| 221 | 0 | 10 |
| 下载次数 | 被引频次 | 阅读次数 |
作为云边端协同网络的关键技术之一，计算卸载是解决边缘嵌入式设备计算能力不足、资源有限等问题的有效手段。当前一些相关工作主要关注在给定环境模拟参数下如何降低延迟、减少能耗等，然而，如何准确感知云边端协同网络的实时变化、灵活地实施任务卸载是一个亟须解决的挑战。提出了名为FreeOffload的云边端协同网络任务卸载框架，利用拓展的伯克利包过滤器(extended Berkeley package filter, eBPF)技术实现了计算资源、网络状态的实时感知。设计了适用于异构嵌入式端设备的任务灵活重卸载方案，实现边缘节点负载均衡。搭建小型云边端协同原型实验系统，评估结果表明，该框架能在引入较小开销的情况下高效灵活地实现端设备任务卸载。
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.
[1] 何涛，杨振东，曹畅，等.算力网络发展中的若干关键技术问题分析[J].电信科学，2022,38(6):62-70.HE T,YANG Z D,CAO C,et al.Analysis of some key technical problems in the development of computing power network[J].Telecommunications science,2022,38(6):62-70.
[2] CHEN Y,ZHANG N,ZHANG Y C,et al.Energy efficient dynamic offloading in mobile edge computing for Internet of Things[J].IEEE transactions on cloud computing,2021,9(3):1050-1060.
[3] QU G J,WU H M,LI R D,et al.DMRO:a deep meta reinforcement learning-based task offloading framework for edge-cloud computing[J].IEEE transactions on network and service management,2021,18(3):3448-3459.
[4] 赵梦远，郝万明，杨守义，等.多用户多边缘的公平卸载及优化策略研究[J].郑州大学学报(理学版),2022,54(5):16-21.ZHAO M Y,HAO W M,YANG S Y,et al.Research on multi-edge fair offloading and optimization strategy for multi-user[J].Journal of Zhengzhou university (natural science edition),2022,54(5):16-21.
[5] 张秋平，孙胜，刘敏，等.面向多边缘设备协作的任务卸载和服务缓存在线联合优化机制[J].计算机研究与发展，2021,58(6):1318-1339.ZHANG Q P,SUN S,LIU M,et al.Online joint optimization mechanism of task offloading and service caching for multi-edge device collaboration[J].Journal of computer research and development,2021,58(6):1318-1339.
[6] LI Y,ZHANG X,SUN Y K,et al.Joint offloading and resource allocation with partial information for multi-user edge computing[C]//2022 IEEE Globecom Workshops.Piscataway:IEEE Press,2022:1736-1741.
[7] XIONG X,ZHENG K,LEI L,et al.Resource allocation based on deep reinforcement learning in IoT edge computing[J].IEEE journal on selected areas in communications,2020,38(6):1133-1146.
[8] BAEK J,KADDOUM G.Heterogeneous task offloading and resource allocations via deep recurrent reinforcement learning in partial observable multifog networks[J].IEEE Internet of Things journal,2021,8(2):1041-1056.
[9] 谢人超，廉晓飞，贾庆民，等.移动边缘计算卸载技术综述[J].通信学报，2018,39(11):138-155.XIE R C,LIAN X F,JIA Q M,et al.Survey on computation offloading in mobile edge computing[J].Journal on communications,2018,39(11):138-155.
[10] MAHENGE M P J,LI C L,SANGA C A.Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications[J].Digital communications and networks,2022,8(6):1048-1058.
[11] WANG M Z,WU T,MA T,et al.Users′ experience matter:Delay sensitivity-aware computation offloading in mobile edge computing[J].Digital communications and networks,2022,8(6):955-963.
[12] TANG T T,LI C,LIU F G.Collaborative cloud-edge-end task offloading with task dependency based on deep reinforcement learning[J].Computer communications,2023,209:78-90.
[13] XU X L,ZHANG X Y,GAO H H,et al.BeCome:blockchain-enabled computation offloading for IoT in mobile edge computing[J].IEEE transactions on industrial informatics,2020,16(6):4187-4195.
[14] STAROVOITOV A.Net:filter:rework/optimize internal BPF interpreter′s instruction set[EB/OL].(2014-03-28)[2023-04-10].https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?id=bd4cf0 ed331a275e9bf5a49e6d0fd55dffc551b8.
[15] ISOVALENT.Cilium GitHub repository[EB/OL].(2023-03-17)[2023-04-10].https://github.com/cilium/cilium.
[16] HE Y,ZOU Z H,SUN K,et al.rapidpatch:firmware hotpatching for real-time embedded devices[C]//31st USENIX Security Symposium.Boston:USENIX Association,2022:2225-2242.
[17] Zephyr.A proven RTOS ecosystem,by developers,for developers[EB/OL].(2023-02-19)[2023-04-10].https://zephyrproject.org/.
[18] FFmpeg.A complete,cross-platform solution to record,convert and stream audio and video[EB/OL].(2022-05-14)[2023-04-10].https://ffmpeg.org/.
[19] EasyDarwin.Open source,high performance,industrial rtsp streaming server[EB/OL].(2017-03-07)[2023-04-10].https://github.com/EasyDarwin/EasyDarwin/.
基本信息:
DOI：10.13705/j.issn.1671-6841.2023284
中图分类号:TP393.09
引用信息:
[1]李硕,严飞,张立强等.面向云边端协同网络的eBPF赋能任务卸载研究[J].郑州大学学报(理学版),2025,57(04):15-22+39.DOI:10.13705/j.issn.1671-6841.2023284.
基金信息:
湖北省重大研究计划项目(2023BAA027); 国家重点研发计划项目(2022YFB3103804)