集群性能評估系統(tǒng)專家決策子系統(tǒng)的設計與實現(xiàn)
發(fā)布時間：2019-06-27 15:47
【摘要】：性能分析對構建高效率的計算系統(tǒng)意義重大,但隨著云計算的流行,計算系統(tǒng)日益復雜,性能分析工作變得十分困難。而現(xiàn)有的性能分析工具,主要采取用戶行為模擬等手段,關注用戶體驗,監(jiān)測響應時間、錯誤率等參數(shù),弱化了分析部分;同時只適用于Web類用戶交互應用,無法應用于諸如科學計算等領域。因此,下一代性能分析工具的設計開發(fā)工作迫在眉睫。本文基于機器學習算法設計了一種深入挖掘應用各項性能指標內(nèi)在聯(lián)系,統(tǒng)籌多指標聯(lián)合定位應用問題的專家決策系統(tǒng),解決了現(xiàn)有分析工具定位分析問題能力不足,適用場景有限的問題。本文首先介紹了背景知識及開發(fā)實現(xiàn)所涉及的相關技術,并調(diào)研和說明了現(xiàn)有分析工具的功能和特點。通過分析專家決策系統(tǒng)在整個性能評估系統(tǒng)中的位置,結合RTC應用分析實例,得出決策系統(tǒng)的工作場景和總體需求。然后,提出了專家決策系統(tǒng)的總體結構,并對、決策系統(tǒng)的關鍵流程進行了闡述,并給出了決策系統(tǒng)的內(nèi)外接口。在此基礎上,設計了專家決策系統(tǒng)的類圖,并針對各個模塊中涉及的關鍵問題,給出了詳細的流程設計,完成了決策系統(tǒng)原型的設計和實現(xiàn)工作。隨后對專家決策系統(tǒng)進行了功能測試和驗證,詳細的介紹了關鍵功能模塊的測試用例,測試結果表明功能原型基本符合需求。最后,對專家決策系統(tǒng)的開發(fā)設計工作進行了總結,并指出了下一步的幾個主要研究方向。
[Abstract]:Performance analysis is of great significance to the construction of high-efficiency computing system, but with the popularity of cloud computing, the computing system is becoming more and more complex, and the performance analysis work becomes very difficult. And the existing performance analysis tool mainly adopts the means of user behavior simulation, so as to pay attention to the parameters such as user experience, monitoring response time, error rate and the like, weaken the analysis part, and can only be applied to the interactive application of the Web user, and can not be applied to the fields such as scientific calculation and the like. Therefore, the design and development of the next-generation performance analysis tool is urgent. This paper, based on the machine learning algorithm, designs an expert decision-making system for deep excavation and application of various performance indexes and integrated multi-index joint positioning and application, and solves the problem that the existing analysis tool's ability to locate and analyze the problem is not enough and the scene is limited. This paper first introduces the background knowledge and the related technology involved in the development, and studies and explains the functions and characteristics of the existing analysis tools. By analyzing the position of the expert decision-making system in the whole performance evaluation system, combined with the analysis example of the RTC application, the working scene and the overall demand of the decision-making system are obtained. Then, the overall structure of the expert decision-making system is put forward, and the key process of the decision-making system is described, and the internal and external interfaces of the decision-making system are given. On this basis, the class diagram of the expert decision-making system is designed, and the detailed process design is given for the key problems involved in each module, and the design and implementation of the prototype of the decision-making system are completed. Then, the function test and verification of the expert decision-making system are carried out, and the test cases of the key function modules are described in detail. The test results show that the functional prototype basically conforms to the requirements. Finally, the development and design of the expert decision-making system are summarized, and the main research directions of the next step are pointed out.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP182;TP311.52
本文編號：2506929
[Abstract]:Performance analysis is of great significance to the construction of high-efficiency computing system, but with the popularity of cloud computing, the computing system is becoming more and more complex, and the performance analysis work becomes very difficult. And the existing performance analysis tool mainly adopts the means of user behavior simulation, so as to pay attention to the parameters such as user experience, monitoring response time, error rate and the like, weaken the analysis part, and can only be applied to the interactive application of the Web user, and can not be applied to the fields such as scientific calculation and the like. Therefore, the design and development of the next-generation performance analysis tool is urgent. This paper, based on the machine learning algorithm, designs an expert decision-making system for deep excavation and application of various performance indexes and integrated multi-index joint positioning and application, and solves the problem that the existing analysis tool's ability to locate and analyze the problem is not enough and the scene is limited. This paper first introduces the background knowledge and the related technology involved in the development, and studies and explains the functions and characteristics of the existing analysis tools. By analyzing the position of the expert decision-making system in the whole performance evaluation system, combined with the analysis example of the RTC application, the working scene and the overall demand of the decision-making system are obtained. Then, the overall structure of the expert decision-making system is put forward, and the key process of the decision-making system is described, and the internal and external interfaces of the decision-making system are given. On this basis, the class diagram of the expert decision-making system is designed, and the detailed process design is given for the key problems involved in each module, and the design and implementation of the prototype of the decision-making system are completed. Then, the function test and verification of the expert decision-making system are carried out, and the test cases of the key function modules are described in detail. The test results show that the functional prototype basically conforms to the requirements. Finally, the development and design of the expert decision-making system are summarized, and the main research directions of the next step are pointed out.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP182;TP311.52
【參考文獻】
相關期刊論文 前1條
1 吳江;黃晟青;蔡駿;;互聯(lián)網(wǎng)購物網(wǎng)站用戶體驗設計研究[J];包裝工程;2012年08期
，本文編號：2506929
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