本文選題：綠色計算　切入點：云計算　出處：《哈爾濱工業(yè)大學》2013年碩士論文　論文類型：學位論文

【摘要】：隨著信息技術(shù)的高速發(fā)展，信息技術(shù)行業(yè)所帶來的能量消耗也成為人們十分關(guān)注的問題之一。隨著云計算的發(fā)展逐漸成熟，相關(guān)的應用正在逐年增加，由于云計算數(shù)據(jù)中心服務器及其配套設備規(guī)模的高速增長，快速攀升的能耗已成為影響企業(yè)利潤的重要因素，研究如何對數(shù)據(jù)中心的資源和任務進行優(yōu)化管理，以降低能耗、減少污染對企業(yè)和環(huán)境保護都有著重要的意義。 云計算數(shù)據(jù)中心通常包含一個服務器機群，這些服務器同時運行大量的應用程序，這種情況就可以對數(shù)據(jù)中心的應用負載進行整合，用較小數(shù)量的服務器運行任務，使服務器的各項資源都能得到充分的利用的同時又不會出現(xiàn)資源爭用的情況，從而達到降低成本，節(jié)約能耗的目的，這就是本文所要研究的內(nèi)容。 由于不同任務對CPU、內(nèi)存等各種計算資源的需求量不同，為了使數(shù)據(jù)中心服務器各項資源得到充分利用，首先需要對任務對不同計算資源的需求量進行預測，針對這一問題，本文首先提出了基于神經(jīng)網(wǎng)絡的程序資源消耗預測模型，使用這一預測模型對云計算任務各項計算資源消耗進行預測，該模型以影響程序運行資源消耗的各項因素作為神經(jīng)網(wǎng)絡輸入，以程序運行所消耗的時間、CPU利用率、內(nèi)存使用量、硬盤使用量作為網(wǎng)絡輸出，，收集程序運行的歷史數(shù)據(jù)作為神經(jīng)網(wǎng)絡的訓練和測試樣本，實現(xiàn)對程序性能及資源使用的預測。 根據(jù)云計算任務各項資源消耗量的預測結(jié)果，對數(shù)據(jù)中心的任務和服務器各項資源進行整合，優(yōu)化任務調(diào)度方案。為了減少運行主機并使其各項硬件資源得到充分的利用，同時又能夠避免資源爭用的情況出現(xiàn)，本文將任務分配問題轉(zhuǎn)化為一個多維多背包問題進行求解，由于任務分配問題是一個NP完全問題，本文設計采用混合遺傳算法對該問題求解，以能耗最小作為目標函數(shù)，求得任務分配問題最低能耗的優(yōu)化解，從而實現(xiàn)降低能耗，節(jié)約成本的目的。
[Abstract]:With the rapid development of information technology, the energy consumption brought by the information technology industry has become one of the problems that people pay close attention to. With the development of cloud computing, the related applications are increasing year by year. Due to the rapid growth of cloud computing data center servers and their supporting equipment scale, the rapidly rising energy consumption has become an important factor affecting the profits of enterprises. This paper studies how to optimize the management of data center resources and tasks in order to reduce energy consumption. Reducing pollution is of great significance to enterprises and environmental protection. Cloud computing data centers typically contain a cluster of servers that run a large number of applications at the same time, so that the application load of the data center can be consolidated to run tasks with a smaller number of servers. So that all the resources of the server can be fully utilized without the situation of resource contention, so as to achieve the purpose of reducing cost and saving energy consumption, this is the content of this paper. Because different tasks require different computing resources, such as CPU, memory and so on, in order to make full use of the resources of the data center server, it is necessary to forecast the demand of different computing resources for different tasks, aiming at this problem. In this paper, a program resource consumption prediction model based on neural network is proposed, which is used to predict the computing resource consumption of cloud computing tasks. In this model, the factors that affect the consumption of running resources are taken as the input of neural network, and the CPU utilization, memory usage and hard disk usage are used as the network output. The historical data of program running are collected as training and test samples of neural network to predict program performance and resource usage. According to the forecast results of resource consumption of cloud computing task, the task of data center and the resource of server are integrated, and the task scheduling scheme is optimized. In order to reduce the running host and make full use of its hardware resources, At the same time, the problem of resource contention can be avoided. In this paper, the task assignment problem is transformed into a multi-dimensional multi-knapsack problem, because the task assignment problem is a NP-complete problem. In this paper, a hybrid genetic algorithm is used to solve the problem. With the minimum energy consumption as the objective function, the optimal solution of the minimum energy consumption of the task assignment problem is obtained, so as to reduce the energy consumption and save the cost.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP308;TP301.6

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