Multi-task Domain Adaptation for Computation Offloading in Edge-intelligence Networks

TL;DR

This paper presents a novel Multi-Task Domain Adaptation model for computation offloading in edge networks, enabling better generalization across environments while preserving privacy and reducing computational costs.

Contribution

The paper introduces a teacher-student based MTDA approach that adapts offloading models to new domains without source data access, improving performance in dynamic MEC environments.

Findings

Outperforms benchmark methods in accuracy and MSE

Maintains high performance across various scenarios

Effective in environments with many users

Abstract

In the field of multi-access edge computing (MEC), efficient computation offloading is crucial for improving resource utilization and reducing latency in dynamically changing environments. This paper introduces a new approach, termed as Multi-Task Domain Adaptation (MTDA), aiming to enhance the ability of computational offloading models to generalize in the presence of domain shifts, i.e., when new data in the target environment significantly differs from the data in the source domain. The proposed MTDA model incorporates a teacher-student architecture that allows continuous adaptation without necessitating access to the source domain data during inference, thereby maintaining privacy and reducing computational overhead. Utilizing a multi-task learning framework that simultaneously manages offloading decisions and resource allocation, the proposed MTDA approach outperforms benchmark methods regarding mean squared error and accuracy, particularly in environments with increasing numbers of users. It is observed by means of computer simulation that the proposed MTDA model maintains high performance across various scenarios, demonstrating its potential for practical deployment in emerging MEC applications.