移动边缘计算中一种贪心策略的内容卸载方案

doi:10.11772/j.issn.1001-9081.2019030509

计算机应用 ›› 2019, Vol. 39 ›› Issue (9): 2664-2668.DOI: 10.11772/j.issn.1001-9081.2019030509

移动边缘计算中一种贪心策略的内容卸载方案

袁培燕, 蔡云云

河南师范大学计算机与信息工程学院, 河南新乡 453007

收稿日期:2019-03-27 修回日期:2019-05-14 发布日期:2019-09-10 出版日期:2019-09-10
通讯作者: 袁培燕
作者简介:袁培燕(1978-),男,河南邓州人,副教授,博士,CCF高级会员,主要研究方向:移动网络、无线通信;蔡云云(1994-),女,河南安阳人,硕士研究生,主要研究方向:移动边缘计算。
基金资助:
国家自然科学基金资助项目（U1804164，U1404602）；河南省科技攻关基金资助项目（172102210341）。

Content offloading scheme of greedy strategy in mobile edge computing system

YUAN Peiyan, CAI Yunyun

College of Computer and Information Engineering, Henan Normal University, Xinxiang Henan 453007, China

Received:2019-03-27 Revised:2019-05-14 Online:2019-09-10 Published:2019-09-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (U1804164, U1404602), the Science and Technology Foundation of Henan Province (172102210341).

摘要/Abstract

摘要：

基于移动边缘计算的内容卸载技术可以有效降低骨干网络的流量压力，提升终端用户体验。针对终端用户与小基站之间的异质接触率，设计了一种贪心策略的内容卸载方案。首先，将内容最优卸载问题转化为内容最大投递率问题；其次，证明最大投递率问题满足子模性，在此基础上，采用贪心算法部署内容，该算法可以以概率（1-1/e）保证其最优性；最后，详细分析了内容流行度指数以及缓存大小对不同卸载方案的影响。实验结果表明，所提方案提高了内容投递率同时降低了内容传输时延。

关键词: 移动边缘计算, 投递率, 内容卸载, 子模性, 贪心算法

Abstract:

Content offloading technology based on mobile edge computing can effectively reduce the traffic pressure on the backbone network and improve the end user's experience. A content offloading scheme of greedy strategy was designed for the heterogeneous contact rate between end users and small base stations. Firstly, the content optimal offloading problem was transformed into the content maximum delivery rate problem. Secondly, the maximum delivery rate problem was proved to satisfy the submodularity. On this basis, the greedy algorithm was used to deploy the content. The algorithm was able to guarantee its optimality with the probability (1-1/e). Finally, the impacts of content popularity index and cache size on different offloading schemes were analyzed in detail. The experimental results show that the proposed scheme improves the content delivery rate and reduces the content transmission delay at the same time.

Key words: mobile edge computing, delivery rate, content offloading, submodularity, greedy algorithm

中图分类号:

TP393.01

袁培燕, 蔡云云. 移动边缘计算中一种贪心策略的内容卸载方案[J]. 计算机应用, 2019, 39(9): 2664-2668.

YUAN Peiyan, CAI Yunyun. Content offloading scheme of greedy strategy in mobile edge computing system[J]. Journal of Computer Applications, 2019, 39(9): 2664-2668.

