A mobile edge computing-based applications execution framework for Internet of Vehicles

doi:10.1007/s11704-021-0425-6

Front. Comput. Sci.

2022, Vol. 16

Issue (5) : 165506 https://doi.org/10.1007/s11704-021-0425-6

RESEARCH ARTICLE

A mobile edge computing-based applications execution framework for Internet of Vehicles

Libing WU^1,^2,³(

), Rui ZHANG¹, Qingan LI¹(

), Chao MA², Xiaochuan SHI²

¹. School of Computer Science, Wuhan University, Wuhan 430000, China
². School of Cyber Science and Engineering, Wuhan University, Wuhan 430000, China
³. Shenzhen Research Institute of Wuhan University, Shenzhen 518052, China

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Abstract

Mobile edge computing (MEC) is a promising technology for the Internet of Vehicles, especially in terms of application offloading and resource allocation. Most existing offloading schemes are sub-optimal, since these offloading strategies consider an application as a whole. In comparison, in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning. In our framework, each application is modelled as a directed acyclic graph, where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks. Both vehicles and MEC server within the communication range can be used as candidate offloading nodes. Then, the offloading involves assigning these computing nodes to subtasks. In addition, the proposed offloading scheme deal with the delay constraint of each subtask. The experimental evaluation show that, compared to existing non-partitioning offloading schemes, this proposed one effectively improves the performance of the application in terms of execution time and throughput.

Keywords mobile edge computing application partition directed acyclic graph offloading Internet of Vehicles

Corresponding Author(s): Libing WU,Qingan LI

Just Accepted Date: 26 January 2021 Issue Date: 12 January 2022

Cite this article:

Libing WU,Rui ZHANG,Qingan LI, et al. A mobile edge computing-based applications execution framework for Internet of Vehicles[J]. Front. Comput. Sci., 2022, 16(5): 165506.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-021-0425-6
https://academic.hep.com.cn/fcs/EN/Y2022/V16/I5/165506

Fig.1 MEC architecture

Fig.2 The architecture of the mobile-edge cloud-enabled in Internet of Vehicles

Fig.3 Application execution in the framework

Symbol	Description
$G = (N, E)$	directed acyclic graph $G$ of application
$N$	the number of nodes of $G$
$E$	the edges of $G$
$c i$	the number of CPU cycles to complete the $i$
$d i j$	the size of data transferred between $i$ and $j$
$c p i$	the computation time of $i$
$c m i j$	the time of data transmission between $i$ and $j$
$l i$	theith subtask is performed locally
$m i$	theith subtask is offloaded on VES
$s i$	theith subtask is offloaded on FES
$T$	the execution time of the application
$T P$	the throughput of the application

Tab.1 The main symbols

Fig.4 An example of a directed acyclic graph for an application partitioning

Fig.5

Symbol	Data value	Description
$B$	5 ?10 MHz	the bandwidth in vehicular networks
$d i j$	25 MB	the maximal transfer data between $i$ and $j$
$c i$	2500 Megacycles	the maximal number of CPU cycles to complete $i$
$F l$	500 ?10000 MHz	the computation capability of local
$F v e s$	20000 MHz	the maximal computation capability of VES
$F f e s$	100000 MHz	the maximal computation capability of FES

Tab.2 The simulation parameters

Group	$N$	CCR	$B / M H z$	Local resources/MHz	$T r e q / m s$
1	*	0.5	10	500	10
2	20	*	10	500	10
3	10	0.2	*	600	10
4	20	0.5	10	*	10
5	15	0.5	10	500	*

Tab.3 The set values of each group of experimental parameters

Fig.6 The impact of the number of nodes (N) on performance. (a) Execution time; (b) Throughput

Fig.7 The impact of CCR values on performance. (a) Execution time; (b) Throughput

Fig.8 The impact of bandwidth size on performance. (a) Execution time; (b) Throughput

Fig.9 The impact of the local resources on performance. (a) Execution time;(b) Throughput

Fig.10 The impact of delay constraints of subtasks (

T r e q

) on performance. (a) Execution time; (b) Throughput

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