【摘要】：云計算是搭建在同構(gòu)廉價普通節(jié)點上的分布式計算環(huán)境，在節(jié)點不可信的特點下，分布式存儲成為了云計算的必然選擇，存儲與計算的整合趨勢也日益明顯。本文搭建了一個簡單的對等架構(gòu)的云計算環(huán)境，并從分布式存儲與并行計算兩個方面對二者可能的協(xié)同進(jìn)行了研究，提出了一套分布式存儲策略和面向數(shù)據(jù)驅(qū)動的調(diào)度策略。 本文的云計算環(huán)境的物理層搭建在機(jī)柜式環(huán)境上，網(wǎng)絡(luò)拓?fù)鋵硬捎脤Φ仁郊軜?gòu)，整個環(huán)境基于Spring框架和各Java嵌入式組件搭建，并采用了多種流行通信框架，可以實現(xiàn)環(huán)境的快速搭建、高可擴(kuò)展和快速部署。本文將傳統(tǒng)的MPI環(huán)境與分布式存儲進(jìn)行了整合，探究了一種云計算環(huán)境下不采用MapReduce編程模型實現(xiàn)并行計算的方法。 在協(xié)同性的分布式存儲方面，本文提出了一套云計算環(huán)境下適用于地震資料的分布式存儲策略，該策略在充分研究地震資料及其處理特點的基礎(chǔ)上，對地震資料的數(shù)據(jù)劃分，副本的部署和定位進(jìn)行了研究。在協(xié)同性的并行計算方面，本文除了對分布式存儲和MPI環(huán)境進(jìn)行了整合，還設(shè)計了一種面向數(shù)據(jù)驅(qū)動的對等式調(diào)度策略，，該策略充分利用了分布式存儲的優(yōu)勢，不僅攤銷了分布式存儲的成本，而且節(jié)約了數(shù)據(jù)準(zhǔn)備的時間。
[Abstract]:Cloud computing is a distributed computing environment built on the isomorphic low cost ordinary nodes. With the characteristics of the nodes being unreliable, distributed storage has become the inevitable choice of cloud computing, and the integration of storage and computing is becoming more and more obvious. In this paper, a simple peer-to-peer cloud computing environment is built, and the possible collaboration between distributed storage and parallel computing is studied. A set of distributed storage strategy and data-driven scheduling strategy are proposed. In this paper, the physical layer of cloud computing environment is built in the cabinet environment, the network topology layer adopts the peer-to-peer architecture, the whole environment is based on the Spring framework and each Java embedded component, and adopts a variety of popular communication frameworks. Environment can be quickly built, high scalability and rapid deployment. In this paper, the traditional MPI environment is integrated with distributed storage, and a method of parallel computing without using MapReduce programming model in cloud computing environment is explored. In the aspect of collaborative distributed storage, this paper proposes a distributed storage strategy for seismic data in cloud computing environment. Based on the full study of the characteristics of seismic data and its processing, this strategy divides the seismic data. The deployment and location of copies were studied. In the aspect of collaborative parallel computing, this paper not only integrates distributed storage and MPI environment, but also designs a data-driven peer-to-peer scheduling strategy, which makes full use of the advantages of distributed storage. It not only amortizes the cost of distributed storage, but also saves the time of data preparation.
【學(xué)位授予單位】：中國石油大學(xué)（華東）
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP333

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