發(fā)布時(shí)間：2018-09-17 15:20

【摘要】：隨著我國(guó)智能電網(wǎng)技術(shù)不斷地發(fā)展和深化，電力信息系統(tǒng)面臨著數(shù)據(jù)海量、分布異構(gòu)、處理復(fù)雜、使用繁瑣、維護(hù)困難等問題，而云計(jì)算技術(shù)具有強(qiáng)大處理能力、動(dòng)態(tài)性、靈活性、虛擬化以及面向服務(wù)等特性，正是一種應(yīng)對(duì)上述問題的有效解決方案。因此可以將云計(jì)算引入到電力系統(tǒng)中，構(gòu)建電力系統(tǒng)的專屬云—電力云。在電力云中，，任務(wù)調(diào)度系統(tǒng)是一個(gè)重要的組成部分，是提高電力云整體并發(fā)性、保證用戶任務(wù)和電力云資源合理分配、提高電力云的計(jì)算性能的關(guān)鍵。因此本文對(duì)電力云任務(wù)調(diào)度系統(tǒng)進(jìn)行研究，主要從調(diào)度系統(tǒng)模型以及調(diào)度算法兩個(gè)方面進(jìn)行了研究。 在對(duì)云計(jì)算基礎(chǔ)理論和電力信息平臺(tái)研究的基礎(chǔ)上，針對(duì)復(fù)雜的用戶任務(wù)，提出了一種基于改進(jìn)MapReduce模型的任務(wù)調(diào)度系統(tǒng)，保留了原有MapReduce模型的數(shù)據(jù)并行性優(yōu)點(diǎn)的同時(shí)，實(shí)現(xiàn)在架構(gòu)上進(jìn)行任務(wù)分解和并行計(jì)算，大大擴(kuò)展了MapReduce模型的并行性。在該任務(wù)調(diào)度系統(tǒng)模型的基礎(chǔ)之上，針對(duì)不同用戶任務(wù)的需求，提出了一種電力云任務(wù)調(diào)度算法，該算法以遺傳算法為原型，通過調(diào)整算法的多維約束條件來滿足不同用戶的需求。最后本文提出來的任務(wù)調(diào)度算法與多種其他的算法進(jìn)行仿真實(shí)驗(yàn)對(duì)比，實(shí)驗(yàn)結(jié)果表明該算法是一種十分有效的任務(wù)調(diào)度算法。
[Abstract]:With the continuous development and deepening of smart grid technology in China, power information system is faced with the problems of massive data, heterogeneous distribution, complex processing, cumbersome use, difficult maintenance and so on. Cloud computing technology has a strong processing ability, dynamic, and so on. Flexibility, virtualization, and service-oriented features are an effective solution to these problems. Therefore, cloud computing can be introduced into the power system to build the power system's exclusive cloud-power cloud. Task scheduling system is an important part of power cloud, which is the key to improve the concurrency of power cloud, ensure the rational distribution of user's tasks and power cloud resources, and improve the computing performance of power cloud. Therefore, this paper studies the power cloud task scheduling system, mainly from two aspects: the dispatching system model and the scheduling algorithm. Based on the research of cloud computing theory and power information platform, a task scheduling system based on improved MapReduce model is proposed for complex user tasks, which retains the advantages of data parallelism of the original MapReduce model. The implementation of task decomposition and parallel computing in architecture greatly extends the parallelism of MapReduce model. Based on the model of the task scheduling system and according to the needs of different users, a power cloud task scheduling algorithm is proposed, which is based on genetic algorithm (GA). The multi-dimensional constraints of the algorithm are adjusted to meet the needs of different users. Finally, the task scheduling algorithm proposed in this paper is compared with many other algorithms. The experimental results show that the algorithm is a very effective task scheduling algorithm.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP301.6;TM73

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本文編號(hào)：2246320