本文選題：云計算 + 能耗模型��； 參考：《南京郵電大學(xué)》2013年碩士論文

【摘要】：高能耗是云計算系統(tǒng)的一個主要問題，且隨著近年來云計算規(guī)模的日益擴大，其能耗開銷也愈加嚴(yán)重。本文針對云計算系統(tǒng)中的空閑和奢侈能耗，以動態(tài)電源管理及資源調(diào)度為研究內(nèi)容，以節(jié)能為主要研究目標(biāo)，主要作了以下四個方面的工作： （1）總結(jié)了國內(nèi)外節(jié)能技術(shù)的研究現(xiàn)狀，重點闡述了云計算的相關(guān)技術(shù)、現(xiàn)有的動態(tài)電源管理策略及云環(huán)境下的資源調(diào)度算法。 （2）本文充分考慮資源在休眠、空閑、工作和轉(zhuǎn)換等不同狀態(tài)下的多種能耗開銷，提出一種基于不同狀態(tài)的能耗估算模型（Energy Consumption Estimation Model based on DifferentStates，ECEMDS），并且用多功能計量插座對其進(jìn)行驗證。 （3）針對云計算系統(tǒng)中空閑能耗，本文基于指數(shù)平均算法，提出了一種自適應(yīng)的空閑時間預(yù)測策略。該策略引入了自適應(yīng)權(quán)值調(diào)節(jié)因子，動態(tài)調(diào)節(jié)歷史空閑時間對預(yù)測空閑時間的影響，同時結(jié)合分段滑動窗口的思想，將滑動窗口內(nèi)的空閑時間分為長、中、短三類，，取每類個數(shù)最多的空閑時間的平均值作為指數(shù)平均算法的實際空閑時間值。該策略通過對下一段空閑時間的預(yù)測，決定是否要切換物理主機的狀態(tài)以降低空閑能耗。實驗結(jié)果說明本文提出的策略預(yù)測準(zhǔn)確率較高，系統(tǒng)響應(yīng)時間少，節(jié)能效果好。 （4）本文針對奢侈能耗，提出一種云環(huán)境下節(jié)能的資源調(diào)度算法。首先，對云環(huán)境中的資源調(diào)度進(jìn)行建模。接著，提出一種基于改進(jìn)Min-Min算法的最小能耗資源調(diào)度算法Min-Energy，在滿足云任務(wù)QoS需求的基礎(chǔ)上，以云環(huán)境下每個任務(wù)的能耗最小為目標(biāo)調(diào)度資源。算法按照優(yōu)先級對任務(wù)排序，然后估算每個任務(wù)在各個資源上的能耗總和，選擇每個任務(wù)的最小能耗對應(yīng)的資源進(jìn)行調(diào)度。在CloudSim平臺的仿真結(jié)果表明，Min-Energy算法在完成時間和能量消耗方面均具有較好的性能，能夠達(dá)到節(jié)能的目的。
[Abstract]:High energy consumption is a major problem in cloud computing systems, and with the increasing scale of cloud computing in recent years, its energy consumption is becoming more and more serious.Aiming at the idle and extravagant energy consumption in cloud computing system, this paper takes dynamic power management and resource scheduling as the research content, and takes energy conservation as the main research goal, mainly doing the following four aspects of work:This paper summarizes the research status of energy saving technology at home and abroad, and focuses on the related technologies of cloud computing, the existing dynamic power management strategy and resource scheduling algorithm in cloud environment.In this paper, energy Consumption Estimation Model based on difference states is proposed and verified by multifunctional metering sockets, considering the energy cost of resources in different states, such as dormancy, idle, work and conversion. The energy consumption estimation model based on different states is presented in this paper.Aiming at the idle energy consumption in cloud computing system, an adaptive idle time prediction strategy based on exponential average algorithm is proposed in this paper.The strategy introduces the adaptive weight adjustment factor, dynamically adjusts the influence of the historical idle time on the prediction of idle time, and combines the idea of piecewise sliding window, divides the idle time in the sliding window into three categories: long, medium and short.The average of the idle time with the largest number of each class is taken as the actual idle time value of the exponential average algorithm.By predicting the next idle time, the strategy determines whether to switch the state of the physical host to reduce idle energy consumption.The experimental results show that the strategy proposed in this paper has higher prediction accuracy, less system response time and better energy saving effect.In this paper, a resource scheduling algorithm for energy saving in cloud environment is proposed for luxury energy consumption.Firstly, resource scheduling in cloud environment is modeled.Then, a Min-Energy scheduling algorithm based on improved Min-Min algorithm is proposed. On the basis of satisfying the QoS requirements of cloud tasks, the minimum energy consumption of each task in the cloud environment is taken as the target.The algorithm sorts the tasks according to the priority, then estimates the total energy consumption of each task on each resource, and selects the resources corresponding to the minimum energy consumption of each task to schedule.The simulation results on the CloudSim platform show that the Min-Energy algorithm has better performance in terms of completion time and energy consumption, and can achieve the purpose of energy saving.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP3

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