Joint optimization of multi-dimensional resource allocation and task offloading for QoE enhancement in Cloud-Edge-End collaboration

Abstract

Cloud-Edge-End Collaboration (CEEC) computing architecture inherits many merits from both edge computing and cloud computing and thus is considered as a promising candidate for future network services. In CEEC, user’s QoE is impacted by offload performance which should consider offload strategy, computational resources and network status simultaneously. However, previous offload optimization studies neglect the joint consideration of dependent task offloading, computational resources and channel resources, which may not produce potential performance improvement. In this paper, we investigate the joint optimization of dependent task offloading, computational resource allocation, user transmission power control, and channel resource allocation in the CEEC scenario, with the goal of maximizing user’s QoE. Initially, a new QoE metric is defined to capture the impacts of delay and energy consumption on user’s QoE. Following this definition, we formulate the joint optimization problem as a Mixed Integer Nonlinear Programming (MINLP) problem and introduce a method of multi-agent deep reinforcement learning to solve our MINLP problem with high computation complexity. Extensive experiments are performed, and experimental results show that our proposed scheme outperforms baselines in a series of metrics.