霧輔助物聯網中公平節能的計算遷移

陳思光; 尤子慧

doi:10.13374/j.issn2095-9389.2021.02.19.002

霧輔助物聯網中公平節能的計算遷移

doi: 10.13374/j.issn2095-9389.2021.02.19.002

陳思光^{1, 2, ,},
尤子慧¹

1.
南京郵電大學江蘇省寬帶無線通信和物聯網重點實驗室，南京 210003
2.
南京郵電大學江蘇省通信與網絡技術工程研究中心，南京 210003

基金項目: 國家自然科學基金資助項目（61971235, 61771258）；江蘇省“333高層次人才培養工程”資助項目；南京郵電大學‘1311’人才計劃資助項目；中國博士后科學基金（面上一等）資助項目（2018M630590）；網絡與信息安全安徽省重點實驗室開放課題資助項目（AHNIS2020001）；江蘇省博士后科研資助計劃資助項目（ 2021K501C）；賽爾網絡下一代互聯網技術創新資助項目（ NGII20190702）

詳細信息

通訊作者:
E-mail: sgchen@njupt.edu.cn

中圖分類號: TP393.0
計量
- 文章訪問數: 483
- HTML全文瀏覽量: 199
- PDF下載量: 37
- 被引次數: 0
出版歷程
- 收稿日期: 2021-02-19
- 網絡出版日期: 2022-06-21
- 刊出日期: 2022-11-01

Fairness and energy co-aware computation offloading for fog-assisted IoT

CHEN Si-guang^{1, 2
, ,},
YOU Zi-hui¹

1.
Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
Jiangsu Engineering Research Center of Communications and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

More Information

Corresponding author: E-mail: sgchen@njupt.edu.cn

摘要

摘要: 為了構建綠色且長生命周期的物聯網，本文提出了一種霧輔助的公平節能物聯網計算遷移方案。首先，基于霧節點計算能力、帶寬資源以及融合霧節點能耗公平性的遷移決策的聯合考量，構建了一個最小化所有任務完成總能耗的優化問題。其次，提出了基于動量梯度和坐標協同下降的公平性能耗最小化算法用于解決上述混合整數非線性規劃問題。該算法基于霧節點的歷史平均能耗、距離、計算能力以及剩余能量值設計了公平性指標以獲得對于霧節點能耗公平性最優的遷移決策；通過提出的動量梯度與坐標協同下降法，聯合優化霧節點分配給各個任務的計算及帶寬資源占比，達到最小化任務處理總能耗。最后，仿真結果表明本文方案能夠取得較快的收斂速度，且與隨機選擇和貪婪任務遷移方案兩種基準方案相比，本文方案的總能耗最低，霧節點的能耗公平性最高，且網絡壽命分別平均提高了23.6%和31.2%。進一步地，該方案在不同霧節點數量以及不同任務大小的環境下仍然能夠保持性能優勢，體現了方案魯棒性高的特點。
- 計算遷移 /
- 霧計算 /
- 公平性指標 /
- 能耗最小化 /
- 網絡壽命
Abstract: As an extension of the cloud computing paradigm, fog computing has attracted wide attention due to its advantages of low energy consumption, short time delay, and high bandwidth saving. Meanwhile, the fog computing-based computation offloading mechanism provides strong support for alleviating the pressure of data processing, realizing low delay service, and prolonging the network lifetime. To construct a green and long lifetime Internet of Things (IoT), this paper proposes a fairness and energy co-aware computation offloading scheme for fog-assisted IoT. Based on the joint optimization consideration of the fog node’s computing capacity, bandwidth resource, and offloading decision with energy consumption fairness, an optimization problem is first formulated to minimize the total energy consumption of all computation tasks. Second, a momentum gradient and coordinate collaboration descent-based fair energy minimization algorithm are proposed to solve the above mixed integer nonlinear programming problem. In this algorithm, based on the historical average energy consumption, distance, computing capacity, and residual energy of the fog node, a fair index is designed to obtain the offloading decision with the optimal energy consumption fairness. Minimization of the total energy consumption for processing all tasks can be achieved by jointly optimizing the occupation ratios of computing and bandwidth resources with the developed momentum gradient and coordinate collaboration descent method. Finally, simulation results show that the proposed scheme can achieve a faster convergence speed. Meanwhile, the total energy consumption of this scheme is the lowest compared to the random selection and greedy task offloading (GTO) schemes, the energy consumption fairness of the fog node is the highest, and the network lifetime is enhanced by 23.6% and 31.2% on average, respectively. Furthermore, this scheme can still maintain its performance advantage under different numbers of fog nodes and different task sizes, indicating the high robustness of the proposed scheme.
- computation offloading /
- fog computing /
- fairness index /
- energy consumption minimization /
- network lifetime

HTML全文

圖 1 網絡模型

Figure 1. Network model

下載: 全尺寸圖片幻燈片

圖 2 動量梯度下降法和傳統梯度下降法的總能耗對比

Figure 2. Comparison of the total energy consumption between the momentum gradient descent and gradient descent

下載: 全尺寸圖片幻燈片

圖 3 Jain’s公平指數三種方案對比

Figure 3. Comparison of the Jain’s fairness index for the three different schemes

下載: 全尺寸圖片幻燈片

圖 4 總能耗的三種方案對比

Figure 4. Comparison of the total energy consumption for the three different schemes

下載: 全尺寸圖片幻燈片

圖 5 霧節點總歷史平均能耗的三種方案對比

Figure 5. Comparison of the total historical average energy consumption for the three different schemes

下載: 全尺寸圖片幻燈片

圖 6 總能耗關于平均距離以及平均任務大小的對比

Figure 6. Comparison of the total energy consumption versus the average distance and average task size

