本文選題：云計算　切入點：任務(wù)調(diào)度　出處：《廈門大學(xué)》2014年碩士論文

【摘要】：云計算是一種新型的商業(yè)計算模型,它通過網(wǎng)絡(luò)進行連接,能夠獲得各種應(yīng)用、數(shù)據(jù)和IT服務(wù)。云計算的核心,是根據(jù)用戶的需求,對云環(huán)境中的資源和用戶提交的任務(wù)進行統(tǒng)一的調(diào)度和管理,而用戶只需要按需付費。因而在云服務(wù)中,如何滿足不同用戶對服務(wù)質(zhì)量(QoS)的不同需求,是云計算調(diào)度必須要考慮的重要問題。 遺傳算法是一種進化算法,它借鑒生物界的進化思想和自然界中“優(yōu)勝劣汰”的自然選擇機制,是一種全局優(yōu)化搜索算法。遺傳算法由于其本身所具備的并行性和全局解空間搜索的特點,被引入到了大規(guī)模集群系統(tǒng)的資源調(diào)度中。本文以用戶對服務(wù)質(zhì)量的需求為出發(fā)點,通過權(quán)重向量的設(shè)置,綜合考慮不同用戶對作業(yè)完成時間、帶寬、可靠性和費用等4個因素的不同需求,設(shè)計基于用戶滿意度的適應(yīng)度函數(shù),以保證服務(wù)質(zhì)量。 針對遺傳算法存在的“早熟”問題,本文采用模擬退火算法對其進行優(yōu)化。模擬退火算法借鑒物理上固體退火的機理,具有能夠跳出局部最優(yōu)解的特性,是一種全局最優(yōu)算法。然而,它存在對整個搜索空間的情況了解不多的缺點。將遺傳算法和模擬退火算法結(jié)合起來,能夠充分發(fā)揮兩者的優(yōu)勢,彌補二者的不足,提高算法性能。本文在遺傳算法產(chǎn)生新個體的過程中引入模擬退火算子,根據(jù)模擬退火算法中的Metropolis準則來決定是否接受遺傳算法產(chǎn)生的新個體,在保證種群多樣性的同時,也使種群能夠逐步進化。 本文還介紹了云仿真工具CloudSim,并配置了實驗環(huán)境。在CloudSim仿真平臺上,對本文所設(shè)計的遺傳算法和模擬退火算法優(yōu)化后的遺傳算法進行了仿真實驗。通過與基本遺傳算法進行實驗比較,表明本文設(shè)計的遺傳算法能夠更好地滿足不同用戶對云服務(wù)質(zhì)量的不同需求。通過對優(yōu)化前后兩種遺傳算法以及CloudSim自帶的隨機分配算法RA和輪詢算法RR之間的實驗結(jié)果對比,表明采用模擬退火算子對算法進行優(yōu)化后,算法性能有所改善。
[Abstract]:Cloud computing is a new type of business computing model, which can access various applications, data and IT services through a network connection.The core of cloud computing is to schedule and manage the resources and tasks submitted by users according to the needs of users, and users only need to pay on demand.Therefore, how to meet the different needs of different users for QoS in cloud services is an important issue that must be considered in cloud computing scheduling.Genetic algorithm (GA) is an evolutionary algorithm, which draws lessons from the evolutionary thinking of the biological world and natural selection mechanism of "survival of the fittest" in nature, and is a global optimization search algorithm.Genetic algorithm (GA) is introduced into the resource scheduling of large-scale cluster system because of its parallelism and global solution space search.Based on the user's demand for quality of service (QoS), this paper considers the different requirements of different users for four factors, such as job completion time, bandwidth, reliability and cost, by setting the weight vector.The fitness function based on user satisfaction is designed to guarantee the quality of service.Aiming at the problem of precocity in genetic algorithm, simulated annealing algorithm is used to optimize it.The simulated annealing algorithm is a global optimal algorithm because it can jump out of the local optimal solution by referring to the mechanism of solid annealing in physics.However, it has the disadvantage of not knowing much about the whole search space.The combination of genetic algorithm and simulated annealing algorithm can give full play to the advantages of the two algorithms, make up for their shortcomings and improve the performance of the algorithm.In this paper, the simulated annealing operator is introduced in the process of generating new individuals by genetic algorithm. According to the Metropolis criterion of simulated annealing algorithm, we decide whether to accept the new individuals generated by genetic algorithm. At the same time, we can ensure the diversity of population.It also allows the population to evolve.The cloud simulation tool CloudSimand is also introduced in this paper, and the experimental environment is configured.On the platform of CloudSim, the genetic algorithm and simulated annealing algorithm are simulated.By comparing with the basic genetic algorithm, it is shown that the genetic algorithm designed in this paper can better meet the different needs of different users for cloud quality of service.By comparing the experimental results between the two genetic algorithms before and after optimization, CloudSim's own random assignment algorithm RA and the polling algorithm RR, it is shown that the performance of the algorithm is improved by using simulated annealing operator to optimize the algorithm.
【學(xué)位授予單位】：廈門大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP18;TP393.01

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本文編號：1730102