On-edge multi-task transfer learning: model and practice with data-driven task allocation
Open accessAbstract
On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation burden to the 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 task importance. We then show that task allocation with task importance for MTL (TATIM) is a variant of 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 which bridges model and practice via a new architecture and main components design within AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4 percent energy consumption compared with the state-of-the-art when solving TATIM.
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Transfer learning is useful where computing resources are not sufficient, such as Edge or IoT devices.
“This transfer learning is also useful where computing resources are not sufficient such as Edge or IoT devices [70].”
From A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic (2020) · cites this paper as [70]
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- Authors
- Chen, Q., Zheng, Z., Hu, C., Wang, D., & Liu, F.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Huawei Technologies (China) (CN)
- Hong Kong Polytechnic University (HK)
- Venue
- IEEE Transactions on Parallel and Distributed Systems
- Published
- 2020-06-01
- Volume
- 31
- Issue
- 6
- Pages
- 1357-1371
- ISSN
- 1045-9219 (ISSN-L)
- 1558-2183
- 2161-9883
- OpenAlex
- W2996942942
- Cited by
- 88
- References
- 64
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 22 · 2023: 14 · 2022: 19 · 2021: 11 · 2020: 7
- FWCI
- 5.06
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Parallel and Distributed Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61722206, 61761136014, 61520106005)
- NSFC-DFG (392046569)
- National Key Research and Development (2017YFB1001703)
- Fundamental Research Funds for the Central Universities (2017KFKJXX009, 3004210116)
- RGC GRF (PolyU 15210119, CRF C5026-18G, ITF UIM/363, ITF ITS/070/19FP, PolyU 1-ZVPZ)
- Huawei Collaborative
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- edge computing, task allocation, task significance, knapsack problem, resource-constrained edge devices, data scarcity, AIOps, energy consumption, trace-driven simulation
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