本文選題：云環(huán)境　切入點：資源分配　出處：《大連理工大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著信息產(chǎn)業(yè)的急速發(fā)展,迎來了大數(shù)據(jù)的時代。網(wǎng)絡(luò)中數(shù)據(jù)量的劇增給已經(jīng)成熟的網(wǎng)絡(luò)結(jié)構(gòu)提出了巨大的挑戰(zhàn)。云計算是依托成熟的虛擬化技術(shù),從網(wǎng)格計算、分布式計算和協(xié)同計算的基礎(chǔ)上發(fā)展出來的。而云環(huán)境具有異構(gòu)性和動態(tài)性,如何根據(jù)用戶及任務(wù)的特點和需求進行資源的合理分配是需要解決的重要問題之一。針對大型云計算環(huán)境下的多節(jié)點協(xié)作問題建立了動態(tài)分級的網(wǎng)絡(luò)計算模型,并進一步提出了動態(tài)分級的資源分配算法(Dynamically Hierarchical Resource-Allocation Algorithm,DHRA).動態(tài)分級網(wǎng)絡(luò)計算模型采用模糊模式識別理論,根據(jù)任務(wù)和資源節(jié)點的信息將其動態(tài)地分為不同的等級。從而形成動態(tài)分級的網(wǎng)絡(luò)計算模型。因此對于每個任務(wù)只需要在相應(yīng)等級的節(jié)點中尋找合適的節(jié)點執(zhí)行即可,有效地減小了問題的規(guī)模。在此基礎(chǔ)上,在資源分配算法中引入多Agent機制,增加了系統(tǒng)的可靠性和自主性。綜合考慮了任務(wù)的完成時間、節(jié)點的負載、系統(tǒng)通信量等因素,使得算法在各方面都有較好的性能和效率。對于由大型應(yīng)用分解的相互關(guān)聯(lián)子任務(wù)的并行計算問題,由于所有任務(wù)的計算量和所需資源等信息都是已知的,采用隨機搜索類算法中的遺傳算法。并為了實現(xiàn)多方面的性能優(yōu)化,提出多目標(biāo)遺傳算法(Multi-Object Genetic Algorithm, MOGA)。采用任務(wù)完成時間和任務(wù)節(jié)點相關(guān)性兩個適應(yīng)度函數(shù)共同控制種群的進化方向。實現(xiàn)了在保證完成時間的前提下減少通信量的目的。對于DHRA算法和傳統(tǒng)的協(xié)商算法產(chǎn)生的通信量,進行了定量的理論分析,證明DHRA算法可以有效地減少系統(tǒng)通信量。并且對DHRA算法和MOGA算法在不同的任務(wù)和節(jié)點數(shù)時進行多組仿真實驗。將DHRA算法與MinMin算法進行對比,DHRA算法有效地減少的系統(tǒng)通信量的產(chǎn)生,同時保證任務(wù)完成時間也有一定的減少。有效地提高了系統(tǒng)的穩(wěn)定性和執(zhí)行效率。同樣地,對MOGA算法與傳統(tǒng)GA算法進行比較,在相同的條件下MOGA算法獲得了比傳統(tǒng)遺傳算法更少的任務(wù)完成時間和通信量。都有效地提高了系統(tǒng)的穩(wěn)定性和執(zhí)行效率。
[Abstract]:With the rapid development of information industry, the era of big data is ushered in. The huge increase in the amount of data in the network poses a great challenge to the mature network structure. Cloud computing is based on mature virtualization technology, from grid computing, Developed on the basis of distributed computing and collaborative computing. The cloud environment is heterogeneous and dynamic. How to allocate resources reasonably according to the characteristics and requirements of users and tasks is one of the important problems to be solved. A dynamic hierarchical network computing model is established to solve the multi-node collaboration problem in large-scale cloud computing environment. Furthermore, a dynamic Hierarchical Resource-Allocation algorithm is proposed for dynamic resource allocation. Fuzzy pattern recognition theory is used in the computing model of dynamic hierarchical network. According to the information of the task and resource nodes, they are dynamically divided into different levels. Thus, a dynamic hierarchical network computing model is formed. Therefore, for each task, it is only necessary to find the appropriate node in the corresponding level node to execute the task. On the basis of this, the multiple Agent mechanism is introduced into the resource allocation algorithm, which increases the reliability and autonomy of the system. The factors such as the completion time of the task, the load of the node, the traffic of the system, and so on, are considered synthetically. The algorithm has better performance and efficiency in all aspects. For parallel computing problems of interrelated subtasks decomposed by large applications, the information of all tasks is known, such as the amount of computation and the resources required. The genetic algorithm is used in the random search algorithm, and in order to optimize the performance of many aspects, A multi-objective genetic algorithm named Multi-Object Genetic algorithm (Moga) is proposed to control the evolution direction of the population by using two fitness functions: task completion time and task node correlation. The goal of reducing traffic while ensuring completion time is achieved. For the traffic generated by the DHRA algorithm and the traditional negotiation algorithm, A quantitative theoretical analysis was carried out. It is proved that the DHRA algorithm can effectively reduce the system traffic, and the simulation experiments of DHRA algorithm and MOGA algorithm are carried out in different tasks and nodes. Compared with MinMin algorithm, the DHRA algorithm can effectively reduce the system. The generation of communication traffic, At the same time, the task completion time is also reduced, which effectively improves the stability and efficiency of the system. Similarly, the MOGA algorithm is compared with the traditional GA algorithm. Under the same conditions, the MOGA algorithm achieves less task completion time and traffic than the traditional genetic algorithm, and improves the stability and efficiency of the system effectively.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP3

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