本文選題：云計算 + 多實例��； 參考：《武漢理工大學》2013年碩士論文

【摘要】：云計算是一種虛擬化的、可伸縮的IT服務,它可以按照用戶的需求動態(tài)地提供服務。隨著各云平臺用戶需求的不斷增加,其資源負荷量也有了較大的變化,如何準確預測客戶需求并合理分配云資源成為各平臺供應商面臨的挑戰(zhàn)。因此,各種云資源的市場交易平臺應運而生,為用戶及時、便捷的獲取云服務提供了可靠保障。 然而,云計算資源目前出現了多種購買模式,如亞馬遜彈性云平臺的3種類型的實例計費方案,分別為：按需運行實例、保留定制實例和現貨競價實例。前兩類實例的價格一定,但是,關于云供應商如何針對現貨競價實例進行定價及定價趨勢的問題,目前的研究結果尚未十分明朗,另一方面,用戶如何更好地從云計算環(huán)境中獲得決策過程所需的決策信息、如何從海量決策信息中處理動態(tài)的用戶需求信息,都會影響用戶最終的購買決策效率。因此研究云計算多實例的市場交易和購買決策十分必要。 云計算多實例的市場交易和購買決策面臨的主要挑戰(zhàn)有：(1)云供應商如何針對現貨競價實例定價及其價格變動的趨勢研究只考慮了定性的影響趨勢,無法量化；(2)用戶對于自身需求信息無法進行全面的處理和分析；(3)在購買決策過程中,只考慮了服務質量這一單一因素,使得服務呈現單一化的趨勢,無法滿足用戶多方面的需求。 基于以上問題,本文提出了基于云聯(lián)盟的云計算市場交易體系,為供需雙方的交易提供架構支撐。然后構建云計算市場交易預測模型,包括現貨競價實例價格預測模型和客戶需求預測模型。最后研究了基于客戶的多實例組合購買決策,以達到滿足用戶多樣化需求的目標。具體的工作包括： (1)提出了基于云計算的市場交易體系； (2)設計了云計算市場現貨實例的價格預測模型和算法,充分挖掘云供應商現貨實例的定價規(guī)律,為云用戶的投標決策提供依據； (3)設計了云計算市場客戶需求預測模型,并采用灰色BP神經網絡進行仿真訓練,改進了傳統(tǒng)BP神經網絡的缺點,使得預測值更為精確,從而充分挖掘云用戶自身的需求,使得用戶更好的了解自身的需求規(guī)律,優(yōu)化購買決策； (4)構建了基于客戶的云計算市場多實例組合購買決策模型,首先從成本優(yōu)化和服務時間最小入手,結合不同實例類型的特點,構建單目標約束模型,然后綜合考慮客戶各方面的需求,構建雙目標約束模型,同時達到客戶成本最小化和服務時間最短的目標。
[Abstract]:Cloud computing is a virtualized, scalable IT service that provides services dynamically according to user needs. With the increasing demand of cloud platform users, the amount of resource load has also changed greatly. How to accurately predict customer demand and allocate cloud resources rationally becomes a challenge to each platform provider. Therefore, various cloud resources market trading platform emerged as the times require, providing a reliable guarantee for users to obtain cloud services in a timely and convenient manner. However, there are many purchase modes for cloud computing resources at present, such as three types of instance billing schemes of Amazon elastic cloud platform, which are: running on demand instance, retaining customization instance and spot bidding instance. The price of the first two types of examples is certain. However, the current research results on how cloud suppliers carry out pricing and pricing trends for spot bidding examples are not very clear. On the other hand, How to obtain the decision information from the cloud computing environment and how to deal with the dynamic user demand information from the massive decision information will affect the efficiency of the final purchase decision. Therefore, it is necessary to study the market transaction and purchase decision of cloud computing multi-instance. The main challenges of cloud computing multi-instance market transaction and purchase decision are: (1) cloud suppliers' research on how to price spot bidding cases and the trend of price change only consider qualitative influence trends, but can not be quantified; 2) in the process of purchasing decision, the single factor of quality of service is considered only, which makes the service present a single trend and can not meet the needs of users in many aspects. 2) the users can not handle and analyze the information of their own needs comprehensively. 3) in the process of purchasing decision, only the single factor of service quality is considered, which makes the service present a single trend. Based on the above problems, this paper proposes a cloud computing market transaction system based on cloud alliance, which provides the framework support for the transaction between supply and demand. Then the transaction forecasting model of cloud computing market is constructed, including spot bidding example price forecasting model and customer demand forecasting model. Finally, the multi-instance purchasing decision based on customers is studied to meet the needs of customers. The specific work includes: 1) putting forward the market transaction system based on cloud computing; (2) designing the price forecasting model and algorithm of cloud computing market spot case, fully mining the pricing law of cloud supplier spot case. This paper provides the basis for cloud users' bidding decision. (3) A cloud computing market customer demand prediction model is designed, and the grey BP neural network is used for simulation training, which improves the shortcomings of the traditional BP neural network. Make the forecast value more accurate, thus fully mining the needs of cloud users, make users better understand their own demand law, optimize the purchase decision; In this paper, we construct a multi-instance combined purchase decision model based on customers in cloud computing market. Firstly, we construct a single-objective constraint model based on cost optimization and minimum service time, combined with the characteristics of different case types. Then, considering the needs of all aspects of customers, a two-objective constraint model is constructed, and the goal of minimizing customer cost and minimum service time is achieved at the same time.
【學位授予單位】：武漢理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F49

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