Revenue maximization strategy for mobile-edge computing server with limited computing resources

doi:10.11772/j.issn.1001-9081.2019081351

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (3): 765-769.DOI: 10.11772/j.issn.1001-9081.2019081351

• Advanced computing • Previous Articles Next Articles

Revenue maximization strategy for mobile-edge computing server with limited computing resources

HUANG Dongyan¹, FU Zhongwei², WANG Bo²

1. College of Electronics and Information Engineering, Shenzhen University, Shenzhen Guangdong 518060, China;
2. Ministry Education Key Laboratory of Cognitive Radio and Information Processing(Guilin University of Electronic Technology), Guilin Guangxi 541004, China

Received:2019-08-05 Revised:2019-10-13 Online:2020-03-10 Published:2019-10-31
Supported by:
This work is partially supported by the Scientific Base and Talent Special Project of Guangxi (GUIKEAD19110042), the Director Fund of Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing (GXKL06160111).

计算资源受限的移动边缘计算服务器收益优化策略

黄冬艳¹, 付中卫², 王波²

1. 深圳大学电子与信息工程学院, 广东深圳 518060;
2. 认知无线电与信息处理省部共建教育部重点实验室(桂林电子科技大学), 广西桂林 541004

通讯作者: 王波
作者简介:黄冬艳(1984-),女,广西南宁人,博士,主要研究方向:移动边缘计算、区块链;付中卫(1993-),男,河南商丘人,硕士研究生,主要研究方向:移动边缘计算、资源优化;王波(1977-),男,陕西西安人,讲师,博士,主要研究方向:5G移动通信。
基金资助:
广西科技基地和人才专项（桂科AD19110042）；广西无线宽带通信与信号处理重点实验室主任基金资助项目（GXKL06160111）。

Abstract

Abstract: Mobile Edge Computing (MEC) servers receive revenue by leasing computing resources to users. Improving revenue with limited computing resources is critical for MEC servers. Therefore, a strategy of improving MEC server revenue by the optimization of computing task execution order was proposed. Firstly, the revenue maximization problem of servers was modeled to an optimization problem with task execution order as optimization parameter. Then, an algorithm based on the branch and bound approach was proposed to find the optimal task execution order. Simulation results show that the average revenue of MEC server of the proposed algorithm is 11%, 14% and 21% higher than those of Large Task First (LTF) algorithm, Low-Latency Task First (LLTF) algorithm, and First Come First Served (FCFS) algorithm respectively. The proposed strategy can significantly improve the servers’ revenue while guaranteeing offloading users’ Quality of Service (QoS).

Key words: Mobile Edge Computing (MEC), revenue maximization, limited computing resource, computing resource allocation, branch and bound approach

摘要： 移动边缘计算（MEC）服务器通过向用户提供计算资源获得收益。对MEC服务器而言，如何在计算资源受限的情况下提高自身收益至关重要，为此提出一种通过优化计算任务执行次序提高MEC服务器收益的策略。首先，将MEC服务器收益最大化问题建模为以任务执行次序为优化变量的优化问题；然后提出了一种基于分支定界法的算法求解任务执行次序。仿真结果表明，采用所提算法获得的MEC服务器平均收益分别比大任务优先（LTF）算法、低延迟任务优先（LLTF）算法和先到先服务（FCFS）算法提高了11%、14%和21%。在保证卸载用户服务质量（QoS）同时，所提策略可以显著提高服务器的收益。

关键词: 移动边缘计算, 收益最大化, 计算资源受限, 计算资源分配, 分支定界法

CLC Number:

TP391.9

HUANG Dongyan, FU Zhongwei, WANG Bo. Revenue maximization strategy for mobile-edge computing server with limited computing resources[J]. Journal of Computer Applications, 2020, 40(3): 765-769.

黄冬艳, 付中卫, 王波. 计算资源受限的移动边缘计算服务器收益优化策略[J]. 计算机应用, 2020, 40(3): 765-769.

