本文選題：云計(jì)算 + 實(shí)時(shí)任務(wù)調(diào)度��； 參考：《北京郵電大學(xué)》2016年碩士論文

【摘要】：云計(jì)算通過(guò)將計(jì)算資源、存儲(chǔ)資源和服務(wù)資源等通過(guò)網(wǎng)絡(luò)連接起來(lái),形成一個(gè)資源池,然后根據(jù)用戶(hù)的需求,對(duì)資源進(jìn)行統(tǒng)一的調(diào)度和管理。如何對(duì)資源池中的資源進(jìn)行及時(shí)、高效地調(diào)度,以滿(mǎn)足用戶(hù)對(duì)云服務(wù)質(zhì)量的多方面的需求,是云計(jì)算研究中很重要的一環(huán)。當(dāng)前的實(shí)時(shí)任務(wù)調(diào)度算法只關(guān)注于滿(mǎn)足用戶(hù)任務(wù)的實(shí)時(shí)性需求并且這些算法不夠靈活,對(duì)于實(shí)時(shí)變化的異構(gòu)系統(tǒng),不能較好的適應(yīng)。本文借助遺傳算法全局優(yōu)化搜索的特點(diǎn),從用戶(hù)的實(shí)時(shí)性需求和系統(tǒng)的總體吞吐量出發(fā),設(shè)計(jì)出基于實(shí)時(shí)性和總體吞吐量的適應(yīng)度函數(shù),進(jìn)而可以將基于遺傳算法的任務(wù)調(diào)度應(yīng)用到實(shí)時(shí)任務(wù)調(diào)度的環(huán)境當(dāng)中。不僅能夠更好地適應(yīng)實(shí)時(shí)變化的云計(jì)算環(huán)境,更能夠保證任務(wù)的總體運(yùn)行效率。針對(duì)遺傳算法存在的收斂速度慢的問(wèn)題,本文提出了基于資源感知的優(yōu)化策略。根據(jù)任務(wù)負(fù)載的大小、資源使用情況以及任務(wù)的類(lèi)型,引導(dǎo)遺傳算法的收斂過(guò)程,從而加速收斂過(guò)程。然后在模擬環(huán)境中進(jìn)行了實(shí)驗(yàn),通過(guò)與傳統(tǒng)的實(shí)時(shí)任務(wù)調(diào)度算法在不同的參數(shù)條件下進(jìn)行比較,驗(yàn)證了改進(jìn)后的遺傳算法在任務(wù)完成率、資源利用率等方面都具有較好的效率。銀行實(shí)時(shí)預(yù)測(cè)系統(tǒng)通過(guò)機(jī)器學(xué)習(xí)的方法預(yù)測(cè)資源的閾值以及使用趨勢(shì)等,對(duì)于及時(shí)發(fā)現(xiàn)系統(tǒng)故障,瓶頸具有很好的參考價(jià)值。本文提出的調(diào)度算法對(duì)于這種數(shù)據(jù)量、運(yùn)算量較大,并且需要較好的實(shí)時(shí)性的系統(tǒng)具有較好的適用性。最后將文中提出的改進(jìn)算法和傳統(tǒng)的任務(wù)調(diào)度算法分別對(duì)該系統(tǒng)進(jìn)行任務(wù)調(diào)度,通過(guò)任務(wù)完成率、系統(tǒng)總體運(yùn)行時(shí)間及系統(tǒng)能耗等方面的對(duì)比,證明本文提出的算法在實(shí)際應(yīng)用中也有較高的效率。
[Abstract]:Cloud computing connects computing resources, storage resources and service resources through the network to form a pool of resources. Then according to the needs of users, resources are uniformly scheduled and managed. How to schedule the resources in the resource pool in a timely and efficient manner to meet the needs of users in many aspects of cloud service quality is an important part of cloud computing research. The current real-time task scheduling algorithms only focus on meeting the real-time requirements of user tasks and these algorithms are not flexible enough to adapt to real-time changing heterogeneous systems. In this paper, according to the characteristics of global optimization search based on genetic algorithm, a fitness function based on real-time and total throughput is designed based on the real-time requirements of users and the overall throughput of the system. Then the genetic algorithm based task scheduling can be applied to the real-time task scheduling environment. It can not only adapt to the real-time changing cloud computing environment, but also ensure the overall operational efficiency of the task. Aiming at the problem of slow convergence rate of genetic algorithm, an optimization strategy based on resource awareness is proposed in this paper. According to the size of task load, resource usage and task type, the convergence process of genetic algorithm is guided and the convergence process is accelerated. By comparing with the traditional real-time task scheduling algorithm under different parameter conditions, the improved genetic algorithm is proved to be more efficient in terms of task completion rate and resource utilization. The bank real-time prediction system predicts the threshold value and the usage trend of the resource by the method of machine learning, which has a good reference value for detecting the system fault in time. The scheduling algorithm proposed in this paper is suitable for the system with large amount of computation and good real-time performance. Finally, the proposed improved algorithm and the traditional task scheduling algorithm are compared in terms of task completion rate, system total running time and system energy consumption. It is proved that the proposed algorithm has high efficiency in practical application.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP311.52;TP18

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