本文選題：云計(jì)算 + 資源管理　； 參考：《合肥工業(yè)大學(xué)》2014年博士論文

【摘要】：云計(jì)算是一種基于互聯(lián)網(wǎng)的新型信息資源服務(wù)系統(tǒng)，可以為用戶提供包括基礎(chǔ)設(shè)施、平臺(tái)和應(yīng)用在內(nèi)的可定制彈性虛擬化資源服務(wù)。在技術(shù)進(jìn)步、需求引領(lǐng)和服務(wù)模式創(chuàng)新等因素的共同驅(qū)動(dòng)下，云計(jì)算得到了工業(yè)界和學(xué)術(shù)界的普遍認(rèn)可，已經(jīng)在現(xiàn)實(shí)生活中形成涵蓋移動(dòng)互聯(lián)網(wǎng)、物聯(lián)網(wǎng)等在內(nèi)的新型創(chuàng)意產(chǎn)業(yè)，并以其低成本和無(wú)處不在的應(yīng)用得到迅速發(fā)展，將從根本上改變?nèi)藗兩畹姆椒矫婷�。為了滿足這些多元化、海量的應(yīng)用資源需求，云計(jì)算必須擁有龐大的資源集群，這些資源在地理上是分布的，類型上是異構(gòu)的，并且在各自的管理域中又具有不同的資源管理策略和資源使用計(jì)價(jià)準(zhǔn)則。資源管理是云計(jì)算的核心問題之一，其目的是利用虛擬化技術(shù)屏蔽底層資源的異構(gòu)性和復(fù)雜性，使得海量分布式資源形成一個(gè)統(tǒng)一的巨型資源池，并在此基礎(chǔ)上，合理運(yùn)用相關(guān)資源管理方法和技術(shù)，確保資源的合理、高效的分配和使用。因此，如何實(shí)現(xiàn)對(duì)云計(jì)算資源的有效管理成為一個(gè)富有挑戰(zhàn)性的研究課題。本文從云計(jì)算基礎(chǔ)設(shè)施運(yùn)營(yíng)商和服務(wù)提供商的角度出發(fā)，將研究?jī)?nèi)容集中于云計(jì)算資源的優(yōu)化管理方向，主要包括資源的描述、組織、發(fā)現(xiàn)、匹配、配置和監(jiān)控等內(nèi)容，著重研究了云計(jì)算資源負(fù)荷的短期動(dòng)態(tài)預(yù)測(cè)方法，基于短期預(yù)測(cè)的云資源優(yōu)化配置方法，以及云資源需求中長(zhǎng)期組合預(yù)測(cè)方法。目的是通過對(duì)以上幾方面內(nèi)容的研究，使得云計(jì)算資源能夠得到有效地組織和合理的配置，在保證資源服務(wù)質(zhì)量的同時(shí)，降低數(shù)據(jù)中心能源消耗和運(yùn)營(yíng)成本，提升云計(jì)算基礎(chǔ)設(shè)施運(yùn)營(yíng)商和服務(wù)提供商的利潤(rùn)，實(shí)現(xiàn)綠色計(jì)算，為云計(jì)算的健康、持續(xù)發(fā)展提供理論參考。 基于以上論述，本文云計(jì)算環(huán)境下資源需求預(yù)測(cè)與優(yōu)化配置方法研究的主要內(nèi)容有：云計(jì)算環(huán)境下資源管理問題綜合研究；基于特征提取與分類的云資源負(fù)荷短期動(dòng)態(tài)預(yù)測(cè)方法研究；基于云計(jì)算負(fù)荷短期動(dòng)態(tài)預(yù)測(cè)的資源優(yōu)化配置方法研究，以及針對(duì)云計(jì)算基礎(chǔ)設(shè)施運(yùn)營(yíng)商和服務(wù)提供商中長(zhǎng)期資源總量規(guī)劃需求的云資源需求中長(zhǎng)期組合預(yù)測(cè)方法研究。 本文的具體研究?jī)?nèi)容和創(chuàng)新性工作主要有以下幾個(gè)方面： 首先，在總結(jié)了以往云計(jì)算資源描述格式和語(yǔ)言、發(fā)現(xiàn)架構(gòu)和技術(shù)，以及動(dòng)態(tài)組織、優(yōu)化分配和即時(shí)監(jiān)控等方面研究成果的基礎(chǔ)上，進(jìn)而闡述了云環(huán)境下資源管理所面臨和需要解決的新問題，并以此構(gòu)建了云環(huán)境下資源管理框架，給出了該框架在制造業(yè)背景下的應(yīng)用思路。 其次，分析了云計(jì)算資源需求負(fù)荷相對(duì)于先前的網(wǎng)格計(jì)算、分布式計(jì)算及其它高性能計(jì)算所表現(xiàn)出不同特點(diǎn)的基礎(chǔ)上，討論了短期負(fù)荷預(yù)測(cè)對(duì)于云計(jì)算實(shí)現(xiàn)資源實(shí)時(shí)控制、保持整個(gè)系統(tǒng)穩(wěn)定運(yùn)行、降低數(shù)據(jù)中心能耗和保障云服務(wù)的QoS所起的重要作用，構(gòu)建了基于資源負(fù)荷序列特征提取、分類和預(yù)測(cè)的多步驟預(yù)測(cè)方法。該方法運(yùn)用定長(zhǎng)重疊移動(dòng)滑窗技術(shù)從云計(jì)算資源負(fù)荷序列中提取子序列，再分別利用基于核模糊C聚類的監(jiān)督式聚類算法和基于隱形馬兒科夫鏈的非監(jiān)督式聚類算法對(duì)所提取的子序列進(jìn)行特征分類，在此基礎(chǔ)上，再使用基于遺傳算法優(yōu)化的Elman神經(jīng)網(wǎng)絡(luò)對(duì)云計(jì)算短期動(dòng)態(tài)資源負(fù)荷進(jìn)行預(yù)測(cè)，以此獲得優(yōu)良的預(yù)測(cè)效果。 接著，基于云計(jì)算短期負(fù)荷預(yù)測(cè)的結(jié)果，本文構(gòu)建基于負(fù)荷預(yù)測(cè)的云計(jì)算資源優(yōu)化配置框架，提出了一種基于資源監(jiān)控和負(fù)荷預(yù)測(cè)的資源配置自適應(yīng)彈性控制系統(tǒng)，實(shí)施主動(dòng)控制與被動(dòng)反應(yīng)相結(jié)合的混合彈性控制的資源配置策略以實(shí)現(xiàn)云計(jì)算資源的有效利用；進(jìn)一步地，，鑒于目前云計(jì)算服務(wù)提供商所采用的單虛擬機(jī)服務(wù)單用戶的資源管理模式所帶來(lái)的低資源利用率問題，本文構(gòu)建了一個(gè)具有五層結(jié)構(gòu)的新型公有云架構(gòu)，在該架構(gòu)的基礎(chǔ)上，提出了基于單虛擬機(jī)服務(wù)多用戶的虛擬化資源自適應(yīng)配置模式，該模式能針對(duì)不同用戶提出的應(yīng)用資源請(qǐng)求自動(dòng)搜尋最優(yōu)虛擬化資源，并在不影響服務(wù)質(zhì)量的基礎(chǔ)上，將不同的應(yīng)用運(yùn)行在同一臺(tái)虛擬機(jī)上，使得云計(jì)算提供商能在保證服務(wù)質(zhì)量的同時(shí)，提高云計(jì)算資源的利用效率，降低能耗。 