Game-Based Resource Allocation and Task Offloading Scheme in Collaborative Cloud-Edge Computing System

doi:10.16182/j.issn1004731x.joss.21-0077

Abstract

Abstract:

Considering the delay, energy consumption and computing resource cost, the utility maximization problem in collaborative cloud-edge system is constructed, and divided into three subproblems: computing resource allocation, uplink power allocation and task offloading strategy. A game-based resource allocation and task offloading(GRATO) scheme is proposed to solve those subproblems. The optimal solution of computing resource allocation is obtained by using convex optimization conditions; a low complexity uplink power allocation method is designed to reduce wireless interfere; a game-based distributed task offloading algorithm (GDTOA) is proposed to optimize the task offloading strategy. Simulation results show that the performance of GRATO is better than other schemes on delay and energy consumption, and it can sense the priority of users, resulting in higher utility and lower latency for emergency users..

Key words: edge computing, resource allocation, computation task offloading, game, utility maximization

CLC Number:

TP301.6

Xuewen Wu, Jingxian Liao. Game-Based Resource Allocation and Task Offloading Scheme in Collaborative Cloud-Edge Computing System[J]. Journal of System Simulation, 2022, 34(7): 1468-1481.

Figures/Tables 10

Fig. 1

Table 1

Experimental parameters

参数	数值
系统带宽 $B$ /MHz	20
用户最大上行发射功率 $p m a x$ /dBm	23
背景噪声功率 $σ 2$ /dBm	-100
从BS到云的单位数据传输时延 $v$ / $μ s / b i t - 1$	$1$
能量系数 $κ$	$5 × 10 - 27$
MEC服务器的CPU频率F/GHz	20
用户设备的CPU频率 $f i l$ /GHz	1
MEC服务器的计算资源成本/ $$ / G H z - 1$	$0.015$
云服务器的计算资源成本/ $$ / G H z - 1$	$0.03$

Table 1

Fig. 2

Table 2

Runtime of schemes

方案	$N = 5$	$N = 10$
MCORA	0.096±0.02	0.22±0.07
COSO	0.63±0.2	0.77±0.18
DCORA	0.71±0.18	0.95±0.32
JTORA	0.93±0.2	2.51±0.4
GRATO	3.26±0.37	11.8±2.5
Exhaustive	32.2±3.9	2 543±36

Table 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

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