本文選題：云計算 + 任務調(diào)度��； 參考：《中國科學技術大學》2017年碩士論文

【摘要】：當今時代對海量數(shù)據(jù)處理能力的迫切需求和網(wǎng)絡技術的迅猛發(fā)展直接促使了云計算的產(chǎn)生。云計算通過互聯(lián)網(wǎng)將計算能力等服務以商品的形式提供給用戶,使得用戶可按需獲取計算資源然后依照相應的計價模式按用付費。云計算環(huán)境下的任務調(diào)度關乎云數(shù)據(jù)中心的運行效率并且直接影響到用戶的服務體驗。為促進云計算的可持續(xù)發(fā)展、提升用戶的服務體驗,制定真正滿足用戶需求的高效合理的任務調(diào)度策略是十分必要的。為改善調(diào)度系統(tǒng)的調(diào)度性能,本文分別研究了云計算環(huán)境下獨立及關聯(lián)任務調(diào)度中的常用算法。并針對最受用戶關心的調(diào)度時間和調(diào)度費用問題,在常用任務調(diào)度算法的基礎上提出了相應的改進算法。首先,分別對云計算中常用的獨立任務調(diào)度和關聯(lián)任務調(diào)度算法進行了研究和對比,并詳細分析了其各自的應用特性和優(yōu)缺點。其次,針對云環(huán)境中的獨立任務調(diào)度,綜合對任務集合調(diào)度時間、調(diào)度成本和系統(tǒng)資源利用率的考慮,提出了一種基于多種群遺傳算法的獨立任務調(diào)度策略。其以多種群遺傳算法代替?zhèn)鹘y(tǒng)遺傳算法,避免早熟收斂,并以min-min及max-min算法初始化種群,以提高最優(yōu)解的搜索效率。對于經(jīng)遺傳操作產(chǎn)生的子代,使用Metropolis準則對其進行篩選,使算法能以一定的概率接受差解,避免陷入局部最優(yōu)。與其他算法的對比實驗結果表明,該算法可有效減少任務集合調(diào)度時間和調(diào)度成本,且能兼顧到系統(tǒng)的負載均衡,是云環(huán)境下一種行之有效的任務調(diào)度方法,且比其他算法更適應于對大數(shù)量任務集合的處理。最后,針對待調(diào)度任務之間存在優(yōu)先級約束的情況,本文從提高任務調(diào)度的性價比出發(fā),提出了一種基于成本效益的改進關聯(lián)任務調(diào)度算法,并將對關聯(lián)任務的調(diào)度轉換為了對大規(guī)模圖狀數(shù)據(jù)的處理。為了探索更多可能被確定式算法忽略的高質(zhì)量解集,該算法采用多種群遺傳算法擴大最優(yōu)解的搜索范圍,并以任務集合的調(diào)度時間和調(diào)度成本設計適應度函數(shù)。此外,為避免因盲目復制冗余任務導致費用的過度增長,本文對傳統(tǒng)任務復制技術進行了改進。對比實驗結果表明,通過兩方面的改進,該算法相較于確定式調(diào)度算法可以有效降低任務集合的調(diào)度成本,同時保證合理的調(diào)度長度。
[Abstract]:Nowadays, the urgent demand for mass data processing ability and the rapid development of network technology directly promote the generation of cloud computing. Cloud computing provides services such as computing power to users in the form of goods through the Internet, which enables users to obtain computing resources on demand and then pay according to the corresponding pricing model. Task scheduling in cloud computing environment relates to the efficiency of cloud data center and directly affects the service experience of users. In order to promote the sustainable development of cloud computing, enhance the service experience of users, and formulate an efficient and reasonable task scheduling strategy to meet the needs of users, it is very necessary. In order to improve the scheduling performance of the scheduling system, the common algorithms of independent and associated task scheduling in cloud computing environment are studied in this paper. Aiming at the problem of scheduling time and scheduling cost which are most concerned by users, this paper proposes an improved algorithm based on the commonly used task scheduling algorithms. First of all, the common algorithms of independent task scheduling and associated task scheduling in cloud computing are studied and compared, and their application characteristics, advantages and disadvantages are analyzed in detail. Secondly, an independent task scheduling strategy based on multi-population genetic algorithm is proposed for independent task scheduling in cloud environment, considering the scheduling time, scheduling cost and system resource utilization. Multi-population genetic algorithm is used to replace traditional genetic algorithm to avoid premature convergence and min-min and max-min algorithms are used to initialize the population so as to improve the search efficiency of the optimal solution. For the offspring generated by genetic operation, Metropolis criterion is used to screen them, so that the algorithm can accept the differential solution with a certain probability and avoid falling into local optimum. Compared with other algorithms, the experimental results show that the algorithm can effectively reduce the task set scheduling time and scheduling cost, and can take into account the load balance of the system. It is an effective task scheduling method in the cloud environment. And it is more suitable to deal with large number of task sets than other algorithms. Finally, in view of the priority constraints between tasks to be scheduled, this paper proposes an improved algorithm for scheduling associated tasks based on cost-benefit, which is based on improving the performance and price ratio of task scheduling. The scheduling of associated tasks is converted to the processing of large scale graph data. In order to explore more high quality solution sets which may be neglected by deterministic algorithms, this algorithm uses multi-population genetic algorithm to expand the search range of optimal solutions, and designs fitness functions based on scheduling time and scheduling cost of task sets. In addition, in order to avoid excessive increase of cost caused by blind duplication of redundant tasks, the traditional task replication technology is improved in this paper. The experimental results show that compared with the deterministic scheduling algorithm, the proposed algorithm can effectively reduce the scheduling cost of the task set and ensure a reasonable scheduling length.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP301.6

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本文編號：2097102