本文選題：云計算 + 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)進步、需求引領(lǐng)和服務(wù)模式創(chuàng)新等因素的共同驅(qū)動下，云計算得到了工業(yè)界和學(xué)術(shù)界的普遍認可，已經(jīng)在現(xiàn)實生活中形成涵蓋移動互聯(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畹姆椒矫婷妗榱藵M足這些多元化、海量的應(yīng)用資源需求，云計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價準則。資源管理是云計算的核心問題之一，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統(tǒng)一的巨型資源池，并在此基礎(chǔ)上，合理運用相關(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，如何實現(xiàn)對云計算資源的有效管理成為一個富有挑戰(zhàn)性的研究課題。本文從云計算基礎(chǔ)設(shè)施運營商和服務(wù)提供商的角度出發(fā)，將研究內(nèi)容集中于云計算資源的優(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，著重研究了云計算資源負荷的短期動態(tài)預(yù)測方法，基于短期預(yù)測的云資源優(yōu)化配置方法，以及云資源需求中長期組合預(yù)測方法。目的是通過對以上幾方面內(nèi)容的研究，使得云計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，降低數(shù)據(jù)中心能源消耗和運營成本，提升云計算基礎(chǔ)設(shè)施運營商和服務(wù)提供商的利潤，實現(xiàn)綠色計算，為云計算的健康、持續(xù)發(fā)展提供理論參考。 基于以上論述，本文云計算環(huán)境下資源需求預(yù)測與優(yōu)化配置方法研究的主要內(nèi)容有：云計算環(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荷短期動態(tài)預(yù)測方法研究；基于云計算負荷短期動態(tài)預(yù)測的資源優(yōu)化配置方法研究，以及針對云計算基礎(chǔ)設(shè)施運營商和服務(wù)提供商中長期資源總量規(guī)劃需求的云資源需求中長期組合預(yù)測方法研究。 本文的具體研究內(nèi)容和創(chuàng)新性工作主要有以下幾個方面： 首先，在總結(jié)了以往云計算資源描述格式和語言、發(fā)現(xiàn)架構(gòu)和技術(shù)，以及動態(tài)組織、優(yōu)化分配和即時監(jiān)控等方面研究成果的基礎(chǔ)上，進而闡述了云環(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，分析了云計算資源需求負荷相對于先前的網(wǎng)格計算、分布式計算及其它高性能計算所表現(xiàn)出不同特點的基礎(chǔ)上，討論了短期負荷預(yù)測對于云計算實現(xiàn)資源實時控制、保持整個系統(tǒng)穩(wěn)定運行、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)建了基于資源負荷序列特征提取、分類和預(yù)測的多步驟預(yù)測方法。該方法運用定長重疊移動滑窗技術(shù)從云計算資源負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)督式聚類算法對所提取的子序列進行特征分類，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)對云計算短期動態(tài)資源負荷進行預(yù)測，以此獲得優(yōu)良的預(yù)測效果。 接著，基于云計算短期負荷預(yù)測的結(jié)果，本文構(gòu)建基于負荷預(yù)測的云計算資源優(yōu)化配置框架，提出了一種基于資源監(jiān)控和負荷預(yù)測的資源配置自適應(yīng)彈性控制系統(tǒng)，實施主動控制與被動反應(yīng)相結(jié)合的混合彈性控制的資源配置策略以實現(xiàn)云計算資源的有效利用；進一步地，，鑒于目前云計算服務(wù)提供商所采用的單虛擬機服務(wù)單用戶的資源管理模式所帶來的低資源利用率問題，本文構(gòu)建了一個具有五層結(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，該模式能針對不同用戶提出的應(yīng)用資源請求自動搜尋最優(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)用運行在同一臺虛擬機上，使得云計算提供商能在保證服務(wù)質(zhì)量的同時，提高云計算資源的利用效率，降低能耗。 最后，本文根據(jù)實際云計算資源管理中對資源負荷中長期預(yù)測的需求，針對云計算中長期負荷所表現(xiàn)出的兼具動態(tài)性和周期性這一特點，構(gòu)建了基于廣義模糊軟集理論的云計算資源負荷組合預(yù)測模型，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)測精度相結(jié)合來獲得各單項預(yù)測模型的權(quán)重，并針對云計算環(huán)境中資源負荷所表現(xiàn)出的短期動態(tài)性和長期周期性特征，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作為單項預(yù)測模型來分別處理其動態(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算資源負荷組合預(yù)測模型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)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TP393.07

【參考文獻】

相關(guān)期刊論文 前10條

1 陳華友,盛昭瀚;一類基于IOWGA算子的組合預(yù)測新方法[J];管理工程學(xué)報;2005年04期

2 周平;王志鵬;劉娜;李林;劉坤;;美國政府云計算相關(guān)工作綜述[J];信息技術(shù)與標準化;2011年11期

3 李伯虎;張霖;王時龍;陶飛;曹軍威;姜曉丹;宋曉;柴旭東;;云制造——面向服務(wù)的網(wǎng)絡(luò)化制造新模式[J];計算機集成制造系統(tǒng);2010年01期

4 李伯虎;張霖;任磊;柴旭東;陶飛;羅永亮;王勇智;尹超;黃剛;趙欣培;;再論云制造[J];計算機集成制造系統(tǒng);2011年03期

5 張霖;羅永亮;范文慧;陶飛;任磊;;云制造及相關(guān)先進制造模式分析[J];計算機集成制造系統(tǒng);2011年03期

6 傅余洋子;華薇娜;;基于Web of Science數(shù)據(jù)庫中云計算研究文獻的計量分析[J];新世紀圖書館;2013年07期

7 馮登國;張敏;張妍;徐震;;云計算安全研究[J];軟件學(xué)報;2011年01期

8 孫李紅;沈繼紅;;基于相關(guān)系數(shù)的加權(quán)幾何平均組合預(yù)測模型的性質(zhì)[J];系統(tǒng)工程理論與實踐;2009年09期

9 孫智勇;劉星;;模糊軟集合理論在稅收組合預(yù)測中的應(yīng)用[J];系統(tǒng)工程理論與實踐;2011年05期

10 李美娟;陳國宏;林志炳;;基于漂移度的組合預(yù)測方法研究[J];中國管理科學(xué);2011年03期

本文編號：2033441