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- W4313014764 abstract "Edge computing enhances the processing capabilities of edge networks for processing mobile users’ jobs. Approaches that dispatch jobs to a single edge cloud are prone to cause task accumulation and excessive latency due to the uncertain workload and limited resources of edge servers. Offloading tasks to lightly-loaded neighbors, which are multiple hops away, alleviates the dilemma but increases transmission cost and security risks. Hence, how to realize the trade-off between computing latency, offloading cost and security during job dispatching is a great challenge. In this paper, we propose an online Deep learning-based model for Secure Collaborative Job Dispatching (DeepSCJD) in multiple edge clouds. Specifically, we first utilize bi-directional long short-term memory to predict the workload of edge servers and apply the graph neural networks to aggregate the features of directed acyclic graph jobs as well as undirected weighted topology of edge servers. Based on the state composed of these two features, a deep reinforcement learning agent including a simple deep Q network and linear branch, generates a final dispatching decision of tasks, aiming to achieve the smallest average weighted cost. Experiments on real-world data sets demonstrate the efficiency of proposed model and its superiority over traditional and state-of-the-art baselines, reaching the maximum average performance improvement of 54.16% relative to K-Hop. Extensive evaluations manifest the generalization of our model under various conditions." @default.
- W4313014764 created "2023-01-05" @default.
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- W4313014764 date "2022-01-01" @default.
- W4313014764 modified "2023-10-16" @default.
- W4313014764 title "DeepSCJD: An Online Deep Learning-Based Model for Secure Collaborative Job Dispatching in Edge Computing" @default.
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- W4313014764 doi "https://doi.org/10.1007/978-3-031-20984-0_34" @default.
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