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社會屬性感知的邊緣計算任務(wù)調(diào)度策略

王汝言,  聶軒,  吳大鵬,  李紅霞

王汝言, 聶軒, 吳大鵬, 李紅霞. 社會屬性感知的邊緣計算任務(wù)調(diào)度策略[J]. 電子與信息學(xué)報, 2020, 42(1): 271-278. doi: 10.11999/JEIT190301
引用本文: 王汝言, 聶軒, 吳大鵬, 李紅霞. 社會屬性感知的邊緣計算任務(wù)調(diào)度策略[J]. 電子與信息學(xué)報, 2020, 42(1): 271-278. doi: 10.11999/JEIT190301
Ruyan WANG, Xuan NIE, Dapeng WU, Hongxia LI. Social Attribute Aware Task Scheduling Strategy in Edge Computing[J]. Journal of Electronics & Information Technology, 2020, 42(1): 271-278. doi: 10.11999/JEIT190301
Citation: Ruyan WANG, Xuan NIE, Dapeng WU, Hongxia LI. Social Attribute Aware Task Scheduling Strategy in Edge Computing[J]. Journal of Electronics & Information Technology, 2020, 42(1): 271-278. doi: 10.11999/JEIT190301

社會屬性感知的邊緣計算任務(wù)調(diào)度策略

doi: 10.11999/JEIT190301 cstr: 32379.14.JEIT190301
  • 1.

    重慶郵電大學(xué)通信與信息工程學(xué)院 重慶 400065

  • 2.

    重慶高校市級光通信與網(wǎng)絡(luò)重點實驗室 重慶 400065

  • 3.

    泛在感知與互聯(lián)重慶市重點實驗室 重慶 400065

基金項目: 國家自然科學(xué)基金(61771082, 61871062)，重慶市高校創(chuàng)新團(tuán)隊建設(shè)計劃(CXTDX201601020)
詳細(xì)信息
    作者簡介:

    王汝言：男，1969年生，教授，博士，研究方向為泛在網(wǎng)絡(luò)、多媒體信息處理等

    聶軒：男，1995年生，碩士生，研究方向為移動邊緣計算

    吳大鵬：男，1979年生，教授，博士，研究方向為泛在無線網(wǎng)絡(luò)、無線網(wǎng)絡(luò)服務(wù)質(zhì)量控制等

    李紅霞：女，1969年生，高級工程師，研究方向為光無線融合網(wǎng)絡(luò)

    通訊作者:

    吳大鵬　wudp@cqupt.edu.cn

  • 中圖分類號: TP393

Social Attribute Aware Task Scheduling Strategy in Edge Computing

  • 1.

    School of Telecommunication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

  • 2.

    Chongqing Key Laboratory of Optical Communication and Network, Chongqing 400065, China

  • 3.

    Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61771082, 61871062), Chongqing Funded Project of Chongqing University Innovation Team Construction (CXTDX201601020)
  • 摘要:

    邊緣計算服務(wù)器的負(fù)載不均衡將嚴(yán)重影響服務(wù)能力，該文提出一種適用于邊緣計算場景的任務(wù)調(diào)度策略(RQ-AIP)。首先，根據(jù)服務(wù)器的負(fù)載分布情況衡量整個網(wǎng)絡(luò)的負(fù)載均衡度，結(jié)合強化學(xué)習(xí)方法為任務(wù)匹配合適的邊緣服務(wù)器，以滿足傳感器節(jié)點任務(wù)的資源差異化需求；進(jìn)而，構(gòu)造任務(wù)時延和終端發(fā)射功率的映射關(guān)系來滿足物理域的約束，結(jié)合終端用戶社會屬性，為任務(wù)不斷地選擇合適的中繼終端，通過終端輔助調(diào)度的方式實現(xiàn)網(wǎng)絡(luò)的負(fù)載均衡。仿真結(jié)果表明，所提出的策略與其他負(fù)載均衡策略相比能有效地緩解邊緣服務(wù)器之間的負(fù)載和核心網(wǎng)的流量，降低任務(wù)處理時延。

    Abstract:

