本文選題：軟件作為服務(wù) + 多租戶�。� 參考：《山東大學(xué)》2014年碩士論文

【摘要】：云計(jì)算是能夠提供方便、按需網(wǎng)絡(luò)來訪問可配置計(jì)算資源的共享池的模型,它可以用最少的管理工作或者服務(wù)提供商的交互來快速配置和發(fā)布資源。軟件即服務(wù)(Software as a Service,SaaS)作為云服務(wù)的一種模式,用戶在本地不需要安裝任何軟件,可以按需向服務(wù)提供商進(jìn)行定制,已經(jīng)受到了廣泛的認(rèn)可。然而,對于SaaS而言,用戶的信息是以遠(yuǎn)程的方式存儲在SaaS服務(wù)提供商端,用戶自身無法確保其隱私信息的安全性,這就突出了SaaS模式下隱私保護(hù)的重要性,給SaaS服務(wù)提供商提出的重大的挑戰(zhàn)。與此同時(shí),SaaS服務(wù)提供商需要提供部分資源來滿足對租戶信息的隱私保護(hù)。租戶也會提出各自的隱私保護(hù)需求,其中包括隱私保護(hù)性能、隱私保護(hù)水平等,SaaS服務(wù)提供商需要盡可能的滿足租戶的需求,同時(shí)最大化自身的收益。 針對上述問題,本文首先從SaaS服務(wù)提供商的角度出發(fā),結(jié)合SaaS多租戶的特點(diǎn),統(tǒng)籌考慮多租戶提交的隱私保護(hù)需求和SaaS服務(wù)提供商現(xiàn)有資源數(shù)量,盡可能的提高SaaS服務(wù)提供商的收益。文章基于多目標(biāo)優(yōu)化理論來解決SaaS服務(wù)提供商與多租戶隱私保護(hù)策略制定問題,構(gòu)建了SaaS服務(wù)提供商隱私保護(hù)策略制定模型,圍繞模型將隱私保護(hù)策略制定規(guī)約到多目標(biāo)優(yōu)化問題的求解,采用遺傳算法解決規(guī)約成的多目標(biāo)優(yōu)化問題,得到最終的隱私保護(hù)策略。 對于多租戶而言,他們希望盡可能的降低自己的花費(fèi),同時(shí)達(dá)到比較高的隱私保護(hù)性能；對于SaaS服務(wù)提供商而言,他希望在滿足租戶隱私保護(hù)需求的情況下,盡可能多的提高自己的收益。然后,高的隱私保護(hù)性能必然會增加租戶的花費(fèi),同時(shí)也會增加SaaS服務(wù)提供商的資源開銷。針對這一問題,為平衡SaaS服務(wù)提供商與多租戶之間的利益關(guān)系,本文致力于建立與隱私保護(hù)性能,隱私保護(hù)等級關(guān)聯(lián)的多租戶和SaaS服務(wù)提供商的收益函數(shù),根據(jù)納什均衡博弈理論,將多租戶和SaaS服務(wù)提供商確定隱私保護(hù)策略的過程規(guī)約為租戶與SaaS進(jìn)行博弈的過程。將租戶的隱私保護(hù)約束信息作為均衡博弈的約束條件,建立納什均衡博弈模型,通過對博弈模型的分析求解,最終得到適合多租戶和SaaS服務(wù)提供商的隱私保護(hù)策略、租賃費(fèi)用以及SaaS服務(wù)提供商總的收益。實(shí)驗(yàn)表明,本文提出的隱私保護(hù)博弈模型具有較好的可行性、有效性。
[Abstract]:Cloud computing is a model that provides convenient, on-demand networks to access shared pools of configurable computing resources. It can quickly configure and distribute resources with minimal administrative effort or service provider interaction. As a model of cloud services, users do not need to install any software locally and can be customized to service providers on demand, which has been widely accepted. However, for SaaS, the user's information is stored in a remote way in the SaaS service provider, the user itself can not ensure the security of their privacy information, which highlights the importance of privacy protection in SaaS mode. Major challenges for SaaS service providers. At the same time SaaS service providers need to provide some resources to meet the privacy protection of tenant information. Tenants will also put forward their privacy protection requirements, including privacy protection performance, privacy protection level and other SaaS service providers need to meet the needs of tenants as much as possible, while maximizing their own revenue. In view of the above problems, this paper firstly considers the privacy protection requirements submitted by multi-tenants and the number of existing resources of SaaS service providers from the point of view of SaaS service providers and the characteristics of SaaS multi-tenants. Increase SaaS service provider's revenue as much as possible. Based on the theory of multi-objective optimization, this paper solves the problem of SaaS service provider and multi-tenant privacy policy formulation, and constructs a model of SaaS service provider privacy policy formulation. According to the model, the privacy protection strategy is formulated to solve the multi-objective optimization problem, and the genetic algorithm is used to solve the multi-objective optimization problem, and the final privacy protection strategy is obtained. For multi-tenants, they want to keep their costs as low as possible while achieving higher privacy protection performance; for SaaS service providers, he wants to meet tenants' privacy needs. Increase your earnings as much as you can. Then, the high privacy protection performance will inevitably increase the tenant's expense, also will increase the SaaS service provider's resource overhead. In order to balance the benefit relationship between SaaS service provider and multi-tenant, this paper aims to establish the revenue function of multi-tenant and SaaS service provider associated with privacy protection performance and privacy protection level. According to Nash equilibrium game theory, the process of multi-tenant and SaaS service provider determining privacy protection policy is defined as the game process between tenant and SaaS. Taking the privacy protection constraint information of tenants as the constraint condition of equilibrium game, the Nash equilibrium game model is established. Through the analysis and solution of the game model, the privacy protection strategy suitable for multi-tenant and SaaS service provider is obtained. Rental fee and SaaS service provider's total revenue. Experiments show that the proposed game model is feasible and effective.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP309;TP393.09

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本文編號：1862925