面向云边端协同网络的eBPF赋能任务卸载研究

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中图分类号： TP393 文献标志码： A

文章编号： 1671-6841（2025）04-0015-08

Abstract： As a key enabling technology for cloud-edge-end collaborative networks， computing offloading is an effective approach to alleviate issues like insufficient computing capabilities and limited resources in edge embedded devices. Some existing studies focused primarily on reducing latency and energy consumption in simulated settings. Yet accurately perceiving the real-time dynamics of cloud-edge-end collaborative networks and implementing flexible task offloading strategies remained an urgent challenge to tackle. FreeOffload， a task offloading framework for Cloud-Edge-End Collaborative Networks was proposed. Leveraging eBPF technology， FreeOffload realized real-time awareness of computing resources and network status. It also incorporated flexible task re-offloading schemes tailored for heterogeneous embedded end devices， which achieved load balancing across edge nodes. A small-scale cloud-edge-end prototype tested for evaluation was constructed. Results demonstrated that FreeOffload while efficiently and flexibly offloaded tasks from end devices， with low overhead.

Key words： cloud-edge-end collaborative network; extended Berkeley packet filter; task offloading; load balance

0 引言

随着 5G 人工智能时代的到来，新型网络业务不断出现，对算力的需求急剧增加，高算力和低时延的应用场景越来越多样化，其中包括物联网、车联网、云游戏、AR / VR 等。（剩余14036字）