摘要
软件即服务(softuare as a service,SaaS)是一种让用户通过支付订阅费来获得软件访问权的云服务模式。由于其业务的多样性,用户对不同软件的在线访问率存在很大差异,所以不同软件所消耗的云计算资源也存在差异。为避免违反服务等级协议(service level agreement,SLA)而产生违约赔付的风险,SaaS运营商不仅要优化各种软件的计算资源配置,还要对各类软件的订阅量加以限额。在考虑SLA限制的基础上,构建了一个以收益最大化为目标的有资源约束的非线性整数规划模型。由于模型计算的复杂性,其无法在多项式时间内求解,所以设计了基于Q学习-粒子群(particle swarm optimizoction,PSO)的融合算法来求解该NP难题。该算法将Q-学习嵌入到PSO中,动态调整PSO参数,从而避免直接使用PSO时会面临的局部最优陷阱和计算效率低下的问题。仿真实验验证了在不同场景下模型及算法的有效性,结果表明该算法可在云计算资源有限的条件下,以较高的求解效率获得收益更高的订阅限额及资源配置方案。其中,当处于需求波动大的情境下时,运营商应尽可能地降低软件的资源争用比,通过配置足量的虚拟机资源并设定严格的订阅限额来保障软件的服务质量,减少违约赔付成本;相反,当处于需求波动小的情境下时,运营商可以提高软件的资源争用比,通过放宽订阅限额来抢占更大的市场,实现收益最大化。
SaaS is a cloud service model where users obtain software access rights by paying subscription fees.Due to the diversity 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 applications 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 local optima and low computational efficiency associated with direct PSO application.Simulation experiments validate the effectiveness of the model and algorithm in different scenarios.The results indicate that the algorithm can achieve higher revenue for subscription 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.
作者
金晶
程岩
彭慧洁
Jin Jing;Cheng Yan;Peng Huijie(School of Business,East China University of Science&Technology,Shanghai 200237,China)
出处
《计算机应用研究》
CSCD
北大核心
2024年第7期2069-2078,共10页
Application Research of Computers
基金
国家自然科学基金资助项目(71271087)。
关键词
软件即服务
订阅限额
资源配置
粒子群优化
Q-学习
software as a service
subscription limit
resource allocation
particle swarm optimization
Q-learning