最后，本文根據(jù)實(shí)際云計(jì)算資源管理中對(duì)資源負(fù)荷中長(zhǎng)期預(yù)測(cè)的需求，針對(duì)云計(jì)算中長(zhǎng)期負(fù)荷所表現(xiàn)出的兼具動(dòng)態(tài)性和周期性這一特點(diǎn)，構(gòu)建了基于廣義模糊軟集理論的云計(jì)算資源負(fù)荷組合預(yù)測(cè)模型，提出了新的基于夾角余弦的廣義模糊軟集相似性度量方法，將相似性度量結(jié)果與預(yù)測(cè)精度相結(jié)合來(lái)獲得各單項(xiàng)預(yù)測(cè)模型的權(quán)重，并針對(duì)云計(jì)算環(huán)境中資源負(fù)荷所表現(xiàn)出的短期動(dòng)態(tài)性和長(zhǎng)期周期性特征，選用自適應(yīng)神經(jīng)模糊推理系統(tǒng)ANFIS（Adaptive Neuro FuzzyInference System）和季節(jié)性ARIMA模型SARIMA作為單項(xiàng)預(yù)測(cè)模型來(lái)分別處理其動(dòng)態(tài)性和周期性特征，以此構(gòu)建基于廣義模糊軟集理論的云計(jì)算資源負(fù)荷組合預(yù)測(cè)模型GFSS-ANFIS/SARIMA。
[Abstract]:Cloud computing is a new information resource service system based on the Internet, which can provide customizable flexible virtual resource services, including infrastructure, platform and application. Under the common drive of technological progress, demand guidance and service mode innovation, cloud computing has been widely recognized in industry and academia. In real life, the new creative industries, including the mobile Internet, the Internet of things and so on, have been developed rapidly with its low cost and ubiquitous applications. It will fundamentally change all aspects of people's life. In order to meet these diversities, the massive resources need to be used, cloud computing must have a huge collection of resources. The resource management is one of the core problems of cloud computing. The purpose of the resource management is to shield the heterogeneity and complexity of the bottom resources by virtualization technology, so that the mass distribution is distributed. Type resources form a unified huge pool of resources, and on this basis, the rational use of related resources management methods and technologies to ensure the rational and efficient allocation and use of resources. Therefore, how to realize the effective management of cloud computing resources has become a challenging research topic. The research content concentrates on the optimization management direction of cloud computing resources, mainly including the description, organization, discovery, matching, configuration and monitoring of resources, focusing on the short-term dynamic forecasting method of cloud computing resource load, the method of cloud resource optimization based on short-term prediction, and the requirement of cloud resources. The aim of the medium and long term combination forecasting method is to make the cloud computing resources effectively organized and reasonably configured through the study of the above aspects, and to reduce the energy consumption and operation cost of the data center while guaranteeing the quality of the resources service, and improve the profits of the cloud computing infrastructure operators and service providers. Green computing provides a theoretical reference for the healthy and sustainable development of cloud computing.
