Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT

被引:103
作者
Luo, Juan [1 ]
Yin, Luxiu [1 ]
Hu, Jinyu [1 ]
Wang, Chun [1 ]
Liu, Xuan [1 ]
Fan, Xin [1 ]
Luo, Haibo [2 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha, Hunan, Peoples R China
[2] Minjiang Univ, Elect Informat & Control, Fujian Univ, Engn Res Ctr, Fuzhou, Fujian, Peoples R China
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2019年 / 97卷
基金
中国国家自然科学基金;
关键词
Container; Docker; Energy balancing; Fog computing; Multi-cloud;
D O I
10.1016/j.future.2018.12.063
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The traditional architecture of fog computing is for one data center and multiple fog nodes. It is unable to adapt to the current development of private cloud. In addition, virtual machines used for cloud computing, are also used for fog computing as the resource unit, cannot satisfy the requirement of fog computing. Furthermore, the limited capacity of battery power has been one of the major constraints when considering cloud-to-fog-to-sensor pattern in the scenario of Energy Internet. We propose a multi-cloud to multi-fog architecture and design two kinds of service models by employing containers to improve the resource utilization of fog nodes and reduce the service delay. According to the two service models, we present a task scheduling algorithm for energy balancing. The algorithm is based on the transmission energy consumption of terminal devices and uses a dynamic threshold strategy to schedule requests in real time, thereby guaranteeing the energy balancing of terminal devices without increasing the transmission delay. Experimental results show that our proposed service models and scheduling algorithm can reduce service latency, improve fog node efficiency, and prolong WSNs life cycle through energy balancing. (C) 2018 Published by Elsevier B.V.
引用
收藏
页码:50 / 60
页数:11
相关论文
共 24 条
[1]  
[Anonymous], 2016, Cisco Global Cloud Index 2015-2020
[2]  
Bonomi F., 2012, Proceedings of the first edition of the MCC workshop on Mobile cloud computing, P13, DOI [DOI 10.1145/2342509.2342513, 10.1145/2342509.2342513]
[3]  
Felter W, 2015, INT SYM PERFORM ANAL, P171, DOI 10.1109/ISPASS.2015.7095802
[4]   INTERNET OF THINGS CLOUD: ARCHITECTURE AND IMPLEMENTATION [J].
Hou, Lu ;
Zhao, Shaohang ;
Xiong, Xiong ;
Zheng, Kan ;
Chatzimisios, Periklis ;
Hossain, M. Shamim ;
Xiang, Wei .
IEEE COMMUNICATIONS MAGAZINE, 2016, 54 (12) :32-39
[5]   Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures [J].
Hou, Xueshi ;
Li, Yong ;
Chen, Min ;
Wu, Di ;
Jin, Depeng ;
Chen, Sheng .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2016, 65 (06) :3860-3873
[6]   Determining Overhead, Variance & Isolation Metrics in Virtualization for IaaS Cloud [J].
Ismail, Bukhary Ikhwan ;
Jagadisan, Devendran ;
Khalid, Mohammad Fairus .
DATA DRIVEN E-SCIENCE, ISGC 2010: USE CASES AND SUCCESSFUL APPLICATIONS OF DISTRIBUTED COMPUTING INFRASTRUCTURES, 2011, :315-330
[7]   Dynamic Interference Steering in Heterogeneous Cellular Networks [J].
Li, Z. ;
Guo, F. ;
Shu, C. ;
Shin, Kang G. ;
Liu, J. .
IEEE ACCESS, 2018, 6 :28552-28562
[8]  
Liang K., 2015, IEEE NETWORK, V31, P80
[9]   Live and Incremental Whole-System Migration of Virtual Machines Using Block-Bitmap [J].
Luo, Yingwei ;
Zhang, Binbin ;
Wang, Xiaolin ;
Wang, Zhenlin ;
Sun, Yifeng ;
Chen, Haogang .
2008 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING, 2008, :99-106
[10]   Simulation for Training Cochlear Implant Electrode Insertion [J].
Ma, Xingjun ;
Wijewickrema, Sudanthi ;
Zhou, Yun ;
Copson, Bridget ;
Bailey, James ;
Kennedy, Gregor ;
O'Leary, Stephen .
2017 IEEE 30TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), 2017, :1-6