MPROF: Multi-dimensional Preference-driven Resource Optimization Framework for Cloud-Edge-End Collaboration

In cloud-edge-end (CEE) collaboration, the resource optimization based on deep reinforcement learning have achieved significant performance improvements in time-slot systems. However, some studies only focus on computing delay and energy consumption in each time slot, ignoring the impact of task backlog queues on system performance. In addition, the delay-oriented optimization tends to offload a large number of tasks to servers, failing to fully utilize the computing resource of mobile devices. To address these issues, we propose the multi-dimensional preference-driven resource optimization framework (MPROF). This study includes several key points: 1) constructing a three-layer heterogeneous architecture that applying the collaboration among edges for CEE; 2) proposing the task backlog estimation mechanism, which mitigates the impact of previous unfinished tasks on the current time slot; 3) proposing the group relative direct-preference policy optimization (GRDPO) that incorporates the preference information for efficient task offloading, and combines it with mathematical programming for the system resource optimization. The simulation experiments are conducted across multiple typical scenarios. The results show that, the proposed framework outperforms existing mainstream methods in task offloading, system delay, task backlog, and energy consumption control, demonstrating certain practical application prospects.