SaaS Multi-tenant Service Simulation Technology Based on ACO Algorithm and Variability Management

doi:10.12068/j.issn.1005-3026.2026.20250021

Abstract

Abstract:

In order to meet the specific business needs of different tenants， software as a service （SaaS） usually provides simulation customization functions. Through simulation customization， tenants can personalize SaaS according to their own business needs， so as to better meet their business needs. However， existing tenant customization services have the problems of failing to fully meet tenant requirements and having slow algorithm operation and response speeds. Therefore， an SaaS multi-tenant service simulation and customization technology based on an ant colony optimization （ACO） algorithm and variability traversal was proposed. The simulation optimization service deployment was achieved. By introducing a variability model， the adaptability and reusability of service customization and assembly were realized. The experimental results show that in the evaluation of SaaS tenant service resource utilization， the average values of the proposed technology are slightly higher than those of the comparison algorithm on instances a and b. The execution time fluctuates among different configuration schemes， with the shortest being 1 426 ms and the longest being 1 652 ms. The duty cycle of switching resources is relatively stable， with a fluctuation range between 1.12% and 1.51%. A lower duty cycle means that SaaS can more effectively utilize resources and reduce performance losses caused by resource switching at the same time. Based on the configuration schemes and running time data of different SaaS tenants， it is indicated that tenants can effectively derive service configuration schemes. The proposed technology can provide technical references for optimizing the simulation customization performance of SaaS.

Key words: ACO algorithm, cloud computing, SaaS, variability management, multi-tenant

CLC Number:

TP 311

Ying YIN, Yin-tong HUO, Ying-mei LIU. SaaS Multi-tenant Service Simulation Technology Based on ACO Algorithm and Variability Management[J]. Journal of Northeastern University(Natural Science), 2026, 47(1): 82-88.

