本文選題：云計算 + 信任模型��； 參考：《合肥工業(yè)大學》2014年博士論文

【摘要】：云計算作為一種新興的信息服務模式以及大規(guī)模數(shù)據(jù)存儲和處理方式,正在為互聯(lián)網(wǎng)時代服務計算帶來巨大而深刻的變革,使得海量計算資源、存儲資源、軟件資源等通過互聯(lián)網(wǎng)平臺向外按需定制化提供,用戶使用各種網(wǎng)絡服務也變得更加方便而高效。然而,由于本身云計算環(huán)境具有巨大的開放性和復雜性,同時具有資源動態(tài)變化、自治性強、注重安全性等特征,用戶在選擇使用層出不窮的云服務時面臨著各種安全、隱私等風險,同時受到各大云計算發(fā)起者爆出的各種安全事故的影響,逐漸引發(fā)了用戶對云計算的信任危機,也阻礙了云計算的進一步普及和發(fā)展。在云計算面臨各種信任問題的背景下,本文的研究目標是建立云計算信任管理機制,構(gòu)建能夠適應云計算環(huán)境與特點的信任模型,在開放和動態(tài)的云計算環(huán)境下對云服務信任度進行有效的管理和評估,從而降低用戶選擇云服務的風險。本文具體的研究內(nèi)容包括： (1)提出了一種雙層雙視角的云服務信任評估模型——基于客觀信任和主觀信任的云服務信任評估模型,并且同時從局部和全局角度分別對云計算服務的信任度進行綜合動態(tài)評估。在分布式信任服務提供商(TSP)的信任管理框架的基礎上,從云服務的服務水平協(xié)議(SLA)記錄信息和用戶反饋信息兩種角度,對云服務的局部主觀信任(LST)、局部客觀信任(LOT)、全局主觀信任(GST)以及全局客觀信任(GOT)進行評估,其中LST和LOT分別反映了從某云服務用戶的單一視角對云服務提供商的主觀信任及客觀信任度,GST和GOT則反映了從全體用戶視角對云服務提供商的主觀信任及客觀信任度。此外,對于多云環(huán)境,TSP之間需共享多云服務提供商在不同云中的信任信息,通過構(gòu)建TSP之間的信任傳播網(wǎng)絡,從而能夠?qū)缭骗h(huán)境下的云服務提供商可信度進行評估。通過仿真實驗,表明我們提出的信任管理框架和評估方法在識別可信和不可信云服務提供商上是有效且健壯的。 (2)提出了一種云計算信任評估的三層信任屬性框架,分別從軟(硬)件等基礎設施信任、平臺及服務提供商的管理和技術服務信任以及用戶服務交互提供信任上分析、提煉影響云服務信任度的各個信任屬性。并且在此框架的基礎上,提出了一種基于不同實體及不同視角差距模型的云服務可信評估方法。從具有相關專業(yè)經(jīng)驗的專家視角(即可信第三方)和具有個性化信任需求的用戶視角考慮,分別提煉出云服務信任屬性的實際提供性能(Delivery Performance)、用戶感受性能(Perception Performance)以及重要性(Importance Performance)三者之間的差距,再基于差距模型從以上不同視角對云服務信任度進行了綜合考量,得到云服務信任度的差距評估結(jié)果。該方法本質(zhì)上是一種新穎的多屬性決策模型,能夠用來衡量用戶與云服務提供者、可信第三方與云服務提供者以及用戶與可信第三方之間對相同云服務的信任評估差異,從而找出影響云服務可信度的關鍵且薄弱屬性,使得云服務提供商能夠更加高效的提升服務質(zhì)量,提高可信度。 (3)提出了一種新穎且有效的基于多屬性信任評估的個性化云服務選擇機制。云服務信任值的評估來自兩個重要方面：基于感知的信任和基于信譽的信任。對于云服務用戶來說，，需在使用服務之后向云服務系統(tǒng)反饋其服務交互的多屬性評估結(jié)果,并存儲在信任值數(shù)據(jù)庫和信譽值數(shù)據(jù)庫中,分別作為直接信任證據(jù)以供未來再次使用或提供給其他用戶作為間接信任證據(jù)。在抽取了以上多屬性的直接或間接信任證據(jù)之后,將基于感知的信任值和基于信譽的信任值聚合之后得到云服務信任值的最終結(jié)果。其中,基于信譽的信任值是在服務信譽值的基礎上疊加一個從信譽值到信任值的映射函數(shù)而獲取,該函數(shù)反應出用戶對服務信譽的個性化偏好、偏見、信念等。通過這樣一種基于個性化信任評估的云服務選擇機制,本文提出的方法在云服務系統(tǒng)中能夠有效地為用戶選擇符合其個性化信任需求的云服務。 (4)提出了一種基于反饋評價過濾機制的信任感知云服務推薦方法,首先將云服務的屬性特征和用戶需求模型中的各項需求偏好進行匹配,產(chǎn)生備選云服務。在信任構(gòu)建平臺所提供的信任反饋機制的基礎上,通過結(jié)合反饋評分一致性和用戶服務熟悉度兩方面因素,對云服務不公平信任反饋評分進行了過濾。首先,反饋評分一致性原則是從反饋評分的內(nèi)部規(guī)律出發(fā),過濾掉偏離全體信任反饋評分平均水平較大的一部分評分；而用戶服務熟悉度則是根據(jù)反饋評價者對于云服務的使用及反饋行為等外部規(guī)律,結(jié)合用戶交互頻率、服務使用時間、反饋提交時間等參數(shù)過濾。最終,結(jié)合內(nèi)外兩方面因素綜合過濾不公平的反饋評價。
[Abstract]:As a new information service mode and a large data storage and processing mode, cloud computing is bringing huge and profound changes to the service computing in the Internet age. It makes the mass computing resources, storage resources, software resources and so on through the Internet platform to provide, and users use various network services as well. It is more convenient and efficient. However, because the cloud computing environment has huge openness and complexity, and has the characteristics of dynamic change of resources, strong autonomy and security, users face various security, privacy and other risks when choosing an endless cloud service. At the same time, they are exposed to various big cloud computing originators. In the background of cloud computing facing various trust problems, the aim of this paper is to establish cloud computing trust management mechanism, build trust model that can adapt to the environment and characteristics of cloud computing, and open and open. In the dynamic cloud computing environment, the trust of cloud services is managed and evaluated effectively, thus reducing the risk of user selection of cloud services.
