LSTM-based Service Migration for Pervasive Cloud Computing

被引：6

作者：

Jing, Haifeng ^{[1
]}

Zhang, Yafei ^{[2
]}

Zhou, Jiehan ^{[3
]}

Zhang, Weishan ^{[2
]}

Liu, Xin ^{[1
]}

Min, Guizhi ^{[4
]}

Zhang, Zhanmin ^{[4
]}

机构：

[1] China Univ Petr, Sch Comp & Commun Engn, Qingdao, Shandong, Peoples R China

[2] China Univ Petr, Dept Software Engn, Qingdao, Shandong, Peoples R China

[3] Univ Oulu, Oulu, Finland

[4] Huabei Oilfield Co, PetroChina, Engn Technol Res Inst, Renqiu, Peoples R China

来源：

IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY | 2018年

基金：

中国国家自然科学基金;

关键词：

Pervasive Cloud Computing; Service Migration; Machine Learning; LSTM;

D O I：

10.1109/Cybermatics_2018.2018.00305

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Service migration in pervasive cloud computing is important for leveraging cloud resources to execute mobile applications effectively and efficiently. This paper proposes a LSTM (long and short-term memory model) based service migration approach for pervasive cloud computing, i.e., LSTM4PCC, which supports an accurate prediction of cloud resources. LSTM4PCC makes a prediction for cloud resource availability with a LSTM network and establishes a service migration mechanism in order to optimize service executions. We evaluate LSTM4PCC and compare it with the ARIMA (AutoRegressive Integrated Moving Average) approach in terms of prediction accuracy. The results show that LSTM4PCC performs better than ARIMA.

引用

页码：1835 / 1840

页数：6

共 27 条

[1]

[Anonymous], 2010, P ACM MOBISYS, DOI [10.1145/1814433.1814441, DOI 10.1145/1814433.1814441]

[2]

[Anonymous], 2009, TECH REP

[3]

Black Michael, 2009, Proceedings of the 2009 10th IEEE/ACM International Conference on Grid Computing (GRID), P9, DOI 10.1109/GRID.2009.5353077

[4]

Buchbinder N, 2011, LECT NOTES COMPUT SC, V6640, P172, DOI 10.1007/978-3-642-20757-0_14

[5] Decentralized Computation Offloading Game for Mobile Cloud Computing [J].

Chen, Xu .

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2015, 26 (04) :974-983

[6]

Chun BG, 2011, EUROSYS 11: PROCEEDINGS OF THE EUROSYS 2011 CONFERENCE, P301

[7] Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing [J].

Gai, Keke ;

Qiu, Meikang ;

Zhao, Hui ;

Tao, Lixin ;

Zong, Ziliang .

JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2016, 59 :46-54

[8]

Gkatzikis L, 2014, IEEE INT CONF CL NET, P204, DOI 10.1109/CloudNet.2014.6968993

[9] Task-resource scheduling problem [J].

Anna Gorbenko ;

Vladimir Popov .

International Journal of Automation and Computing, 2012, 9 (4) :429-441

[10]

Jing Tai Piao, 2010, Proceedings 2010 9th International Conference on Grid and Cloud Computing (GCC 2010), P87, DOI 10.1109/GCC.2010.29

← 1 2 3 →