Intelligent matching task offloading scheme in green computing power networks

LAN Shizhan; MA Lifang

doi:10.11959/j.issn.1000-0801.2025030

您当前的位置：

首页 >

文章列表页 >

Intelligent matching task offloading scheme in green computing power networks

Research and Development | 更新时间：2025-03-11

- Intelligent matching task offloading scheme in green computing power networks
- Telecommunications Science Vol. 41, Issue 2, Pages: 30-40(2025)
- 作者机构：
  
  1.华南理工大学软件学院，广东广州 510006
  2.中国移动通信集团广西有限公司，广西南宁 530012
  3.广西建设职业技术学院信息工程学院，广西南宁 530007
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62471208)
- DOI：10.11959/j.issn.1000-0801.2025030
  CLC： TP18
- Received：23 September 2024，
  
  Revised：2024-12-26，
  
  Published：20 February 2025
- 稿件说明：
移动端阅览
兰世战,马丽芳.绿色算力网络中智能匹配任务卸载方案[J].电信科学,2025,41(02):30-40.

LAN Shizhan,MA Lifang.Intelligent matching task offloading scheme in green computing power networks[J].Telecommunications Science,2025,41(02):30-40.
兰世战,马丽芳.绿色算力网络中智能匹配任务卸载方案[J].电信科学,2025,41(02):30-40. DOI： 10.11959/j.issn.1000-0801.2025030.

LAN Shizhan,MA Lifang.Intelligent matching task offloading scheme in green computing power networks[J].Telecommunications Science,2025,41(02):30-40. DOI： 10.11959/j.issn.1000-0801.2025030.

摘要

算力网络通过整合云、边、端算力资源，为数字经济提供数据感知、传输、运算等一体化服务，但其高速发展伴随亟待解决的高能耗问题。任务卸载技术通过合理分配计算任务，提升用户体验、降低传输时延并减少能耗，是一种重要的解决方案。为降低算力网络的整体能耗，实现绿色可持续发展，提出了一种基于匹配机制的智能匹配任务卸载方案。通过匹配算力网络中的任务与节点资源，减少因不合理任务卸载产生的能源消耗，提升算力网络的整体性能表现。同时采用结合强化学习与神经网络的深度学习方法，进一步优化卸载策略，显著降低算力网络能耗。仿真实验验证表明，该方法具有良好的有效性与可靠性。

Abstract

By integrating cloud

edge

and device resources

the computing power networks provide integrated services such as data sensing

transmission

and computation for the digital economy. However

their rapid development is accompanied by pressing challenges of high energy consumption. The task offloading technology is an important solution that allocates computing tasks properly

improves user experience

reduces transmission latency and energy consumption

making it a crucial solution. In order to reduce the overall energy consumption of computing power networks and achieve green

sustainable development

an intelligent matching task unloading scheme based on matching mechanism was proposed. By matching tasks and node resources in the computing power network

the scheme minimized energy consumption caused by inefficient task offloading and enhances overall network performance. Furthermore

a deep learning approach combining reinforcement learning with neural networks was employed to further optimize the offloading strategy

significantly reducing network energy consumption. Simulation experiments demonstrate that the proposed method is effective and reliable.

关键词

Keywords

references

郭琨 , 康雨馨 , 卓训方 . 京津冀国家算力枢纽节点赋能全球数字经济标杆城市建设 [J ] . 大数据 , 2023 , 9 ( 5 ): 134 - 139 .

GUO K , KANG Y X , ZHUO X F . Beijing-Tianjin-Hebei national integrated big-data center system empowers global digital economy benchmark city construction [J ] . Big Data Research , 2023 , 9 ( 5 ): 134 - 139 .

段晓东 , 姚惠娟 , 付月霞 , 等 . 面向算网一体化演进的算力网络技术 [J ] . 电信科学 , 2021 , 37 ( 10 ): 76 - 85 .

DUAN X D , YAO H J , FU Y X , et al . Computing force network technologies for computing and network integration evolution [J ] . Telecommunications Science , 2021 , 37 ( 10 ): 76 - 85 .

罗军舟 , 金嘉晖 , 宋爱波 , 等 . 云计算: 体系架构与关键技术 [J ] . 通信学报 , 2011 , 32 ( 7 ): 3 - 21 .

LUO J Z , JIN J H , SONG A B , et al . Cloud computing: architecture and key technologies [J ] . Journal on Communications , 2011 , 32 ( 7 ): 3 - 21 .

RAZA K , PATLE V K , ARYA S . A review on green computing for eco-friendly and sustainable IT [J ] . Journal of Computational Intelligence and Electronic Systems , 2012 , 1 ( 1 ): 3 - 16 .

