本文選題：彌散云資源　切入點(diǎn)：感知　出處：《河北經(jīng)貿(mào)大學(xué)》2014年碩士論文

【摘要】：云計(jì)算是一種實(shí)現(xiàn)大規(guī)模計(jì)算的信息處理方式，本質(zhì)上是利用虛擬化技術(shù)、分布式計(jì)算技術(shù)和網(wǎng)絡(luò)技術(shù)將分散的云基礎(chǔ)單元（簡(jiǎn)稱云元）匯聚到一起形成共享資源池，并以按需、彈性和可度量的方式向用戶提供云服務(wù)，這些“云元”通�？缂骸⑹�、甚至數(shù)據(jù)中心分布并隨時(shí)間動(dòng)態(tài)變化，隨著分散的“云元”在時(shí)空上動(dòng)態(tài)變化，資源池中的海量資源呈現(xiàn)彌散特征，形成彌散云資源，即一種分布式的具有時(shí)變性的變粒度的云資源。怎樣在海量資源中準(zhǔn)確快速感知并調(diào)度這些資源，提供低成本高性能可伸縮的云服務(wù)，提高用戶滿意度是目前和今后云計(jì)算技術(shù)領(lǐng)域面臨的重要問(wèn)題。 本文從IaaS資源提供方和請(qǐng)求方兩個(gè)角度，研究彌散云資源的感知與調(diào)度問(wèn)題，主要內(nèi)容有：（一）從IaaS資源提供方角度，提出基于MA感知的彌散云資源調(diào)度方法，主要研究：（1）提出基于移動(dòng)Agent范型的IaaS層資源部署架構(gòu)，并給出彌散云資源感知方法：基于作業(yè)完成時(shí)間分布預(yù)測(cè)，通過(guò)設(shè)置置信區(qū)間，剔除不符合條件的策略，一次性壓縮策略空間，引入Skyline計(jì)算思想細(xì)粒度抽取價(jià)值資源，形成資源視圖；（2）研究基于移動(dòng)Agent感知的彌散云資源調(diào)度方法MA_RS：利用基于移動(dòng)Agent的資源感知層感知的信息，利用彌散特性，結(jié)合多任務(wù)的松耦合特性，構(gòu)建“搜索——決策——執(zhí)行”這樣一個(gè)多階段迭代調(diào)度模型，實(shí)時(shí)細(xì)粒度的捕獲工作負(fù)載的局部特性，并根據(jù)相鄰階段工作負(fù)載的相似性特點(diǎn)，在工作負(fù)載變化劇烈（平緩）處自適應(yīng)動(dòng)態(tài)分割（合并）調(diào)度區(qū)間，實(shí)現(xiàn)整體調(diào)度性能的提升；（二）基于移動(dòng)Agent的MapReduce云計(jì)算計(jì)算框架，針對(duì)云資源彌散性，從資源請(qǐng)求方的角度考慮提出了一個(gè)公平共享指標(biāo)來(lái)實(shí)現(xiàn)高性能和公平性的基于云資源彌散性感知的公平調(diào)度方法，，主要研究：（1）構(gòu)建基于移動(dòng)Agent的MapReduce分布式計(jì)算框架MapReduce_MA，定義移動(dòng)Agen（t如Master_MA和Slaver-_MA）的功能集合，并具體實(shí)現(xiàn)了移動(dòng)Agent的主要功能；（2）研究基于云資源彌散性感知的公平調(diào)度方法，引入“共享進(jìn)度份額”來(lái)定義共享和公平，根據(jù)用戶偏好選擇任務(wù)，依據(jù)性能函數(shù)匹配相應(yīng)資源，并給出了基于云彌散性感知的公平調(diào)度算法。這種調(diào)度方法不僅權(quán)衡了成本和效益，而且能夠在異構(gòu)集群中提供良好的性能和公平性。 本文將上述架構(gòu)和方法在惠普實(shí)驗(yàn)室開(kāi)放Cirrus集群上進(jìn)行了有效性評(píng)估并通過(guò)實(shí)例驗(yàn)證的形式說(shuō)明了本文感知調(diào)度方法的潛在好處。
[Abstract]:Cloud computing is a kind of information processing method to realize large-scale computing. In essence, it uses virtualization technology, distributed computing technology and network technology to bring together scattered cloud base units (cloud elements) to form a pool of shared resources. And provide cloud services to users on demand, flexibility, and measurability. These "cloud elements" are typically distributed across clusters, rooms, and even data centers, and vary dynamically over time, as dispersed "cloud elements" change dynamically in time and space. The massive resources in the resource pool present the characteristics of dispersion and form the diffuse cloud resources, that is, a kind of distributed, time-varying and variable granularity cloud resources. How to accurately and quickly perceive and schedule these resources in the mass resources? Providing low cost and high performance scalable cloud services and improving user satisfaction are important problems in cloud computing technology field at present and in the future. In this paper, we study the perception and scheduling of diffuse cloud resources from the perspective of IaaS resource provider and requester. The main content of this paper is: (1) from the point of view of IaaS resource provider, we propose a MA aware distributed cloud resource scheduling method. In this paper, we propose a IaaS layer resource deployment architecture based on mobile Agent paradigm, and present a distributed cloud resource awareness method: based on the prediction of job completion time distribution, by setting confidence interval, we can eliminate the non-conforming strategy. In this paper, we introduce the idea of Skyline computing to extract value resources in a one-off compressed policy space, and form a resource view. (2) to study the distributed cloud resource scheduling method based on mobile Agent perception, MARSs: using the information of resource awareness layer based on mobile Agent. A multi-stage iterative scheduling model named "search-decision execution" is constructed by using the dispersion property and the loose coupling of multi-task. The real-time fine-grained local characteristics of the workload are captured. According to the similarity characteristics of workload in adjacent phases, adaptive dynamic partition (merging) scheduling interval is realized at the place where workload changes dramatically (flat), so as to improve the overall scheduling performance. (2) MapReduce cloud computing framework based on mobile Agent. In view of the dispersion of cloud resources, this paper proposes a fair sharing index to achieve high performance and fairness, which is based on the knowledge of cloud resources diffusion and fair scheduling. This paper mainly studies how to construct MapReduceMA-based MapReduce distributed computing framework based on mobile Agent, define the functional set of mobile Agen(t such as Master_MA and Slaver-Stack, and implement the main function of Mobile Agent.) A fair scheduling method based on cloud resource diffusion sexy knowledge is studied. "share schedule share" is introduced to define sharing and fairness. According to user preference, tasks are selected and corresponding resources are matched according to performance function. A fair scheduling algorithm based on cloud diffusion sexy knowledge is presented, which not only balances cost and benefit, but also provides good performance and fairness in heterogeneous clusters. This paper evaluates the effectiveness of the above architecture and methods on HP Labs open Cirrus cluster and illustrates the potential benefits of this method by example verification.
【學(xué)位授予單位】：河北經(jīng)貿(mào)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.01

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 趙樹(shù)超;基于人工蜂群算法的Hadoop調(diào)度算法研究與改進(jìn)[D];河北經(jīng)貿(mào)大學(xué);2016年

本文編號(hào)：1664032