On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation

• URL: http://arxiv.org/abs/2107.02466v1
• Date: Tue, 6 Jul 2021 08:24:25 GMT
• Title: On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation
• Authors: Zimu Zheng, Qiong Chen, Chuang Hu, Dan Wang, Fangming Liu
• Abstract summary: We show that task allocation with task importance for Multi-task Transfer Learning (MTL) is a variant of the NP-complete Knapsack problem. We propose a Data-driven Cooperative Task Allocation (DCTA) approach to solve TATIM with high computational efficiency. Our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM.
• Score: 20.20889051697198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes a heavy computation burden to resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify \emph{task importance}. We then show that task allocation with task importance for MTL (TATIM) is a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a Data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study that bridges model and practice via a new architecture and main components design within the AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4\% energy consumption compared with the state-of-the-art when solving TATIM.

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