林俊宇 , 王慧强 , 马春光 , 等 . 一种基于DAG动态重构的认知网络服务迁移方法 [J ] . 软件学报 , 2014 , 25 ( 10 ): 2373 - 2384 .

LIN J Y , WANG H Q , MA C G , et al . Service migration method for cognitive network based on DAG dynamic reconstruction [J ] . Journal of Software , 2014 , 25 ( 10 ): 2373 - 2384 .

FENG C , HAN P C , ZHANG X , et al . Computation offloading in mobile edge computing networks: a survey [J ] . Journal of Network and Computer Applications , 2022 , 202 : 103366 .

ZHANG J , GUO H Z , LIU J J , et al . Task offloading in vehicular edge computing networks: a load-balancing solution [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 2 ): 2092 - 2104 .

崔勇 , 宋健 , 缪葱葱 , 等 . 移动云计算研究进展与趋势 [J ] . 计算机学报 , 2017 , 40 ( 2 ): 273 - 295 .

CUI Y , SONG J , MIAO C C , et al . Mobile cloud computing research progress and trends [J ] . Chinese Journal of Computers , 2017 , 40 ( 2 ): 273 - 295 .

GUO H Z , LIU J J , REN J , et al . Intelligent task offloading in vehicular edge computing networks [J ] . IEEE Wireless Communications , 2020 , 27 ( 4 ): 126 - 132 .

WANG Y P , LANG P , TIAN D X , et al . A game-based computation offloading method in vehicular multiaccess edge computing networks [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 6 ): 4987 - 4996 .

NING Z L , DONG P R , KONG X J , et al . A cooperative partial computation offloading scheme for mobile edge computing enabled Internet of Things [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 3 ): 4804 - 4814 .

LEI L , XU H J , XIONG X , et al . Joint computation offloading and multiuser scheduling using approximate dynamic programming in NB-IoT edge computing system [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 3 ): 5345 - 5362 .

HAN Y P , ZHAO Z W , MO J W , et al . Efficient task offloading with dependency guarantees in ultra-dense edge networks [C ] // Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM) . Piscataway : IEEE Press , 2019 : 1 - 6 .

ZHANG J , HU X P , NING Z L , et al . Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks [J ] . IEEE Internet of Things Journal , 2018 , 5 ( 4 ): 2633 - 2645 .

YUAN X W , XIE Z D , TAN X . Computation offloading in UAV-enabled edge computing: a Stackelberg game approach [J ] . Sensors , 2022 , 22 ( 10 ): 3854 .

ISMAIL A H , EL-BAHNASAWY N A , HAMED H F A . AGCM: active queue management-based green cloud model for mobile edge computing [J ] . Wireless Personal Communications , 2019 , 105 ( 3 ): 765 - 785 .

WANG J , HU J , MIN G Y , et al . Computation offloading in multi-access edge computing using a deep sequential model based on reinforcement learning [J ] . IEEE Communications Magazine , 2019 , 57 ( 5 ): 64 - 69 .

HUANG L , BI S Z , ZHANG Y J A . Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks [J ] . IEEE Transactions on Mobile Computing , 2020 , 19 ( 11 ): 2581 - 2593 .

ZHENG C , LIU S H , HUANG Y M , et al . Hybrid policy learning for energy-latency tradeoff in MEC-assisted VR video service [J ] . IEEE Transactions on Vehicular Technology , 2021 , 70 ( 9 ): 9006 - 9021 .

LIU F Z , HUANG J W , WANG X B . Joint task offloading and resource allocation for device-edge-cloud collaboration with subtask dependencies [J ] . IEEE Transactions on Cloud Computing , 2023 , 11 ( 3 ): 3027 - 3039 .

MESKAR E , TODD T D , ZHAO D M , et al . Energy aware offloading for competing users on a shared communication channel [J ] . IEEE Transactions on Mobile Computing , 2017 , 16 ( 1 ): 87 - 96 .

ZHOU H , JIANG K , LIU X X , et al . Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 2 ): 1517 - 1530 .

CHEN J , XING H L , XIAO Z W , et al . A DRL agent for jointly optimizing computation offloading and resource allocation in MEC [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 24 ): 17508 - 17524 .

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 .

QU B , BAI Y , CHU Y , et al . Resource allocation for MEC system with multi-users resource competition based on deep reinforcement learning approach [J ] . Computer Networks , 2022 , 215 : 109181 .

ZHANG Z C , YU F R , FU F , et al . Joint offloading and resource allocation in mobile edge computing systems: an actor-critic approach [C ] // Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM) . Piscataway : IEEE Press , 2018 : 1 - 6 .

