计算机应用研究2024，Vol.41Issue(7)：2069-2078,10.DOI:10.19734/j.issn.1001-3695.2023.10.0542
面向在线率差异的SaaS订阅限额及资源配置组合优化
Combination optimization of SaaS subscription limits and resource allocation considering online disparity
摘要
Abstract
SaaS is a cloud service model where users obtain software access rights by paying subscription fees.Due to the diver-sity of business operations,users exhibit significant variations in the online access rates for different software.Consequently,there are variations in the cloud computing resources consumed by different software applications.To avoid the risk of violating SLAs and incurring penalty payments,SaaS operators optimize the computational resource allocation for various software applica-tions and impose subscription limits on each category of software.Considering SLA constraints,this paper formulated a resource-constrained nonlinear integer programming model with the objective of maximizing revenue.Due to the computational complexity of the model,it cannot be solved in polynomial time,and this paper proposed a Q-learning-PSO hybrid algorithm for this NP-hand problem.This algorithm embedded Q-learning into PSO to dynamically adjust PSO parameters,thereby avoiding the issues of lo-cal optima and low computational efficiency associated with direct PSO application.Simulation experiments validate the effective-ness of the model and algorithm in different scenarios.The results indicate that the algorithm can achieve higher revenue for sub-scription limits and resource allocation with superior solving efficiency under the condition of limited cloud computing resources.Specifically,in scenarios with significant demand fluctuations,operators should aim to reduce the resource contention ratio of software.This can be achieved by provisioning an ample amount of virtual machine resources and enforcing strict subscription limits to ensure the quality of service,consequently reducing penalty payments.Conversely,in scenarios with minimal demand fluctuations,operators have the flexibility to increase the resource contention ratio of software.By relaxing subscription limits,they can seize a larger market share,thus realizing revenue maximization.关键词
软件即服务/订阅限额/资源配置/粒子群优化/Q-学习Key words
software as a service/subscription limit/resource allocation/particle swarm optimization/Q-learning分类
信息技术与安全科学引用本文复制引用
金晶,程岩,彭慧洁..面向在线率差异的SaaS订阅限额及资源配置组合优化[J].计算机应用研究,2024,41(7):2069-2078,10.基金项目
国家自然科学基金资助项目(71271087) （71271087）
