关键词: 算力网络, 动态任务卸载, 深度强化学习, 能耗优化, 任务卸载

Abstract:

In Computing Power Network （CPN）， User Device （UE） with limited computational capability and battery capacity rely on external computing nodes for collaborative task processing. The existing study mainly focuses on direct Workflow Task （WT） offloading， but the following key challenges are faced： 1） long waiting latency and high energy consumption caused by task dependencies； 2） high-power mode maintained by UE persistently when precursor task data required to be cached on UE； 3） complexity of offloading decisions increased by uncertainties in resource states within CPN dynamic environments； 4） the difficulty in achieving efficient balance caused by multi-objective conflicts between task completion time and energy consumption. To address these challenges， a Dynamic Optimization and Offloading for Workflow Task （DOOWT） was developed to improve energy efficiency. In the algorithm， the Workflow Structure Optimization （WSO） algorithm was utilized to rearrange the task graph， so as to reduce task waiting latencies， thereby lowering overall energy consumption； a Dynamic-Based Task Offloading （DBTO） algorithm based on Deep Deterministic Policy Gradient （DDPG） was employed to adjust offloading strategies dynamically， thereby enhancing computational performance and resource utilization in CPN. Experimental results demonstrate that compared with conventional methods such as Random unloading （Random）， the proposed method reduces the WT waiting latency by 60%， shortens the average WT completion latency by 79%， and decreases the overall energy consumption by 82%. It can be seen that this method provides theoretical and technical support for the optimization and scheduling of energy consumption-sensitive tasks.

Key words: Computing Power Network (CPN), Dynamic-Based Task Offloading (DBTO), Deep Reinforcement Learning (DRL), energy consumption optimization, task offloading