本文關(guān)鍵詞：材料計算模擬從計算集群到彈性云的遷移研究　出處：《北京科技大學(xué)》2017年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 云計算 網(wǎng)格計算 材料模擬 作業(yè)調(diào)度 負載均衡 資源分配

【摘要】：理論,計算和實驗材料科學(xué)與工程的最新進展,不僅是加快新材料發(fā)現(xiàn)速度的保證,而且減少了將這些發(fā)現(xiàn)作為新產(chǎn)品推向市場所需的時間。利用高通量密度泛函理論(DFT)計算進行新材料的篩選和基礎(chǔ)研究,為材料科學(xué)和材料創(chuàng)新提供了有趣的設(shè)計機會。高通量DFT通常涉及對數(shù)萬甚至數(shù)十萬化合物的計算和規(guī)模的這種變化需要新的計算能力和數(shù)據(jù)管理方法。到目前為止,為了確保及時的計算,這種高吞吐量DFT材料模擬必須在專用高性能計算機(HPC)上運行。然而,隨著生產(chǎn)和分析材料數(shù)據(jù)所需的工作數(shù)量和復(fù)雜程度的增加,HPC環(huán)境在合理的時間內(nèi)解決給定的科學(xué)問題成為一個挑戰(zhàn)。最新的計算范式,網(wǎng)格和云計算的出現(xiàn),使科學(xué)和IT領(lǐng)域都發(fā)生了巨大的變化。一方面,網(wǎng)格計算允許不同種類的資源,以安全的和靈活的方式,從個人電腦到超級計算機進行訪問,以解決在科學(xué)和工程領(lǐng)域所產(chǎn)生的大規(guī)模共享問題。另一方面,云計算是網(wǎng)格的進一步發(fā)展,它帶來了巨大的機會,以相對較低的成本托管和運行來自不同領(lǐng)域的現(xiàn)實應(yīng)用,而不需要擁有任何IT基礎(chǔ)架構(gòu)。此外,它還可以通過互聯(lián)網(wǎng)向消費者提供各種硬件資源作為服務(wù)�？紤]到在HPC上運行高通量DFT計算的障礙,本文旨在研究在云和網(wǎng)格計算環(huán)境中運行高通量DFT材料模擬,以充分利用地理分布和互連的大規(guī)模異構(gòu)來自網(wǎng)格提供的自主資源,同時受益于從動態(tài)彈性方式獲取大量云資源的可能性。在本論文中,為處理網(wǎng)格和云計算環(huán)境中的作業(yè)調(diào)度問題設(shè)計了新算法。以下詳細地論述了主要貢獻:1)提出了一種有效的作業(yè)調(diào)度算法,稱為兩種選擇調(diào)度算法(TCSA),用于動態(tài)分配作業(yè)到資源,以最小化作業(yè)執(zhí)行時間,并最大限度地提高云環(huán)境中的資源利用率。2)開發(fā)了一種改進的粒子群優(yōu)化算法(PSO)來解決網(wǎng)格環(huán)境中的作業(yè)調(diào)度問題。開發(fā)的PSO算法旨在同時最小化最長執(zhí)行時間的作業(yè)調(diào)度和所有任務(wù)的執(zhí)行時間3)為解決云計算環(huán)境中的作業(yè)調(diào)度問題設(shè)計了基于隨機化的負載均衡算法。該算法旨在通過在云中均勻分配資源之間的工作負載,從而最小化作業(yè)執(zhí)行時間并最大限度地提高資源利用率。4)提出了一種簡化版本的粒子群優(yōu)化算法(PSO)來解決云計算環(huán)境中的作業(yè)調(diào)度問題,以完成時間為目標(biāo)。為了評估所提出的算法的性能,我們通過在不同的場景下進行幾個模擬實驗,將所提出的作業(yè)調(diào)度算法與幾種現(xiàn)有的最新調(diào)度算法相比較。實驗結(jié)果表明,我們設(shè)計的算法工作得很好,能夠在合理的時間內(nèi)找到最優(yōu)或近似最優(yōu)解。此外,我們的方法在上述目標(biāo)方面顯著優(yōu)于其他比較算法,特別是當(dāng)調(diào)度問題變得太復(fù)雜或太大時。
[Abstract]:The latest progress of theory calculation and experiment of materials science and engineering, not only speed up new material discovery rate guarantee, but also reduce the findings as new products to the market. The time required to use high-throughput density functional theory (DFT) calculation and selection based on screen for new materials, designed to provide a chance interesting for materials science and materials innovation. The change of high throughput DFT usually involves tens of thousands or even hundreds of thousands of compounds are calculated and the size of the computation ability and data management methods. So far, in order to ensure the timely calculation, the high throughput of DFT material in high performance computer simulation must be special (HPC) on the run. However, with the increase of the number of work required for the production and analysis of material data and the complexity of the HPC environment to solve scientific problems given within a reasonable period of time has become a challenge. New paradigm of computing, grid and cloud computing, has brought about great changes in the field of science and IT. On the one hand, grid computing allows different kinds of resources in a secure and flexible way, from the personal computer to the super computer access, large-scale sharing to solve the issues arising in the fields of Science and engineering. On the other hand, cloud computing is the further development of grid, it has brought great opportunities, practical application of the relatively low cost of hosting and running from different fields, and do not need to have any IT infrastructure. In addition, it can also provide a variety of hardware resources as a service to consumers via the Internet to consider. Operation of high flux DFT in HPC calculation of obstacles, this paper aims to simulate the high flux of DFT material in the cloud and the operation of computing grid environment, in order to make full use of large scale heterogeneous geographic distribution and interconnection From the autonomous resource grid provides the possibility and benefit from access to a large number of cloud resources from the dynamic elastic method. In this paper, a new algorithm is designed for grid and cloud computing scheduling problems in the environment are discussed in detail. The following main contributions: 1) presents a job scheduling algorithm, called two selection scheduling algorithm (TCSA), for the dynamic allocation of jobs to resources, to minimize the execution time of operation, and maximize the utilization of.2 resources in the cloud environment) developed an improved particle swarm optimization (PSO) algorithm to solve the scheduling problem in grid environment. The development of the PSO algorithm at the same time to minimize the execution time of the job scheduling and execution time for all tasks 3) for scheduling problem in cloud computing environment the design of load balancing algorithm based on randomization. It is aimed at. In a uniform distribution of resources between the cloud workloads, thereby minimizing the job execution time and maximize the resource utilization rate of.4) presents a simplified version of the particle swarm optimization (PSO) algorithm to solve the scheduling problem in cloud computing environment, in order to complete the time as the goal. To evaluate the performance of the proposed our algorithm, through several simulation experiments in different scenarios, comparing the proposed scheduling algorithm with several existing new scheduling algorithm. The experimental results show that our algorithm is designed to work well within a reasonable period of time to find the optimal or approximate optimal solution. In addition, our method the target is significantly better than other algorithms, especially when the scheduling problem becomes too complex or too large.

【學(xué)位授予單位】：北京科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：TB305;TP18

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相關(guān)期刊論文 前1條

1 Maryam Yazdan Mehr;Willem Dirk van Driel;G.Q.(Kouchi) Zhang;;Progress in Understanding Color Maintenance in Solid-State Lighting Systems[J];Engineering;2015年02期

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本文編號：1408991