Based on the above discussion, the main contents of the research on resource demand forecasting and optimal configuration under cloud computing environment are: comprehensive research on resource management in cloud computing environment; research on short-term dynamic prediction method of cloud resource load based on feature extraction and classification; resource optimization based on short-term dynamic forecasting of cloud computing load Method research and long-term combined forecasting method for cloud resource demand in cloud computing infrastructure operators and service providers.
The specific research contents and innovative work in this paper are as follows:
First, on the basis of summarizing the previous description format and language of cloud computing resources, and finding the results of architecture and technology, dynamic organization, optimal allocation and real-time monitoring, this paper expounds the new problems facing and needs to be solved in resource management under the cloud environment, and builds a framework of resource management under the cloud environment. The framework of the framework in the context of manufacturing applications.
Secondly, based on the different characteristics of the previous grid computing, distributed computing and other high performance computing, the demand load of the cloud computing resources is discussed, and the short-term load forecasting for the real-time control of the resources for the cloud computing, keeping the whole system running steadily, reducing the energy consumption of the data center and ensuring the QoS of the cloud service is discussed. The multi step prediction method based on resource load sequence feature extraction, classification and prediction is constructed. The method uses fixed length overlapping mobile sliding window technology to extract subsequences from cloud computing resource load sequence, and then uses supervised clustering algorithm based on Kernel Fuzzy C clustering and non supervision based on stealth horse paediatrics chain. On the basis of this, the Elman neural network based on genetic algorithm is used to predict the short-term dynamic resource load of cloud computing, so as to obtain good prediction results.
Then, based on the result of cloud computing short-term load forecasting, this paper constructs the framework of cloud computing resource optimization based on load forecasting, proposes a resource allocation adaptive elastic control system based on resource monitoring and load forecasting, and implements the resource allocation strategy of hybrid elastic control with active control and passive reaction. In this paper, a new public cloud architecture with five layers of structure is constructed in view of the low resource utilization problem brought by the single virtual machine service single user resource management model adopted by cloud computing service providers. On the basis of this architecture, the paper proposes a single virtual machine based on the single virtual machine. This model can automatically search for the optimal virtual resources for the application resources proposed by different users, and on the basis of the quality of service, the different applications run on the same virtual machine, so that the cloud computing provider can improve the quality of service while improving the quality of service. The efficiency of the use of cloud computing resources to reduce energy consumption.
Finally, according to the requirement of long term prediction in the management of resource load in the management of cloud computing, this paper constructs a cloud computing resource load combination forecasting model based on the generalized fuzzy soft set theory, and proposes a new broad sense cosine based on the characteristics of the long and long term load in the cloud computing. The fuzzy soft set similarity measure method combines the similarity measurement results with the prediction accuracy to obtain the weight of each single prediction model. The adaptive neural fuzzy inference system ANFIS (Adaptive Neuro FuzzyInference System) is selected for the short-term dynamic and long-term periodic characteristics of the resource load in the cloud computing environment. The seasonal ARIMA model SARIMA is used as a single prediction model to deal with its dynamic and periodic characteristics respectively, so as to construct the cloud computing resource load forecasting model GFSS-ANFIS/SARIMA. based on the generalized fuzzy soft set theory.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.07

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