Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity

被引:26
作者
Li, Wei [1 ,2 ]
Jin, Shunfu [1 ,2 ]
机构
[1] Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao 066004, Hebei, Peoples R China
[2] Yanshan Univ, Key Lab Comp Virtual Technol & Syst Integrat Hebe, Qinhuangdao 066004, Hebei, Peoples R China
关键词
Mobile edge computing; Task offloading; Average delay; Energy consumption level; Cost function; Lagrangian function; Karush-Kuhn-Tucker condition;
D O I
10.1007/s11227-021-03781-w
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
With the development for the technology of mobile edge computing (MEC) and the grave situation for the shortage of global energy, the problem of computation offloading in a cloud computing framework is getting more attention by network managers. In order to improve the experience quality of users and increase the energy efficiency of the system, we focus on the issue of task offloading strategy in MEC system. In this paper, we propose a task offloading strategy in the MEC system with a heterogeneous edge. By considering the execution and transmission of tasks under the task offloading strategy, we present an architecture for the MEC system. We establish a system model composed of M/M/1, M/M/c and M/M/infinity queues to capture the execution process of tasks in local mobile device (MD), MEC server and remote cloud servers, respectively. Moreover, by trading off the average delay of tasks, the energy consumption level of the MD and the offloading expend of the system, we construct a cost function for serving one task and formulate a joint optimization problem for the task offloading strategy accordingly. Furthermore, under the constraints of steady state and proportion scope, we use the Lagrangian function and the corresponding Karush-Kuhn-Tucker (KKT) condition to obtain the optimal task offloading strategy with the minimum system cost. Finally, we carry out numerical experiments on the MEC system to investigate the influence of system parameters on the task offloading strategy and to obtain the optimal results. The experiment results show that the task offloading strategy proposed in this paper can balance the average delay, the energy consumption level and the offloading expend with the optimal allocation ratio.
引用
收藏
页码:12486 / 12507
页数:22
相关论文
共 48 条
[1]  
[Anonymous], 2017, P IEEE 5 WORKSH ADV
[2]   Hierarchical Load Balancing and Clustering Technique for Home Edge Computing [J].
Babou, Cheikh Saliou Mbacke ;
Fall, Doudou ;
Kashihara, Shigeru ;
Taenaka, Yuzo ;
Bhuyan, Monowar H. ;
Niang, Ibrahima ;
Kadobayashi, Youki .
IEEE ACCESS, 2020, 8 :127593-127607
[3]   A2Cloud: An Analytical Model for Application-to-Cloud Matching to Empower Scientific Computing [J].
Balos, Cody ;
De La Vega, David ;
Abuelhaj, Zachariah ;
Kari, Chadi ;
Mueller, David ;
Pallipuram, Vivek K. .
PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD), 2018, :548-555
[4]   Centralized and Distributed Architectures for Energy and Delay Efficient Fog Network-Based Edge Computing Services [J].
Bozorgchenani, Arash ;
Tarchi, Daniele ;
Corazza, Giovanni Emanuele .
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, 2019, 3 (01) :250-263
[5]  
Chen YC, 2017, 2017 9TH INTERNATIONAL CONFERENCE ON ADVANCED INFOCOMM TECHNOLOGY (ICAIT 2017), P155, DOI 10.1109/ICAIT.2017.8388906
[6]  
Dao NN, 2017, I C INF COMM TECH CO, P1280, DOI 10.1109/ICTC.2017.8190921
[7]  
Delfin S, 2019, PROCEEDINGS OF THE 2019 3RD INTERNATIONAL CONFERENCE ON COMPUTING METHODOLOGIES AND COMMUNICATION (ICCMC 2019), P1106, DOI [10.1109/iccmc.2019.8819633, 10.1109/ICCMC.2019.8819633]
[8]   Price Competition in an Oligopoly Market with Multiple IaaS Cloud Providers [J].
Feng, Yuan ;
Li, Baochun ;
Li, Bo .
IEEE TRANSACTIONS ON COMPUTERS, 2014, 63 (01) :59-73
[9]  
Kuo W, 2019, P 8 GLOB C CONS EL G, P1
[10]  
Kwak J, 2014, IEEE INFOCOM SER, P2292, DOI 10.1109/INFOCOM.2014.6848173