Figures/Tables 4

References 20

[1]	李凌书，邬江兴，刘文彦.SaaS云环境下基于容器指纹匿名的网络欺骗方法［J］.信息安全学报， 2022，7（2）：72-86.
	Li Ling-shu， Wu Jiang-xing， Liu Wen-yan. An anonymous network deception method based on container fingerprint modification for SaaS applications［J］.Journal of Cyber Security， 2022，7（2）：72-86.
[2]	管婉青，张海君，路兆铭. 基于DRL的6G多租户网络切片智能资源分配算法［J］. 北京邮电大学学报， 2020， 43（6）： 132-139.
	Guan Wan-qing， Zhang Hai-jun， Lu Zhao-ming. Intelligent resource allocation algorithm for 6G multi-tenant network slicing based on deep reinforcement learning［J］. Journal of Beijing University of Posts and Telecommunications， 2020， 43（6）： 132-139.
[3]	Kuru K. Management of geo-distributed intelligence： deep insight as a service （DINSaaS） on forged cloud platforms （FCP）［J］. Journal of Parallel and Distributed Computing， 2021， 149： 103-118.
[4]	程华盛，敬超.面向多租户数据中心的联邦学习架构下通信开销优化方法［J］.计算机应用研究， 2024，41（9）：2823-2830.
	Cheng Hua-sheng， Jing Chao.Optimization method for communication overhead in federated learning architecture for multi-tenant data center［J］.Application Research of Computers， 2024，41（9）：2823-2830.
[5]	邓彭冲. 高职院校体育管理云信息系统设计与实现［J］. 自动化技术与应用， 2020， 39（12）： 47-50， 65.
	Deng Peng-chong. Design and implementation of sports management cloud information system in higher vocational colleges［J］. Techniques of Automation and Applications， 2020， 39（12）： 47-50， 65.
[6]	张纪林，邵玉曹，任永坚，等. 支持多租户模式的业务流程动态定制模型［J］. 计算机科学， 2022， 49（）： 705-713.
	Zhang Ji-lin， Shao Yu-cao， Ren Yong-jian， et al. Dynamic customization model of business processes supporting multi-tenant［J］. Computer Science， 2022， 49（sup1）： 705-713.
[7]	王廷，刘刚. 支持网络切片和绿色通信的软件定义虚拟化接入网［J］. 计算机研究与发展， 2021， 58（6）： 1291-1306.
	Wang Ting， Liu Gang. Software defined virtualized access network supporting network slicing and green communication［J］. Journal of Computer Research and Development， 2021， 58（6）： 1291-1306.
[8]	Ke C B， Xiao F， Huang Z Q， et al. A user requirements-oriented privacy policy self-adaption scheme in cloud computing［J］. Frontiers of Computer Science， 2022， 17（2）： 172203.
[9]	徐海洋，刘海龙，陈先，等. 基于组合负载预测模型的多租户数据库弹性伸缩方法［J］. 软件学报， 2025， 36（3）： 981-994.
	Xu Hai-yang， Liu Hai-long， Chen Xian， et al. Elastic scaling method for multi-tenant databases based on hybrid workload prediction model［J］. Journal of Software， 2025， 36（3）： 981-994.
[10]	冉金鹏，王翔，赵尚弘，等. 基于果蝇优化的虚拟SDN网络映射算法［J］. 信息网络安全， 2020， 20（6）： 65-74.
	Ran Jin-peng， Wang Xiang， Zhao Shang-hong， et al. Virtual SDN network embedding algorithm based on fruit fly optimization［J］. Netinfo Security， 2020， 20（6）： 65-74.
[11]	文宇鸿，周游，吴秋霖，等. 多租户固态盘服务质量保障技术综述［J］. 计算机研究与发展， 2023， 60（3）： 555-571.
	Wen Yu-hong， Zhou You， Wu Qiu-lin， et al. Quality of service guaranty technology of multi-tenant solid-state drives： a survey［J］. Journal of Computer Research and Development， 2023， 60（3）： 555-571.
[12]	李妍，郭得科，曹晓丰，等. 应用感知的数据中心网络多租户共享方法［J］. 计算机学报， 2021， 44（7）： 1363-1377.
	Li Yan， Guo De-ke， Cao Xiao-feng， et al. Application-aware network sharing for multi-tenant data center［J］. Chinese Journal of Computers， 2021， 44（7）： 1363-1377.
[13]	Ortegat G， Grolaux D， Riviere E， et al. Engineering the transition of interactive collaborative software from cloud computing to edge computing［J］. Proceedings of the ACM on Human-Computer Interaction， 2022， 6： 1-31.
[14]	刘海龙，王硕，侯舒峰，等. 云多租数据库资源规划调度技术综述［J］. 软件学报， 2025， 36（1）： 446-468.
	Liu Hai-long， Wang Shuo， Hou Shu-feng， et al. Survey on resource planning and scheduling technologies for multi-tenant cloud databases［J］. Journal of Software， 2025， 36（1）： 446-468.
[15]	朱斌庚，陈晓曼. 基于云计算SaaS的电能质量在线监测平台设计和开发［J］. 自动化与仪器仪表， 2023（8）： 49-51， 55.
	Zhu Bin-geng， Chen Xiao-man. Design and development of power quality online monitoring platform based on cloud computing SaaS［J］. Automation & Instrumentation， 2023（8）： 49-51， 55.
[16]	胡宇翔，冯旭，董永吉，等.基于深度强化学习的多租户算网资源分配算法［J］.物联网学报， 2024， 8（4）：34-44.
	Hu Yu-xiang， Feng Xu， Dong Yong-ji， et al. Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning［J］.Chinese Journal on Internet of Things， 2024，8（4）：34-44.
[17]	黄锦灏，丁钰真，肖亮，等. 一种基于强化学习的嵌入式系统抗拒绝服务攻击的缓存调度方案［J］. 计算机科学， 2020， 47（7）： 282-286.
	Huang Jin-hao， Ding Yu-zhen， Xiao Liang， et al. Reinforcement learning based cache scheduling against denial-of-service attacks in embedded systems［J］. Computer Science， 2020， 47（7）： 282-286.
[18]	欧东阳，张开强，陈圣蕾，等. 基于PCPEC的数据中心功耗攻击防御策略［J］. 计算机科学， 2022， 49（12）： 374-380.
	Dong-yang Ou， Zhang Kai-qiang， Chen Sheng-lei， et al. Data center power attack defense strategy based on PCPEC［J］. Computer Science， 2022， 49（12）： 374-380.
[19]	周知，刘方明. 面向多租户数据中心资源回收利用的能效激励机制［J］. 中国科学：（信息科学）， 2021， 51（5）： 735-749.
	Zhou Zhi， Liu Fang-ming. An energy-efficient incentive mechanism for resource recycling in multi-tenant datacenters［J］. Scientia Sinica （Informationis）， 2021， 51（5）： 735-749.
[20]	杜楠，彭文成，谭亚新. 基于SAAS的LVC装备试验资源实时调度模型研究［J］. 系统工程与电子技术， 2023， 45（12）： 3924-3931.
	Du Nan， Peng Wen-cheng， Tan Ya-xin. Research on real-time scheduling model of LVC equipment test resources based on SAAS［J］. Systems Engineering and Electronics， 2023， 45（12）： 3924-3931.

租户	配置维度								运行时间/ms
租户	1	2	3	4	5	6	7	8	运行时间/ms
User0	a1	a2	a3	a3	a2	a1	a3	a3	772
User1	a1	a3	a1	a3	a1	a1	a1	a2	685
User2	a3	a3	a1	a2	a3	a1	a1	a3	635
User3	a1	a1	a2	a1	a1	a2	a1	a3	616
User4	a1	a2	a3	a1	a2	a3	a2	a3	684
User5	a2	a1	a2	a1	a3	a3	a2	a3	951
User6	a3	a2	a1	a3	a3	a3	a3	a3	547
User7	a2	a1	a3	a1	a2	a1	a3	a2	512
User8	a2	a3	a3	a1	a1	a1	a3	a2	543
User9	a3	a2	a2	a3	a3	a1	a1	a1	721

租户	配置维度								运行时间/ms
租户	1	2	3	4	5	6	7	8	运行时间/ms
User0	a1	a2	a3	a3	a2	a1	a3	a3	772
User1	a1	a3	a1	a3	a1	a1	a1	a2	685
User2	a3	a3	a1	a2	a3	a1	a1	a3	635
User3	a1	a1	a2	a1	a1	a2	a1	a3	616
User4	a1	a2	a3	a1	a2	a3	a2	a3	684
User5	a2	a1	a2	a1	a3	a3	a2	a3	951
User6	a3	a2	a1	a3	a3	a3	a3	a3	547
User7	a2	a1	a3	a1	a2	a1	a3	a2	512
User8	a2	a3	a3	a1	a1	a1	a3	a2	543
User9	a3	a2	a2	a3	a3	a1	a1	a1	721