Multiobjective Optimization for Computation Offloading in Fog Computing

被引:402
作者
Liu, Liqing [1 ]
Chang, Zheng [2 ]
Guo, Xijuan [1 ]
Mao, Shiwen [3 ]
Ristaniemi, Tapani [2 ]
机构
[1] Yanshan Univ, Coll Informat Sci & Engn, Qinhuangdao 066004, Peoples R China
[2] Univ Jyvaskyla, Dept Math Informat Technol, Jyvaskyla 40014, Finland
[3] Auburn Univ, Dept Elect & Comp Engn, Auburn, AL 36849 USA
基金
芬兰科学院;
关键词
Cloud computing; cost; energy consumption; execution delay; fog computing; offloading probability; power allocation; CLOUDS;
D O I
10.1109/JIOT.2017.2780236
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet of Things. In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay, how to design a cost model for the MDs to enjoy the fog and cloud services is also important. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system. Specifically, three queuing models are applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link are explicitly considered. Based on the theoretical analysis, a multiobjective optimization problem is formulated with a joint objective to minimize the energy consumption, execution delay, and payment cost by finding the optimal offloading probability and transmit power for each MD. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over several existed schemes are observed.
引用
收藏
页码:283 / 294
页数:12
相关论文
共 25 条
[1]  
[Anonymous], PROC 23 INT CONF
[2]   Communicating While Computing [Distributed mobile cloud computing over 5G heterogeneous networks] [J].
Barbarossa, Sergio ;
Sardellitti, Stefania ;
Di Lorenzo, Paolo .
IEEE SIGNAL PROCESSING MAGAZINE, 2014, 31 (06) :45-55
[3]   Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing [J].
Chen, Xu ;
Jiao, Lei ;
Li, Wenzhong ;
Fu, Xiaoming .
IEEE-ACM TRANSACTIONS ON NETWORKING, 2016, 24 (05) :2827-2840
[4]   Fog and IoT: An Overview of Research Opportunities [J].
Chiang, Mung ;
Zhang, Tao .
IEEE INTERNET OF THINGS JOURNAL, 2016, 3 (06) :854-864
[5]   Fog Computing: Helping the Internet of Things Realize Its Potential [J].
Dastjerdi, Amir Vahid ;
Buyya, Rajkumar .
COMPUTER, 2016, 49 (08) :112-116
[6]   Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption [J].
Deng, Ruilong ;
Lu, Rongxing ;
Lai, Chengzhe ;
Luan, Tom H. ;
Liang, Hao .
IEEE INTERNET OF THINGS JOURNAL, 2016, 3 (06) :1171-1181
[7]   Computation Offloading for Service Workflow in Mobile Cloud Computing [J].
Deng, Shuiguang ;
Huang, Longtao ;
Taheri, Javid ;
Zomaya, Albert Y. .
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2015, 26 (12) :3317-3329
[8]   Computing with Nearby Mobile Devices: A Work Sharing Algorithm for Mobile Edge-Clouds [J].
Fernando, Niroshinie ;
Loke, Seng W. ;
Rahayu, Wenny .
IEEE TRANSACTIONS ON CLOUD COMPUTING, 2019, 7 (02) :329-343
[9]   Interior point methods 25 years later [J].
Gondzio, Jacek .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2012, 218 (03) :587-601
[10]   Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds [J].
Guo, Xijuan ;
Liu, Liqing ;
Chang, Zheng ;
Ristaniemi, Tapani .
WIRELESS NETWORKS, 2018, 24 (01) :79-88