【摘要】：近年來,隨著信息技術(shù)的快速發(fā)展,公司企業(yè)越來越重視部門、組織間的溝通效率,業(yè)務(wù)流程管理(Business Process Management,BPM)技術(shù)可以為跨組織、跨部門業(yè)務(wù)集成等方面提供靈活有效的管理方式,因此,業(yè)務(wù)流程管理系統(tǒng)在企業(yè)中應(yīng)用越來越廣泛。但是隨著企業(yè)的發(fā)展,業(yè)務(wù)的不斷增多,許多企業(yè)在實(shí)施BPM技術(shù)的過程中都面臨耗費(fèi)大、缺乏擴(kuò)展性以及響應(yīng)效率低的問題。云計(jì)算的特點(diǎn)恰好為企業(yè)在實(shí)施業(yè)務(wù)流程管理過程中遇到的問題提供解決方案。本文將結(jié)合BPM技術(shù)和云計(jì)算技術(shù)來解決傳統(tǒng)BPM面臨的諸多問題。通過對(duì)云工作流任務(wù)調(diào)度、云服務(wù)相關(guān)的QoS、負(fù)載反饋相關(guān)技術(shù)和理論的研究后,本文主要從以下幾點(diǎn)進(jìn)行研究:首先提出基于云平臺(tái)的分布式業(yè)務(wù)流程引擎模型,該模型采用主從架構(gòu),由任務(wù)監(jiān)控節(jié)點(diǎn)和任務(wù)服務(wù)節(jié)點(diǎn)組成,運(yùn)用Hadoop平臺(tái)中的分布式文件系統(tǒng)來存儲(chǔ)流程定義文件;然后在該模型的基礎(chǔ)上進(jìn)行任務(wù)調(diào)度算法的研究,提出基于QoS的任務(wù)分配算法和基于負(fù)載反饋的延遲調(diào)度算法�；赒oS的任務(wù)預(yù)調(diào)度算法是指通過用戶對(duì)流程服務(wù)的QoS需求,把QoS需求考慮在任務(wù)分配過程中,運(yùn)用遺傳算法計(jì)算出任務(wù)的分配策略,提高整個(gè)系統(tǒng)的任務(wù)吞吐量;同時(shí)為了盡可能保證每個(gè)業(yè)務(wù)流程任務(wù)在服務(wù)節(jié)點(diǎn)執(zhí)行時(shí)可以獲得充足的資源,又提出基于負(fù)載反饋的延遲調(diào)度算法,重點(diǎn)研究調(diào)度時(shí)機(jī)的選擇,優(yōu)化任務(wù)在服務(wù)節(jié)點(diǎn)的執(zhí)行次序,將最迫切的請(qǐng)求優(yōu)先調(diào)度。最后,通過實(shí)驗(yàn)對(duì)比運(yùn)用遺傳算法和順序分配算法在相同流程定義的情況下全部任務(wù)的完成時(shí)間,并對(duì)結(jié)果進(jìn)行分析總結(jié),實(shí)驗(yàn)結(jié)果表明,運(yùn)用遺傳算法生成的分配策略要比順序分配執(zhí)行的時(shí)間更短。同樣,對(duì)運(yùn)用負(fù)載反饋的延遲調(diào)度算法進(jìn)行實(shí)驗(yàn)數(shù)據(jù)分析,實(shí)驗(yàn)表明該算法能夠有效的平衡資源,提高資源利用率。
[Abstract]:In recent years, with the rapid development of information technology, companies increasingly attach importance to departments, inter-organizational communication efficiency, business process management (Business Process Management,BPM (business process management) technology for cross-organization. Cross-departmental business integration provides flexible and effective management methods, so business process management systems are more and more widely used in enterprises. However, with the development of enterprises and the increasing of business, many enterprises are faced with the problems of high cost, lack of scalability and low response efficiency in the process of implementing BPM technology. The characteristics of cloud computing provide solutions to the problems that enterprises encounter in the process of implementing business process management. This article will combine BPM technology and cloud computing technology to solve many problems that traditional BPM faces. After studying the technology and theory of cloud workflow task scheduling and cloud service-related QoS, load feedback, this paper mainly focuses on the following aspects: firstly, a distributed business process engine model based on cloud platform is proposed, which is based on cloud platform. The model adopts master-slave architecture, which consists of task monitoring node and task service node. The distributed file system in Hadoop platform is used to store process definition files. Then the task scheduling algorithm based on this model is studied and the task assignment algorithm based on QoS and the delay scheduling algorithm based on load feedback are proposed. The task pre-scheduling algorithm based on QoS is to calculate the task allocation strategy by using genetic algorithm to improve the task throughput of the whole system by taking the QoS requirement into account in the process of task assignment through the QoS requirement of the user to the process service. At the same time, in order to ensure that every business process task can obtain sufficient resources when the service node is executed, a delay scheduling algorithm based on load feedback is proposed, and the selection of scheduling timing is emphasized. Optimizes the execution order of tasks at the service node and prioritizes the most urgent requests. Finally, the completion time of all tasks is compared by genetic algorithm and sequential assignment algorithm under the same process definition, and the results are analyzed and summarized. The experimental results show that: The allocation strategy generated by genetic algorithm takes less time than sequential allocation. In the same way, the experimental data of the delay scheduling algorithm based on load feedback is analyzed. The experimental results show that the algorithm can effectively balance the resources and improve the utilization rate of the resources.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP301.6;TP393.09

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 魯曉霞;周忠和;;云計(jì)算中主任務(wù)調(diào)度算法的優(yōu)化研究[J];計(jì)算技術(shù)與自動(dòng)化;2011年04期

2 祿樂濱,劉明東;一種基于函數(shù)的多任務(wù)調(diào)度算法[J];空軍工程大學(xué)學(xué)報(bào)(自然科學(xué)版);2000年02期

3 阮幼林 ,劉干 ,朱光喜 ,盧小峰;一個(gè)基于復(fù)制的相關(guān)任務(wù)調(diào)度算法[J];小型微型計(jì)算機(jī)系統(tǒng);2005年03期

4 楊斌;張建軍;;一個(gè)新的基于通信競爭的任務(wù)調(diào)度算法[J];計(jì)算機(jī)工程與應(yīng)用;2007年33期

5 胡同福;王文生;謝能付;;設(shè)備網(wǎng)格中的任務(wù)調(diào)度算法[J];計(jì)算機(jī)工程與設(shè)計(jì);2008年12期

6 周艷慧;張凱;;新的分布式任務(wù)調(diào)度算法[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2008年10期

7 薛繼偉;姜波;劉慶強(qiáng);王征;;基于能力感知的人機(jī)任務(wù)調(diào)度算法[J];計(jì)算機(jī)工程;2009年19期

8 曹曉磊;程?hào)|年;黃萬偉;;基于離散時(shí)間距的在線可重構(gòu)任務(wù)調(diào)度算法[J];小型微型計(jì)算機(jī)系統(tǒng);2010年10期

9 韓曉亞;汪斌強(qiáng);黃萬偉;王保進(jìn);;采用配置完成優(yōu)先策略的可重構(gòu)任務(wù)調(diào)度算法[J];小型微型計(jì)算機(jī)系統(tǒng);2012年03期

10 楊麗;武小年;商可e，

本文編號(hào)：2449113