Online Learning to parallel offloading in heterogeneous wireless networks

Abstract

In the forthcoming era of 6G, the deployment of dense and diverse wireless networks equipped with edge servers makes parallel offloading a crucial technology for multiple access edge computing. However, the effectiveness of parallel offloading is constrained by heterogeneous edge servers (HES) and heterogeneous wireless networks (HWN). Existing solutions often assume that the link state of the wireless network and the computing resources of the edge server are either known or can be estimated as prior knowledge, which is insufficient, especially for HWN. To address this challenge, we propose a novel online learning parallel offloading (OLPO) framework aimed at jointly selecting multiple radio networks and multiple edge servers (MRMS) by learning the unknown and stochastic metrics of wireless links and edge servers. Specifically, we design and compare three OLPO algorithms using multi-armed bandits (MAB) and construct a comparison matrix with the analytic hierarchy process (AHP). Our experimental results demonstrate the effectiveness of the proposed OLPO algorithms and establish their applicability for various traffic types, considering metrics such as delay, delay jitter, packet loss, rate, and energy delay product.