Data-driven task allocation for multi-task transfer learning on the edge

Qiong Chen, Zimu Zheng, Chuang Hu, Dan Wang, Fangming Liu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

13 Citations (Scopus)

Abstract

Edge computing for machine learning has become a heated research topic. On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, one obstacle is that transfer learning imposes heavy computation burden to the resource-constrained edge devices. Motivated by the fact that only a few tasks of Multi-task Transfer Learning (MTL) have a higher potential for overall decision performance improvement, we design a novel task allocation scheme, which assigns more important tasks to more powerful edge devices to maximize the overall decision performance. In this paper, we focus on task allocation under multi-task scenarios by introducing task importance and make the following contributions. First, we reveal that it is important to measure the impact of tasks on overall decision performance improvement and quantify task importance. We also observe the long-tail property of task importance, i.e., only a few tasks are important, which facilitates more efficient task allocation. Second, we show that task allocation with task importance for MTL (TATIM) is in fact 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 innovatively propose a Data-driven Cooperative Task Allocation (DCTA) approach. Third, we evaluate the performance of our DCTA approach by applying it to a real-world industrial operation (e.g., AIOps) scenario. Experiments show that our DCTA approach can reduce 3.24 times of processing time compared with the state-of-the-art when solving TATIM. We offer our DCTA approach as an effective and practical mechanism for reducing the required resource associated with performing MTL on edge devices.

Original languageEnglish
Title of host publicationProceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1040-1050
Number of pages11
ISBN (Electronic)9781728125190
DOIs
Publication statusPublished - Jul 2019
Event39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019 - Richardson, United States
Duration: 7 Jul 20199 Jul 2019

Publication series

NameProceedings - International Conference on Distributed Computing Systems
Volume2019-July

Conference

Conference39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
Country/TerritoryUnited States
CityRichardson
Period7/07/199/07/19

Keywords

  • AIOps
  • Data-driven Task Allocation
  • Edge Computing
  • Transfer Learning

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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