参考文献

[1] BARBAROSSA S, CECI E, MERLUZZI M, et al. Enabling effective mobile edge computing using millimeterwave links[C]//Proceedings of 2017 IEEE International Conference on Communications Workshops. Piscataway, NJ:IEEE, 2017:367-372.
[2] BELLI D, CHESSA S, FOSCHINI L, et al. A social-based approach to mobile edge computing[C]//Proceedings of the 2018 IEEE Symposium on Computers and Communications. Piscataway, NJ:IEEE, 2018:292-297.
[3] ROMAN R, LOPEZ J, MAMBO M. Mobile edge computing, Fog et al.:a survey and analysis of security threats and challenges[J]. Future Generation Computer Systems, 2018, 78:680-698.
[4] 邓晓衡, 关培源, 万志文,等.基于综合信任的边缘计算资源协同研究[J]. 计算机研究与发展, 2018, 55(3):449-477.(DENG X H, GUAN P Y, WAN Z W, et al. Integrated trust based resource cooperation in edge computing[J]. Journal of Computer Research and Development, 2018, 55(3):449-477.)
[5] GUAN P, DENG X, LIU Y, et al. Analysis of multiple clients' behaviors in edge computing environment[J]. IEEE Transactions on Vehicular Technology, 2018, 67(9):9052-9055.
[6] POULARAKIS K, IOSIFIDIS G, PEFKIANAKIS I, et al. Mobile data offloading through caching in residential 802.11 wireless networks[J]. IEEE Transactions on Network and Service Management, 2016, 13(1):71-84.
[7] HUANG C, CHIANG M, DAO D, et al. V2V data offloading for cellular network based on the Software Defined Network (SDN) inside Mobile Edge Computing (MEC) architecture[J]. IEEE Access, 2018, 6:17741-17755.
[8] QIU L, CAO G. Cache increases the capacity of wireless networks[C]//Proceedings of the 35th Annual IEEE International Conference on Computer Communications. Piscataway, NJ:IEEE, 2016:1-9.
[9] QIU L, CAO G. Popularity-aware caching increases the capacity of wireless networks[C]//Proceedings of the 2017 IEEE Conference on Computer Communications. Washington, DC:IEEE Computer Society, 2019:1-9.
[10] LIU D, YANG C. Optimal content placement for offloading in cache-enabled heterogeneous wireless networks[C]//Proceedings of the 2016 IEEE Global Communications Conference. Piscataway, NJ:IEEE, 2016:1-6.
[11] AL-KANJ L, POOR H V, DAWY Z. Optimal cellular offloading via device-to-device communication networks with fairness constraints[J]. IEEE Transactions on Wireless Communications, 2014, 13(8):4628-4643.
[12] HAN J, LI X, JUN T, et al. Network agile preference-based prefetching for mobile devices[C]//Proceedings of the 2014 IEEE 33rd International Performance Computing and Communications Conference. Piscataway, NJ:IEEE, 2014:1-8.
[13] WANG Y, LIU X, CHU D, et al. EarlyBird:mobile prefetching of social network feeds via content preference mining and usage pattern analysis[C]//Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York:ACM, 2015:67-76.
[14] SANADHYA S, MORAVAPALLE U P, KIM K-H, et al. Precog:action-based time-shifted prefetching for Web applications on mobile devices[EB/OL].[2018-12-20]. http://gnan.ece.gatech.edu/archive/precog_hotweb_17.pdf.
[15] YANG Z H, PAN C H, PAN Y J, et al. Cache placement in two-tier HetNets with limited storage capacity:cache or buffer?[J]. IEEE Transactions on Communications, 2018, 66(11):5415-5429.
[16] LUO J, DENG X, ZHANG H, et al. Ultra-low latency service provision in edge computing[C]//Proceedings of the 2018 IEEE International Conference on Communications. Piscataway, NJ:IEEE, 2018:1-6.
[17] 施巍松,张星洲,王一帆,等.边缘计算:现状与展望[J].计算机研究与发展,2019,56(1):69-89.(SHI W S, ZHANG X Z, WANG Y F, et al. Edge computing:state-of-the-art and future directions[J]. Journal of Computer Research and Development, 2019, 56(1):69-89.)
[18] WEN W, CUI Y, ZHENG F C, et al. Random caching based cooperative transmission in heterogeneous wireless networks[J]. IEEE Transactions on Communications, 2018, 66(7):2809-2825.
[19] AO W C, PSOUNIS K. Distributed caching and small cell cooperation for fast content delivery[C]//Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York:ACM, 2015:127-136.

移动边缘计算中一种贪心策略的内容卸载方案

Content offloading scheme of greedy strategy in mobile edge computing system

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