  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.
