發(fā)布時間：2019-03-28 11:25

【摘要】：近年來，隨著實時性應(yīng)用的大量出現(xiàn)，，人們對服務(wù)質(zhì)量的要求也隨之提高。然而，人們在享受大規(guī)模集群服務(wù)器為人們提供的便利時，卻忽視了隨之而來的嚴(yán)峻問題----能耗。隨著我國電荒嚴(yán)重程度的加深，加之國際經(jīng)濟(jì)危機(jī)對企業(yè)運(yùn)營成本的管理提出了更高要求，服務(wù)器利用率的提高與節(jié)能已經(jīng)成為了學(xué)術(shù)界和工業(yè)界關(guān)注的熱點(diǎn)。本文的研究對于提高共享網(wǎng)絡(luò)資源利用率和節(jié)能控制具有重要的意義。 目前網(wǎng)絡(luò)應(yīng)用中，流媒體應(yīng)用占較大份額，因此本文針對流媒體服務(wù)器日益增加的能耗問題，做了如下幾個方面的工作： 1.在Linux系統(tǒng)下，本文針對許多文獻(xiàn)尚未提及的硬盤存取能力進(jìn)行了分析，改進(jìn)了基于/proc文件系統(tǒng)和加載內(nèi)核模塊LKM相結(jié)合的方法，實現(xiàn)了對計算資源、內(nèi)存資源、硬盤資源、網(wǎng)絡(luò)帶寬資源的測量。該方法利用/proc文件系統(tǒng)獲取計算機(jī)信息全面、迅捷、準(zhǔn)確的特點(diǎn)以及加載內(nèi)核模塊LKM占用資源小、訪問權(quán)限高的優(yōu)點(diǎn)，使之更能快速、準(zhǔn)確、全面地獲取了系統(tǒng)實時負(fù)載情況。 2.提出了一種加權(quán)負(fù)載求導(dǎo)預(yù)測法，實現(xiàn)了對流媒體集群服務(wù)器的網(wǎng)絡(luò)資源的預(yù)測。該方法基于流媒體負(fù)載的日周期性，利用負(fù)載曲線變化率的穩(wěn)定性進(jìn)行預(yù)測工作。對現(xiàn)有加權(quán)平均計算方法中只是針對圖形而不是考慮數(shù)據(jù)的分形相似進(jìn)行了改進(jìn)，并將結(jié)果進(jìn)行了平滑處理，確保了其預(yù)測的精度。 3.提出了一種基于預(yù)測的流媒體集群服務(wù)器節(jié)能策略。該策略根據(jù)歷史信息，運(yùn)用加權(quán)負(fù)載求導(dǎo)預(yù)測法預(yù)測出系統(tǒng)的未來負(fù)載情況，根據(jù)各服務(wù)器負(fù)載狀態(tài)進(jìn)行負(fù)載轉(zhuǎn)移，休眠多個空閑服務(wù)器，實現(xiàn)節(jié)能；還可以通過定時喚醒操作，讓空閑服務(wù)器能夠在負(fù)載高峰到達(dá)前并入系統(tǒng)，滿足服務(wù)質(zhì)量要求。因此，該策略可以在保證不對服務(wù)能力造成較大影響的前提下，達(dá)到節(jié)能的目的。 4.搭建了一個具有典型代表性的流媒體集群服務(wù)器應(yīng)用實驗平臺，實現(xiàn)了本文所提出的加權(quán)負(fù)載求導(dǎo)預(yù)測法和基于負(fù)載的服務(wù)器節(jié)能策略，并驗證了其預(yù)測精度，還實現(xiàn)了基于預(yù)測的負(fù)載調(diào)度節(jié)能策略，證實了該節(jié)能控制策略的實際可行性。
[Abstract]:In recent years, with the emergence of a large number of real-time applications, people's requirements for quality of service are also improved. However, when people enjoy the convenience of large-scale cluster server, they ignore the serious problem-energy consumption. With the deepening of the power shortage in China, and the international economic crisis has put forward higher requirements for the management of enterprise operating costs, the improvement of server utilization and energy saving have become the focus of academic and industrial attention. The research in this paper is of great significance for improving the utilization rate of shared network resources and energy-saving control. At present, streaming media applications account for a large share of network applications, so this paper aims at the increasing energy consumption of streaming media servers, and does some work as follows: 1. Under the Linux system, this paper analyzes the hard disk access ability which has not been mentioned in many literatures, improves the method of combining the file system based on / proc and the loading kernel module LKM, realizes the calculation resource, the memory resource, the hard disk resource, and so on. Measurement of network bandwidth resources. The method uses / proc file system to obtain computer information, which is comprehensive, quick and accurate, and the advantages of loading kernel module LKM, such as small resource occupation and high access authority, make it more rapid and accurate. The real-time load of the system is obtained completely. 2. A weighted load prediction method is proposed to predict the network resources of streaming media cluster servers. Based on the daily periodicity of streaming media load, this method makes use of the stability of load curve change rate to predict. In this paper, the existing weighted average calculation methods are improved only for the fractal similarity of the graph rather than the data, and the results are smoothed so as to ensure the accuracy of the prediction. 3. This paper presents a prediction-based energy saving strategy for streaming media cluster servers. According to the historical information, the weighted load prediction method is used to predict the future load of the system, load transfer is carried out according to the load status of each server, and several idle servers are dormant to achieve energy saving. Through regular wake-up operation, the idle server can be integrated into the system before the peak load arrives, to meet the quality of service requirements. Therefore, the strategy can achieve the goal of energy-saving under the premise that it does not have a great impact on service capability. 4. A typical streaming media cluster server application experiment platform is built, and the weighted load derivation prediction method and the load-based server energy saving strategy are implemented in this paper, and the prediction accuracy is verified. Finally, the load scheduling energy saving strategy based on prediction is realized, and the feasibility of the energy saving control strategy is verified.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP393.05

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