    Unbalanced load on the edge computing server will seriously affect service capabilities, a task scheduling strategy Reinforced Q-learning-Automatic Intent Picking (RQ-AIP) for edge computing scenarios is proposed. Firstly, the load balance of the entire network is measured based on the load distribution of the server. By combining the reinforcement learning method, the appropriate edge server is matched for the task to meet the resource differentiation needs of sensor node tasks. Then, a mapping relationship between task delay and terminal transmit power is constructed to satisfy the constraints of the physical domain. Combining the social attributes of terminal, the appropriate relay terminal is continuously selected for the task to achieve the load balancing of network by terminal-assisted scheduling. Simulation results show that compared with other load balancing strategies, the proposed strategy can effectively alleviate the load between the edge servers and the traffic of the core network, reduce task processing latency.

    • HTML全文

  • 圖  1  系統(tǒng)框架圖

    圖  2  網(wǎng)絡(luò)模型圖

    圖  3  不同場景下的負(fù)載均衡度

    圖  4  不同服務(wù)器計算資源下的負(fù)載均衡度

    圖  5  不同任務(wù)數(shù)量下的負(fù)載均衡度

    圖  6  不同終端數(shù)目下的平均完成時延

    圖  7  用戶不同發(fā)送功率下的任務(wù)投遞率

    表  1  仿真參數(shù)設(shè)置

    參數(shù)設(shè)定參數(shù)數(shù)值
    任務(wù)到達(dá)率(個/s)[0, 4]
    任務(wù)所需內(nèi)存(GB)[1, 10]
    任務(wù)所需CPU周期(MHz)50
    任務(wù)時延(s)[200, 1500]
    邊緣服務(wù)器CPU頻率(GHz)3
    無線信道帶寬(MHz)5
    邊緣服務(wù)器數(shù)量(個)5
    學(xué)習(xí)因子0.5
    終端發(fā)射功率(W)[0.1, 2]
    噪聲功率(dBm/Hz)–170
    下載: 導(dǎo)出CSV
  • KUMAR K, LIU Jibang, LU Y H, et al. A survey of computation offloading for mobile systems[J]. Mobile Networks and Applications, 2013, 18(1): 129–140. doi: 10.1007/s11036-012-0368-0
    ZENG Deze, GU Lin, GUO Song, et al. Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system[J]. IEEE Transactions on Computers, 2016, 65(12): 3702–3712. doi: 10.1109/TC.2016.2536019
    MAO Yuyi, ZHANG Jun, and LETAIEF K B. Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(12): 3590–3605. doi: 10.1109/JSAC.2016.2611964
    CHEN Xu, JIAO Lei, LI Wenzhong, et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Transactions on Networking, 2016, 24(5): 2795–2808. doi: 10.1109/TNET.2015.2487344
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    SCH?FER D, EDINGER J, ECKRICH J, et al. Hybrid task scheduling for mobile devices in edge and cloud environments[C]. 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, Athens, Greece, 2018: 669–674. doi: 10.1109/PERCOMW.2018.8480201.
    THAM C K and CHATTOPADHYAY R. A load balancing scheme for sensing and analytics on a mobile edge computing network[C]. The 18th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks, Macau, China, 2017: 1–9. doi: 10.1109/WoWMoM.2017.7974307.
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    YOUNES H, BOUATTANE O, YOUSSFI M, et al. New load balancing framework based on mobile AGENT and ant-colony optimization technique[C]. 2017 Intelligent Systems and Computer Vision, Fez, Morocco, 2017: 1–6.
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    TIAN Rui, JIAO Zhenzhen, BIAN Guiyun, et al. A social-based data forwarding mechanism for V2V communication in VANETs[C]. The 10th International Conference on Communications and Networking in China, Shanghai, China, 2015: 595–599. doi: 10.1109/CHINACOM.2015.7498007.
    Cisco visual networking index: Global mobile data traffic forecast update, 2015–2020[EB/OL]. https://www.cisco.com/c/dam/m/en_in/innovation/enterprise/assets/mobile-white-paper-c11-520862.pdf, 2016.
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  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2019-04-27
  • 修回日期:  2019-10-30
  • 網(wǎng)絡(luò)出版日期:  2019-11-13
  • 刊出日期:  2020-01-21

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