發(fā)布時(shí)間：2018-01-21 12:13

  本文關(guān)鍵詞： 合作型協(xié)同進(jìn)化算法 合作者選擇 云計(jì)算 任務(wù)調(diào)度 CloudSim仿真器　出處：《華南理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：目前,遺傳算法以其獨(dú)特的優(yōu)勢吸引著研究者們的眼球。但是由于遺傳算法在解空間很大的情況下編碼過長,不方便處理。進(jìn)而出現(xiàn)了合作型協(xié)同進(jìn)化算法,它繼承了遺傳算法的優(yōu)點(diǎn),而又克服了遺傳算法的缺點(diǎn),因此在算法提出之日起就得到的廣泛關(guān)注。當(dāng)前,云計(jì)算技術(shù)蓬勃發(fā)展,云平臺(tái)要處理海量的用戶請(qǐng)求。如何對(duì)用戶任務(wù)進(jìn)行合理的調(diào)度,滿足用戶的需求,是云技術(shù)發(fā)展過程中一個(gè)迫切需要解決的問題。因?yàn)楹献鲄f(xié)同進(jìn)化在處理多變量的復(fù)雜問題時(shí)存在優(yōu)勢,將合作協(xié)同進(jìn)化算法應(yīng)用于云任務(wù)調(diào)度中也是目前研究的熱點(diǎn)。合作協(xié)同進(jìn)化算法中一個(gè)種群的個(gè)體只代表問題解的一部分,因此需要從其他種群中選擇合作個(gè)體構(gòu)成完整解之后才能評(píng)價(jià)個(gè)體的優(yōu)劣性。合作者選擇問題是合作協(xié)同進(jìn)化算法中一個(gè)非常重要的問題。當(dāng)前,合作者的選擇問題并沒有一個(gè)合適的解決方案,具有改進(jìn)的空間。本文基于機(jī)器學(xué)習(xí)中分類的思想提出了一種基于距離的合作者選擇方法,通過計(jì)算待評(píng)價(jià)個(gè)體到最優(yōu)個(gè)體和隨機(jī)個(gè)體的距離來選擇最合適的合作團(tuán)體。該方法可以在控制評(píng)價(jià)次數(shù)的情況下,對(duì)個(gè)體做出更加合理的評(píng)價(jià),從而使整個(gè)算法能夠得到更優(yōu)化的解。本文在典型的函數(shù)優(yōu)化以及車間調(diào)度問題中驗(yàn)證了算法可行性和有效性,實(shí)驗(yàn)證明算法能搜索到更優(yōu)化的解。本文將改進(jìn)協(xié)同進(jìn)化算法用于云任務(wù)調(diào)度問題中,主要解決云任務(wù)調(diào)度中用戶任務(wù)請(qǐng)求量大及時(shí)間跨度的問題。首先,將云任務(wù)調(diào)度問題抽象為一個(gè)尋優(yōu)模型;然后設(shè)計(jì)編碼方式和遺傳算子的操作細(xì)節(jié),使算法能夠發(fā)揮最佳性能;最后,設(shè)計(jì)出使用改進(jìn)合作協(xié)同進(jìn)化算法解決云任務(wù)調(diào)度問題的整體調(diào)度流程。之后在模擬器Cloud Sim上進(jìn)行實(shí)驗(yàn)。實(shí)驗(yàn)證明,在數(shù)據(jù)中心虛擬機(jī)性能差異不大的情況下,算法能夠得到比主流調(diào)度算法更優(yōu)的時(shí)間跨度;在數(shù)據(jù)中心虛擬機(jī)性能差異較大的情況下,算法得到的時(shí)間跨度優(yōu)于遺傳算法和標(biāo)準(zhǔn)協(xié)同進(jìn)化算法,但是比MIN-MIN算法的結(jié)果差。因此,算法不適合處理虛擬機(jī)差異較大時(shí)的云環(huán)境調(diào)度問題。
[Abstract]:At present, genetic algorithm has attracted the attention of researchers because of its unique advantages. However, because genetic algorithm encodes too long in large solution space, it is not easy to process, and then cooperative co-evolution algorithm appears. It inherits the advantages of genetic algorithm, but overcomes the shortcomings of genetic algorithm, so it has received extensive attention since the date of the algorithm was put forward. Currently, cloud computing technology is booming. Cloud platform has to deal with a large number of user requests. How to reasonably schedule user tasks to meet the needs of users. It is an urgent problem in the development of cloud technology, because cooperative co-evolution has advantages in dealing with complex multi-variable problems. The application of cooperative coevolutionary algorithm to cloud task scheduling is also a hot topic. In cooperative co-evolution algorithm, the individual of a population represents only a part of the solution of the problem. Therefore, it is necessary to select cooperative individuals from other populations to form a complete solution to evaluate the merits and demerits of individuals. The selection of collaborators is a very important problem in cooperative coevolutionary algorithms. There is not a suitable solution to the problem of partner selection, and there is room for improvement. This paper proposes a distance based partner selection method based on the idea of classification in machine learning. By calculating the distance between the individual to the optimal individual and the random individual to select the most suitable cooperative group, this method can make a more reasonable evaluation of the individual under the condition of controlling the evaluation times. So that the whole algorithm can get a better solution. This paper verifies the feasibility and effectiveness of the algorithm in the typical function optimization and job shop scheduling problem. Experiments show that the algorithm can find a better solution. In this paper, the improved co-evolution algorithm is applied to the cloud task scheduling problem, mainly to solve the problem of large amount of user task request and time span in cloud task scheduling. First of all. The cloud task scheduling problem is abstracted into an optimization model. Then the coding method and the operation details of the genetic operator are designed so that the algorithm can give full play to the best performance. Finally, the overall scheduling process of cloud task scheduling problem using improved cooperative co-evolution algorithm is designed. Then the experiment is carried out on the simulator Cloud Sim. When the performance of the data center virtual machine is not different, the time span of the algorithm is better than that of the mainstream scheduling algorithm. The time span of the algorithm is better than that of the genetic algorithm and the standard co-evolution algorithm, but the result is worse than that of the MIN-MIN algorithm when the performance of the virtual machine in the data center is quite different. The algorithm is not suitable to deal with the cloud environment scheduling problem with large differences in virtual machines.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP18;TP393.07

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