Task Offloading Based on Incomplete System Information in Cloud-Edge-End Collaborative Architecture

The cloud-edge-end network architecture is an emerging model that integrates the advantages of both cloud and edge computing to effectively enhance the system's computational capacity. However, a key challenge within this architecture is how to efficiently offload tasks to fully utilize distributed computing resources while minimizing task execution latency. Most existing approaches assume that complete system information can be obtained in real time to make task offloading decisions. However, this assumption often leads to high communication overhead in practical applications, and the system's dynamic nature can make obtaining complete information impractical. To address this challenge, we propose a partially observable task offloading algorithm (POTO) that makes efficient offloading decisions based on partial system information. First, we model the task offloading problem as a partially observable Markov decision process (POMDP) and design an encoder using a Long Short-Term Memory (LSTM) network to extract system state information from past offloading decisions. Finally, we improve the actor-critic algorithm with a clipping mechanism and action masking to achieve an efficient offloading strategy. Experimental results show that POTO achieves a 95% task success offloading rate while reducing latency by at least 14% compared to other algorithms. Furthermore, even with partial system information, POTO performs at 96% of the effectiveness of algorithms utilizing complete system information.