations. However, micro-clouds present the challenges of both computation and network resource heterogeneity as well as dynamism. In this paper, we introduce DLion, a new and generic decentralized distributed DL system designed to address the key challenges in micro-cloud environments, in order to reduce overall training time and improve model accuracy. We present three key techniques in DLion: (1) Weighted dynamic batching to maximize data parallelism for dealing with heterogeneous and dynamic compute capacity, (2) Per-link prioritized gradient exchange to reduce communication overhead for model updates based on available network capacity, and (3) Direct knowledge transfer to improve model accuracy by merging the best performing model parameters. We build a prototype of DLion on top of TensorFlow and show that DLion achieves up to 4.2X speedup in an Amazon GPU cluster, and up to 2X speed up and 26% higher model accuracy in a CPU cluster over four state-of-the-art distributed DL systems.
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- Award ID(s):
- 1717834
- NSF-PAR ID:
- 10292672
- Date Published:
- Journal Name:
- HPDC '21
- Page Range / eLocation ID:
- 227 to 238
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3431379.3460643
