【摘要】：在過去的十幾年里，，分布式計(jì)算技術(shù)得到了廣泛的研究和應(yīng)用。在分布式系統(tǒng)中，用戶共享所有的資源，彼此之間存在著競爭關(guān)系，為了提高分布式系統(tǒng)的性能，有效的資源分配機(jī)制顯得格外重要。而準(zhǔn)確的對(duì)資源使用情況進(jìn)行預(yù)測可以使資源分配更加有效，所以本文將主要研究如何更準(zhǔn)確的對(duì)各種資源使用情況進(jìn)行預(yù)測。通常將系統(tǒng)資源使用情況看做時(shí)間序列進(jìn)行分析和預(yù)測，傳統(tǒng)的時(shí)間序列分析方法如自回歸模型等都可以用于系統(tǒng)資源使用情況的分析和預(yù)測，近些年來更多的非線性模型被應(yīng)用于時(shí)間序列的預(yù)測也取得了很好的效果。 根據(jù)參考變量維度的不同，時(shí)間序列的預(yù)測可以分為單變量預(yù)測和多變量（多維度）預(yù)測�，F(xiàn)有的各種預(yù)測模型都存在對(duì)數(shù)據(jù)的敏感性，往往在不同數(shù)據(jù)集上預(yù)測效果相差較大。另外實(shí)際情況下，在多變的分布式系統(tǒng)中，也很難保證特定機(jī)器上某種資源的變化規(guī)律一成不變。因而在本文中，我們提出了一種兩層反饋式集成預(yù)測模型，一方面根據(jù)集成學(xué)習(xí)的思想提高預(yù)測的準(zhǔn)確度與適應(yīng)度，另一方面，不斷對(duì)各個(gè)基礎(chǔ)預(yù)測器進(jìn)行優(yōu)化，更進(jìn)一步的提高預(yù)測能力。預(yù)測器優(yōu)化模塊使預(yù)測器集成模塊獲得更好的結(jié)果，同時(shí)預(yù)測器集成模塊會(huì)根據(jù)集成的結(jié)果反作用于預(yù)測器優(yōu)化模塊，這種相互作用不斷提高集成預(yù)測模型的預(yù)測能力。 首先我們將該集成預(yù)測模型應(yīng)用于單變量預(yù)測，將常用的幾種單變量預(yù)測模型進(jìn)行集成，并通過設(shè)計(jì)一系列的實(shí)驗(yàn)驗(yàn)證了集成預(yù)測模型的預(yù)測能力。接下來，我們介紹了幾種多變量預(yù)測模型，在多變量預(yù)測中機(jī)器學(xué)習(xí)的方法取得了很好的效果，我們對(duì)其中的支持向量機(jī)回歸預(yù)測模型進(jìn)行了優(yōu)化，然后與其他幾種多變量預(yù)測模型一起構(gòu)成我們的多變量集成預(yù)測模型。通過一系列的實(shí)驗(yàn)表明，該集成預(yù)測模型在多變量預(yù)測中同樣有較為理想的效果。
[Abstract]:In the past decade, distributed computing technology has been widely studied and applied. In distributed systems, users share all resources, and there is a competitive relationship between them. In order to improve the performance of distributed systems, effective resource allocation mechanism is particularly important. And accurate prediction of resource use can make resource allocation more effective, so this paper will mainly study how to predict the use of various resources more accurately. The use of system resources is usually regarded as time series analysis and prediction. Traditional time series analysis methods such as autoregressive model can be used to analyze and predict the system resource use. In recent years, more nonlinear models have been applied to the prediction of time series. According to the different dimensions of reference variables, the prediction of time series can be divided into single variable prediction and multivariate (multivariate) prediction. All kinds of existing prediction models are sensitive to data, and the prediction results vary greatly in different data sets. In addition, in the changeable distributed system, it is difficult to ensure that the rule of change of a certain resource on a particular machine remains unchanged. Therefore, in this paper, we propose a two-layer feedback integrated prediction model. On the one hand, we improve the accuracy and fitness of prediction according to the idea of integrated learning; on the other hand, we constantly optimize each basic predictor. Further improve the ability to predict. The predictor optimization module makes the predictor integration module obtain better results, and the predictor integration module will react to the predictor optimization module according to the integrated results. This interaction improves the prediction ability of the integrated prediction model. Firstly, we apply the integrated prediction model to single variable prediction, and integrate several commonly used single variable prediction models, and design a series of experiments to verify the prediction ability of the integrated prediction model. Then, we introduce several kinds of multivariate prediction models. The machine learning method in multivariate prediction has achieved good results. We have optimized the support vector machine regression prediction model. Then our integrated multivariable prediction model is constructed with several other multivariable prediction models. A series of experiments show that the integrated prediction model is also effective in multivariate prediction.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP338.8

【引證文獻(xiàn)】

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1 張宗華;張海全;魏馳;牛新征;;基于加權(quán)改進(jìn)的AR模型的負(fù)載預(yù)測研究[J];計(jì)算機(jī)測量與控制;2016年03期

2 畢子健;王翎穎;;電網(wǎng)物資需求預(yù)測方法研究[J];華北電力技術(shù);2015年10期

相關(guān)碩士學(xué)位論文 前1條

1 曲文麗;基于JCF中間件的負(fù)載均衡算法研究[D];中國民航大學(xué);2015年

本文編號(hào)：2174509