References

[1] CHIANG M,ZHANG T. Fog and IoT:an overview of research opportunities[J]. IEEE Internet of Things Journal,2016,3(6):854-864.
[2] EVANS D. The Internet of things:how the next evolution of the Internet is changing everything[EB/OL].[2019-03-26]. https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf.
[3] European Telecommunications Standards Institute, Mobile-edge computing-introductory technical white paper[EB/OL].[2019-03-23]. https://portal.etsi.org/portals/0/tbpages/mec/docs/mobile edge computing introductory technical white paper v1.
[4] TALEB T,SAMDANIS K,MADA B,et al. On multi-access edge computing:a survey of the emerging 5G network edge architecture and orchestration[J]. IEEE Communications Surveys and Tutorials,2017,19(3):1657-1681.
[5] HOANG D T,NIYATO D,WANG P. Optimal admission control policy for mobile cloud computing hotspot with cloudlet[C]//Proceedings of the 2012 IEEE Wireless Communications and Networking Conference:Services, Applications, and Business. Piscataway:IEEE,2012:3145-3149.
[6] YU Y,ZHANG J,LETAIEF K B. Joint subcarrier and CPU time allocation for mobile edge computing[C]//Proceedings of the 2016 IEEE Global Communications Conference. Piscataway:IEEE, 2016:1-6.
[7] CHEN X,JIAO L,LI W,et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Transactions on Networking,2016,24(5):2795-2808.
[8] LI M,YANG S,ZHANG Z,et al. Joint subcarrier and power allocation for OFDMA based mobile edge computing system[C]//Proceedings of the IEEE 28th Annual International Symposium on Personal,Indoor,and Mobile Radio Communications. Piscataway:IEEE,2017:1-6.
[9] XU D,LI Q,ZHU H. Energy-saving computation offloading by joint data compression and resource allocation for mobile-edge computing[J]. IEEE Communications Letters,2019,23(4):704-707.
[10] CAO J,YANG L,CAO J N. Revisiting computation partitioning in future 5G-based edge computing environments[J]. IEEE Internet of Things Journal,2019,6(2):2427-2438.
[11] LIU J,MAO Y,ZHANG J,et al. Delay-optimal computation task scheduling for mobile-edge computing systems[C]//Proceedings of the 2016 IEEE International Symposium on Information Theory. Piscataway:IEEE,2016:1451-1455.
[12] LIU M,LIU Y. Price-based distributed offloading for mobile-edge computing with computation capacity constraints[J]. IEEE Wireless Communications Letters,2017,7(3):420-423.
[13] KIM S H,PARK S,CHEN M,et al. An optimal pricing scheme for the energy efficient mobile edge computation offloading with OFDMA[J]. IEEE Communications Letters,2018,22(9):1992-1925.
[14] GUO J,SONG Z,CUI Y,et al. Energy-efficient resource allocation for multi-user mobile edge computing[C]//Proceedings of the 2017 IEEE Global Communications Conference. Piscataway:IEEE,2017:1-7.
[15] 黄婉珍, 唐国春. 分支定界法求解最小带权误工工件数排序[J]. 应用数学学报,1992,15(2):194-199. (HUANG W Z, TANG G C. A branch and bound approach to minimizing the weighted number of tardy jobs[J]. Acta Mathematicae Applicatae Sinica,1992,15(2):194-199.)

Revenue maximization strategy for mobile-edge computing server with limited computing resources

计算资源受限的移动边缘计算服务器收益优化策略

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 7

Recommended Articles

Metrics

[1]	GUO Mian, ZHANG Jinyou. Computation offloading policy for machine learning in mobile edge computing environments [J]. Journal of Computer Applications, 2021, 41(9): 2639-2645.
[2]	WANG Yijie, FAN Jiafei, WANG Chenyu. Two-stage task offloading strategy based on game theory in cloud-edge environment [J]. Journal of Computer Applications, 2021, 41(5): 1392-1398.
[3]	WANG Daiwei, XU Gaochao, LI Long. Task allocation strategy in unmanned aerial vehicle-assisted mobile edge computing [J]. Journal of Computer Applications, 2021, 41(10): 2928-2936.
[4]	LUO Bin, YU Bo. Computation offloading strategy based on particle swarm optimization in mobile edge computing [J]. Journal of Computer Applications, 2020, 40(8): 2293-2298.
[5]	LIU Xing, YANG Zhen, WANG Xinjun, ZHU Heng. Content distribution acceleration strategy in mobile edge computing [J]. Journal of Computer Applications, 2020, 40(5): 1389-1391.
[6]	YU Yun, LIAN Xiaocan, ZHU Yuhang, TAN Guoping. Resource allocation optimization method for augment reality applications based on mobile edge computing [J]. Journal of Computer Applications, 2019, 39(1): 22-25.
[7]	SHI Zhan, XIN Yu, SUN Yu'e, HUANG He. Task allocation mechanism for crowdsourcing system based on reliability of users [J]. Journal of Computer Applications, 2017, 37(9): 2449-2453.