(1) a double double perspective cloud service trust evaluation model, a cloud service trust evaluation model based on objective trust and subjective trust, is proposed, and the trust degree of cloud computing service is evaluated comprehensively and dynamically from both local and global perspectives. The foundation of trust management framework for distributed trust service provider (TSP) is based on the model of trust evaluation of cloud services. From two angles of service level protocol (SLA) record information and user feedback information, the local subjective trust (LST), local objective trust (LOT), global subjective trust (GST) and global objective trust (GOT) are evaluated, in which LST and LOT reflect the cloud service from a single perspective of a cloud service user. The subjective trust and objective trust of the provider, GST and GOT reflect the subjective and objective trust of the cloud service provider from the perspective of all users. In addition, for the multi cloud environment, the TSP needs to share the trust information of the multi cloud service provider in different clouds, and by building the trust communication network between the TSP, thus it can be able to cross the cloud. The credibility of the cloud service provider in the environment is evaluated. Through simulation experiments, it shows that our proposed trust management framework and evaluation methods are effective and robust in identifying trusted and untrusted cloud service providers.
(2) a three layer trust attribute framework for cloud computing trust evaluation is proposed. It provides trust analysis from soft (hard) components such as infrastructure trust, platform and service provider management, technical service trust and user service interaction, and extracts trust attributes that affect the trust degree of cloud services. A cloud service trustworthiness evaluation method based on different entity and different perspective gap model is proposed. The actual performance (Delivery Performance) of cloud service trust property (Delivery) and user perception performance (P) are extracted from the expert perspective of the relevant professional experience (that is, the letter of the letter) and the user's perspective of the personalized trust requirement. Erception Performance) and the gap between the importance (Importance Performance) three, and then based on the gap model to evaluate the cloud service trust from the above different perspectives, and get the result of the gap assessment of the cloud service trust. This method is essentially a new multi attribute decision model, which can be used to measure the user and the user. Cloud service providers, trusted third parties and cloud service providers, and trust assessment differences between users and trusted third parties to the same cloud services, thus identify the key and weak attributes that affect cloud service credibility, making the cloud service provider more efficient in improving the quality of service and improving credibility.
(3) a novel and effective personalized cloud service selection mechanism based on multi attribute trust evaluation is proposed. The evaluation of trust value of cloud services comes from two important aspects: perceived trust and reputation based trust. For cloud service users, the Xiang Yun service system needs to feed back the multiple genera of its service interaction after the service is used. The results of the assessment are stored in the trust value database and the reputation database as direct trust evidence for the future use again or provided to other users as indirect trust evidence. After extracting the direct or indirect trust evidence of the above multiple attributes, the perceived trust value and credit based trust value are aggregated. Then we get the final result of the trust value of the cloud service. Among them, the reputation based trust value is obtained on the basis of the service reputation value, which is superposed by a mapping function from the reputation value to the trust value. This function reflects the user's personalized preference, prejudice, belief and so on. The service selection mechanism proposed in this paper can effectively select cloud services that meet their personalized trust requirements in cloud service system.
(4) a trust aware cloud service recommendation method based on feedback evaluation and filtering mechanism is proposed. First, it matches the attribute features of the cloud service and the requirements preference of the user requirement model to generate alternative cloud services. On the basis of the trust feedback machine system provided by the trust construction platform, the consistency and consistency of the feedback score are combined with the feedback. In the two aspects of customer service familiarity, we filter the unfair trust feedback score of the cloud service. First, the principle of feedback score consistency is based on the internal rules of the feedback score, filtering out a part of the score that deviates from the average level of the whole trust feedback score, and the familiarity of the user service is based on the feedback evaluator. The use of cloud services and feedback behavior, such as the external rules, combined with user interaction frequency, service time, feedback time and other parameters filtering. Finally, combined with two aspects of internal and external factors to filter unfair feedback evaluation.
【學位授予單位】：合肥工業(yè)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TP393.08

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