Two-Level Multi-Access Edge Computing Energy-Saving Offloading Strategy for IoT

doi:10.3778/j.issn.1002-8331.2108-0069

Abstract

Abstract: Multi-access edge computing（MEC） technology sinks computing and storage resources to the edge of the network, which can greatly improve the computing power and real-time performance of the Internet of things（IoT） system. However, MEC is often constrained by the growth of computing demand and energy constraints. Thus, efficient computing offloading and energy consumption optimization mechanism is an important research direction in MEC technology. To ensure the computing efficiency while maximizing the energy efficiency in the computing process, a two-level edge nodes（ENs） relay network model is proposed, and an optimal energy consumption algorithm（OECA） for joint optimization of computing resources and channel resources is designed. Firstly, the energy efficiency in MEC is modeled as a 0-1 knapsack problem. Secondly, the system adaptively selects the computing mode and allocates wireless channel resources, aiming at minimizing the overall energy consumption of the system. Finally, the OECA algorithm is simulated and verified in Python environment. The simulation results show that OECA can increase the network capacity by 18.3% and reduce the energy consumption by 13.1% compared with the offloading strategy algorithm based on directed acyclic graph algorithm（DAGA）.

Key words: multi-access edge computing, Internet of things（IoT）, offloading strategy, communication energy consumption

摘要： 多接入边缘计算（multi-access edge computing，MEC）技术将计算和存储资源下沉到网络边缘，可大幅提高物联网（Internet of things，IoT）系统的计算能力和实时性。然而，MEC往往面临计算需求增长和能量受限的约束，高效的计算卸载及能耗优化机制是MEC技术中重要的研究领域。为保证计算效率的同时最大程度提升计算过程中的能效，提出了两级边缘节点（edge nodes，ENs）中继网络模型，并设计了一种计算资源及信道资源联合优化的最优能耗卸载策略算法（optimal energy consumption algorithm，OECA）。将MEC中的能效建模为0-1背包问题；以最小化系统总体能耗为目标，系统自适应地选择计算模式和分配无线信道资源；在Python环境下仿真验证了算法性能。仿真结果表明，相比于基于有向无环图的卸载策略算法（directed acyclic graph algorithm，DAGA），OECA可将网络容量提升18.3%，能耗缩减13.1%。

关键词: 多接入边缘计算, 物联网, 卸载策略, 通信能耗

DENG Yu, ZHAO Junhui, ZHANG Qingmiao. Two-Level Multi-Access Edge Computing Energy-Saving Offloading Strategy for IoT[J]. Computer Engineering and Applications, 2022, 58(13): 94-101.

邓宇, 赵军辉, 张青苗. 面向IoT的两级多接入边缘计算节能卸载策略[J]. 计算机工程与应用, 2022, 58(13): 94-101.

References

[1] LIAO J Y，ZHAO J H，GAO F F，et al.A model-driven deep learning method for massive MIMO detection[J].IEEE Communications Letters，2020，24（8）：1724-1728.
[2] TAO Y Z，LIU L，LIU S，et al.A survey：Several technologies of non-orthogonal transmission for 5G[J].China Communications，2015，12（10）：1-15.
[3] ZHANG W S，WANG C X，GE X H，et al.Enhanced 5G cognitive radio networks based on spectrum sharing and spectrum aggregation[J].IEEE Transactions on Communications，2018，66（12）：6304-6316.
[4] PAN C H，ELKASHLAN M，WANG J Z，et al.User-centric C-RAN architecture for ultra-dense 5G networks：Challenges and methodologies[J].IEEE Communications Magazine，2018，56（6）：14-20.
[5] GE X H，PAN L H，LI Q，et al.Multipath cooperative communications networks for augmented and virtual reality transmission[J].IEEE Transactions on Multimedia，2017，19（10）：2345-2358.
[6] ZHAO J H，LIU J，YANG L H.Future 5G-oriented system for urban rail transit：Opportunities and challenges[J].China Communications，2021，18（2）：1-12.
[7] ZHAO J H，DONG P Y，MA X T，et al.Mobile-aware and relay-assisted partial offloading scheme based on parked vehicles in B5G vehicular networks[J].Physical Communication，2020，42（4）：101163.
[8] SHIRAZ M，GANI A，KHOKHAR R H，et al.A review on distributed application processing frameworks in smart mobile devices for mobile cloud computing[J].IEEE Communications Surveys & Tutorials，2013，15（3）：1294-1313.
[9] FOWLEY F，PAHL C，JAMSHIDI P，et al.A classification and comparison framework for cloud service brokerage architectures[J].IEEE Transactions on Cloud Computing，2018，6（2）：358-371.
[10] TALEB T，SAMDANIS K，MADA B，et al.On multi-access edge computing：A survey of the emerging 5G network edge cloud architecture and orchestration[J].IEEE Communications Surveys & Tutorials，2017，19（3）：1657-1681.
[11] 刘炎培，朱淇，赵进超.边缘环境下计算密集型应用的卸载技术研究[J].计算机工程与应用，2020，56（15）：1-14.
LIU Y P，ZHU Q，ZHAO J C.Survey of offloading technology for computing-intensive applications in edge environment[J].Computer Engineering and Applications，2020，56（15）：1-14.
[12] ZHAO Z W，GE M Y，GAO W F，et al.Deploying edge computing nodes for large-scale IoT：A diversity aware approach[J].IEEE Internet of Things Journal，2018，5（5）：3606-3614.
[13] CHEN W W，DONG W，LI K Q.Multi-user multi-task computation offloading in green mobile edge cloud computing[J].IEEE Transactions on Services Computing，2019，12（5）：726-738.
[14] LIU Y Q，PENG M G，SHOU G C，et al.Toward edge intelligence：Multiaccess edge computing for 5G and internet of things[J].IEEE Internet of Things Journal，2020，7（8）：6722-6747.
[15] HUANG X M，YU R，KANG J W，et al.Exploring mobile edge computing for 5G-enabled software defined vehicular networks[J].IEEE Wireless Communications，2017，24（6）：55-63.
[16] SHU C，ZHAO Z W，HAN Y P，et al.Multi-user offloading for edge computing networks：A dependency-aware and latency-optimal approach[J].IEEE Internet of Things Journal，2020，7（3）：1678-1689.
[17] SHU C，ZHAO Z W，HAN Y P，et al.Dependency-aware and latency-optimal computation offloading for multi-user edge computing networks[C]//Proceedings of the 16th Annual IEEE International Conference on Sensing，Communication，and Networking（SECON），10-13 June，2019：1-9.
[18] 胡恒，金凤林，郎思琪.移动边缘计算环境中的计算卸载技术研究综述[J].计算机工程与应用，2021，57（14）：60-74.
HU H，JIN F L，LANG S Q.Survey of research on computation offloading technology in mobile edge computing environment[J].Computer Engineering and Applications，2021，57（14）：60-74.
[19] MA S Y，SONG S D，ZHAO J M，et al.Joint network selection and service placement based on particle swarm optimization for multi-access edge computing[J].IEEE Access，2020，8：160871-160881.
[20] ZHANG X，HUANG H J，HAO Y，et al.Resource provi-sioning in the edge for IoT applications with multilevel services[J].IEEE Internet of Things Journal，2019，6（3）：4262-4271.