TAN L , KUANG Z F , ZHAO L , et al . Energy-efficient joint task offloading and resource allocation in OFDMA-based collaborative edge computing [J ] . IEEE Transactions on Wireless Communications , 2022 , 21 ( 3 ): 1960 - 1972 .

WANG P , LI K L , XIAO B , et al . Multiobjective optimization for joint task offloading, power assignment, and resource allocation in mobile edge computing [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 14 ): 11737 - 11748 .

ZHOU X B , GE S X , LIU P B , et al . DAG-based dependent tasks offloading in MEC-enabled IoT with soft cooperation [J ] . IEEE Transactions on Mobile Computing , 2024 , 23 ( 6 ): 6908 - 6920 .

LV X Y , DU H W , YE Q . TBTOA: a DAG-based task offloading scheme for mobile edge computing [C ] // Proceedings of the ICC 2022 - IEEE International Conference on Communications . Piscataway : IEEE Press , 2022 : 4607 - 4612 .

CAO Z Q , DENG X H , YUE S , et al . Dependent task offloading in edge computing using GNN and deep reinforcement learning [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 12 ): 21632 - 21646 .

FU X , TANG B , GUO F Y , et al . Priority and dependency-based DAG tasks offloading in fog/edge collaborative environment [C ] // Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) . Piscataway : IEEE Press , 2021 : 440 - 445 .

刘昌锦 , 韦哲 . 香农公式在扩频通信中的应用 [J ] . 四川兵工学报 , 2013 , 34 ( 4 ): 80 - 83 .

LIU C J , WEI Z . Research on application of Shannon formula in spread spectrum communication [J ] . Journal of Sichuan Ordnance , 2013 , 34 ( 4 ): 80 - 83 .

QIN M , CHENG N , JING Z W , et al . Service-oriented energy-latency tradeoff for IoT task partial offloading in MEC-enhanced multi-RAT networks [J ] . IEEE Internet of Things Journal , 2021 , 8 ( 3 ): 1896 - 1907 .

XU Y G , LIU L , DING Z J . DAG-aware joint task scheduling and cache management in spark clusters [C ] // Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS) . Piscataway : IEEE Press , 2020 : 378 - 387 .

HEIDARI A , ALI JABRAEIL JAMALI M , JAFARI NAVIMIPOUR N , et al . Deep Q-learning technique for offloading offline/online computation in blockchain-enabled green IoT-edge scenarios [J ] . Applied Sciences , 2022 , 12 ( 16 ): 8232 .

WANG T , LIANG Y Z , YANG Y , et al . An intelligent edge-computing-based method to counter coupling problems in cyber-physical systems [J ] . IEEE Network , 2020 , 34 ( 3 ): 16 - 22 .

KINGA D , ADAM J B . A method for stochastic optimization [C ] // International conference on learning representations (ICLR) . Piscataway : IEEE Press , 2015 : 5 - 6 .

MAO B M , TANG F X , KAWAMOTO Y , et al . Optimizing computation offloading in satellite-UAV-served 6G IoT: a deep learning approach [J ] . IEEE Network , 2021 , 35 ( 4 ): 102 - 108 .

ZHAN W H , LUO C B , MIN G Y , et al . Mobility-aware multi-user offloading optimization for mobile edge computing [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 3 ): 3341 - 3356 .

ZHOU W , LIN C W , DUAN J R , et al . An optimized greedy-based task offloading method for mobile edge computing [M ] Lecture Notes in Computer Science . Cham : Springer International Publishing , 2022 : 494 - 508 .

CHEN G , XU X J , ZENG Q T , et al . A vehicle-assisted computation offloading algorithm based on proximal policy optimization in vehicle edge networks [J ] . Mobile Networks and Applications , 2023 , 28 ( 6 ): 2041 - 2055 .

Views

694

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on constrained policy reinforcement learning based multi-objective optimization of computing power network

A survey of neural architecture search

Research on log text fault association rule mining based on GPT network

Large language model for cloud-network configuration audit and its application in IP network

Infrared image human vehicle detection algorithm based on SDL-YOLO

Related Author

Linjiang SHEN

Chang CAO

Chao CUI

Yan ZHANG

Mingjie HE

Jie ZHANG

Shiguang SHAN

LI Bing

Related Institution

Research Institute of China United Network Communications Co., Ltd.

Inspur Communication Information System Co., Ltd.

Key Lab of Intelligent Information Processing,Institute of Computing Technology Chinese Academy of Sciences

University of Chinese Academy of Sciences

Faculty of Electrical Engineering and Computer Science, Ningbo University

⁰