基于可信服务的云资源智能优化与决策方法

doi:10.3969/j.issn.1671-1122.2023.02.005

摘要/Abstract

摘要：

随着云计算的应用与发展，云安全问题备受关注。云资源在受到恶意攻击时会导致无法匹配，合理分配云资源是云安全的前提。为了解决资源可信服务、资源配置优化和安全方案评估的问题，文章提出了基于可信服务的云资源安全智能优化与决策方法。首先，建立资源安全可信度、时间、成本和服务质量的多目标优化模型；然后，采用改进粒子群算法对其求解，并利用基于G1-改进熵权法主客观组合赋权法的VIKOR评估方法选取最佳云安全方案，同时为了克服粒子群早熟收敛，融合动态惯性权值及速度扰动策略改进算法；最后，仿真实验表明，改进算法相对其他算法的解集更广泛，收敛性更好，并验证了可信服务下评估方法的有效性，提高了云资源服务的安全性。

关键词: 云安全, 可信度, 粒子群, 主客观赋权, VIKOR

Abstract:

With the application and development of cloud computing, cloud security has attracted much attention. Cloud resources cannot be matched under malicious attacks. Rational allocation of cloud resources is a prerequisite for cloud security. In order to solve the problems of resource trusted service, resource allocation optimization and security scheme evaluation, this paper innovatively proposed intelligent optimization and decision method of cloud resource security based on trusted service. Firstly, a multi-objective optimization model of resource security reliability, time, cost and service quality was established. Then, the improved particle swarm optimization algorithm was used to solve it, and the VIKOR evaluation method based on G1-improved entropy weight method was used to select the optimal cloud security scheme. Meanwhile, in order to overcome the premature convergence of particle swarm, dynamic inertia weight and velocity perturbation strategy were integrated to improve the algorithm. Finally, simulation experiments show that the improved algorithm has a wider solution set and better convergence than other algorithms, and verifies the effectiveness of the evaluation method under trusted services, and improves the security of cloud resource services.

Key words: cloud security, credibility, particle swarm, subjective and objective empowerment, VIKOR

中图分类号:

TP309

王艳, 张坤鹏, 纪志成. 基于可信服务的云资源智能优化与决策方法[J]. 信息网络安全, 2023, 23(2): 35-44.

WANG Yan, ZHANG Kunpeng, JI Zhicheng. Intelligent Optimization and Decision Method of Cloud Resources Based on Trusted Service[J]. Netinfo Security, 2023, 23(2): 35-44.

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${{r}_{k}}$	说明
1.0	指标${{q}_{k-1}}$与指标${{q}_{k}}$同样重要
1.2	指标${{q}_{k-1}}$比指标${{q}_{k}}$稍微重要
1.4	指标${{q}_{k-1}}$比指标${{q}_{k}}$明显重要
1.6	指标${{q}_{k-1}}$比指标${{q}_{k}}$强烈重要
1.8	指标${{q}_{k-1}}$比指标${{q}_{k}}$极端重要

子任务	加工时间（h）/加工成本（元）/服务质量/可信度
子任务	${{R}_{1}}$	${{R}_{2}}$	${{R}_{3}}$	${{R}_{4}}$	${{R}_{5}}$	${{R}_{6}}$
${{O}_{1}}$	20/125/ 0.75/0.98	—	30/100/ 0.6/0.95	—	25/110/ 0.7/0.95	—
${{O}_{2}}$	—	35/100/ 0.7/0.95	—	20/120/ 0.7/0.97	—	30/130/ 0.8/0.95
${{O}_{3}}$	30/110/ 0.7/0.95	25/100/ 0.6/0.95	30/120/ 0.85/0.95	—	—	40/120/ 0.86/0.98
${{O}_{4}}$	—	30/90/ 0.5/0.96	—	60/120/ 0.9/0.96	50/100/ 0.7/0.93	—
${{O}_{5}}$	25/105/ 0.85/0.97	—	30/110/ 0.8/0.99	—	—	40/110/ 0.9/0.94

方案	成本/元	时间/h	服务质量	可信度
1	2020	360	13.40	0.74
2	1980	360	13.00	0.76
3	2060	360	14.00	0.71
4	2180	360	15.60	0.73
5	2100	360	14.60	0.74
6	2140	360	15.00	0.71
7	2180	320	16.20	0.78
8	2260	380	15.64	0.74

算法	成本/元	时间/s	服务质量	可信度	CPU
IMOPSO	2127.50	347.50	15.38	0.75	160.70
文献[18]	2175.50	360.50	14.50	0.73	168.60

任务	候选资源集
1	$\{{{R}_{1}},{{R}_{2}},{{R}_{3}},{{R}_{5}},{{R}_{6}}\}$
2	$\{{{R}_{2}},{{R}_{3}},{{R}_{4}},{{R}_{5}},{{R}_{6}}\}$
3	$\{{{R}_{1}},{{R}_{2}},{{R}_{4}},{{R}_{5}},{{R}_{6}}\}$
4	$\{{{R}_{1}},{{R}_{3}},{{R}_{4}},{{R}_{5}},{{R}_{6}}\}$