下載: 全尺寸圖片幻燈片

圖 7 Jain’s公平指數關于平均距離以及霧節點個數的對比

Figure 7. Comparison of the Jain’s fairness index versus the average distance and number of fog nodes

下載: 全尺寸圖片幻燈片

259luxu-164

參考文獻(25)

[1]	Uddin H, Gibson M, Safdar G A, et al. IoT for 5G/B5G applications in smart homes, smart cities, wearables and connected cars // 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). Limassol, 2019: 1
[2]	Sun J, Yao X M, Wang S P, et al. Blockchain-based secure storage and access scheme for electronic medical records in IPFS. IEEE Access, 2020, 8: 59389 doi: 10.1109/ACCESS.2020.2982964
[3]	Chen S G, Zhang S J, Zheng X Y, et al. Layered adaptive compression design for efficient data collection in industrial wireless sensor networks. J Netw Comput Appl, 2019, 129: 37 doi: 10.1016/j.jnca.2019.01.002
[4]	Lyu L J, Nandakumar K, Rubinstein B, et al. PPFA: privacy preserving fog-enabled aggregation in smart grid. IEEE Trans Ind Inform, 2018, 14(8): 3733 doi: 10.1109/TII.2018.2803782
[5]	Chen S G, Yang L, Zhao C X, et al. Double-blockchain assisted secure and anonymous data aggregation for fog-enabled smart grid. Engineering, 2022, 8: 159 doi: 10.1016/j.eng.2020.06.018
[6]	Chen S G, Wang Z H, Zhang H J, et al. Fog-based optimized kronecker-supported compression design for industrial IoT. IEEE Trans Sustain Comput, 2020, 5(1): 95 doi: 10.1109/TSUSC.2019.2906729
[7]	Essalhi S E, El Fenni M R, Chafnaji H. Smart energy management for fog-enabled IoT network // Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications. Rabat, 2020: 1
[8]	Jia M, Yin Z S, Li D B, et al. Toward improved offloading efficiency of data transmission in the IoT-cloud by leveraging secure truncating OFDM. IEEE Int Things J, 2019, 6(3): 4252 doi: 10.1109/JIOT.2018.2875743
[9]	Bonomi F, Milito R, Zhu J, et al. Fog computing and its role in the internet of things // Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. Helsinki, 2012: 13
[10]	Dastjerdi A V, Buyya R. Fog computing: Helping the Internet of Things realize its potential. Computer, 2016, 49(8): 112 doi: 10.1109/MC.2016.245
[11]	Andreev S, Petrov V, Huang K B, et al. Dense moving fog for intelligent IoT: Key challenges and opportunities. IEEE Commun Mag, 2019, 57(5): 34 doi: 10.1109/MCOM.2019.1800226
[12]	Wang Q, Chen S G. Latency-minimum offloading decision and resource allocation for fog-enabled Internet of Things networks. Trans Emerging Tel Tech, 2020, 31(12): e3880
[13]	Guo K, Sheng M, Quek T Q S, et al. Task offloading and scheduling in fog RAN: A parallel communication and computation perspective. IEEE Wirel Commun Lett, 2020, 9(2): 215 doi: 10.1109/LWC.2019.2948860
[14]	Chen S G, Zheng Y M, Lu W F, et al. Energy-optimal dynamic computation offloading for industrial IoT in fog computing. IEEE Trans Green Commun Netw, 2020, 4(2): 566 doi: 10.1109/TGCN.2019.2960767
[15]	Wu Q, Liu H X, Wang R H, et al. Delay-sensitive task offloading in the 802.11p-based vehicular fog computing systems. IEEE Int Things J, 2019, 7(1): 773
[16]	Chen S G, You Z H, Ruan X K. Privacy and energy co-aware data aggregation computation offloading for fog-assisted IoT networks. IEEE Access, 2020, 8: 72424 doi: 10.1109/ACCESS.2020.2987749
[17]	Mukherjee M, Kumar V, Kumar S, et al. Computation offloading strategy in heterogeneous fog computing with energy and delay constraints // 2020. IEEE International Conference on Communications (ICC), Dublin, 2020: 1
[18]	He X Y, Chen Y, Chai K K. Delay-aware energy efficient computation offloading for energy harvesting enabled fog radio access networks // 2018. IEEE 87th Vehicular Technology Conference (VTC Spring), Porto, 2018: 1
[19]	Zhu X, Chen S G, Chen S L, et al. Energy and delay co-aware computation offloading with deep learning in fog computing networks // 2019. IEEE 38th International Performance Computing and Communications Conference (IPCCC), London, 2019: 1
[20]	Xu M, Wang W, Zhang M, et al. Joint optimization of energy consumption and time delay in energy-constrained fog computing networks // 2019. IEEE Global Communications Conference (GLOBECOM), Waikoloa, 2019: 1
[21]	Orive A, Agirre A, Bilbao J, et al. Passive network state monitoring for dynamic resource management in industry 4.0 fog architectures // 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE). Munich, 2018: 1414
[22]	Zhang G W, Shen F, Yang Y, et al. Fair task offloading among fog nodes in fog computing networks // 2018. IEEE International Conference on Communications (ICC), Kansas, 2018: 1
[23]	Zhang G W, Shen F, Liu Z N, et al. FEMTO: fair and energy-minimized task offloading for fog-enabled IoT networks. IEEE Int Things J, 2019, 6(3): 4388 doi: 10.1109/JIOT.2018.2887229
[24]	Wu C Y, Xu Y Y. Greedy coordinate descent method on non-negative quadratic programming // 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM). Hangzhou, 2020: 1
[25]	Zhang L Y, Zhang P C, Yang J, et al. Aperture shape generation based on gradient descent with momentum. IEEE Access, 2019, 7: 157623 doi: 10.1109/ACCESS.2019.2949871