A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical
  Computation Offloading
Ruihuai Liang Bo Yang Zhiwen Yu Xuelin Cao Derrick Wing Kwan Ng Chau Yuen
Computation offloading has become a popular solution to support
computationally intensive and latency-sensitive applications by transferring
computing tasks to mobile edge servers (MESs) for execution, which is known as
mobile/multi-access edge computing (MEC). To improve the MEC performance, it is
required to design an optimal offloading strategy that includes offloading
decision (i.e., whether offloading or not) and computational resource
allocation of MEC. The design can be formulated as a mixed-integer nonlinear
programming (MINLP) problem, which is generally NP-hard and its effective
solution can be obtained by performing online inference through a well-trained
deep neural network (DNN) model. However, when the system environments change
dynamically, the DNN model may lose efficacy due to the drift of input
parameters, thereby decreasing the generalization ability of the DNN model. To
address this unique challenge, in this paper, we propose a multi-head ensemble
multi-task learning (MEMTL) approach with a shared backbone and multiple
prediction heads (PHs). Specifically, the shared backbone will be invariant
during the PHs training and the inferred results will be ensembled, thereby
significantly reducing the required training overhead and improving the
inference performance. As a result, the joint optimization problem for
offloading decision and resource allocation can be efficiently solved even in a
time-varying wireless environment. Experimental results show that the proposed
MEMTL outperforms benchmark methods in both the inference accuracy and mean
square error without requiring additional training data.