本文選題：云計(jì)算 + 性能異常　； 參考：《華中科技大學(xué)》2016年碩士論文

【摘要】：云服務(wù)軟件需要對(duì)外提供不間斷的在線服務(wù),但是由于功能的復(fù)雜和代碼規(guī)模的龐大使得軟件中難以避免存在bug,如果這些bug引發(fā)了性能異常問題,開發(fā)者將很難對(duì)這些性能異常進(jìn)行診斷。這些性能異常問題只有在特定的條件下才會(huì)出現(xiàn),比如特殊的用戶輸入,系統(tǒng)配置等。在其他運(yùn)行環(huán)境,將很難再次重現(xiàn)這些問題,已有的離線調(diào)試工具將無法使用。性能異常出現(xiàn)的時(shí)候往往不產(chǎn)生額外的錯(cuò)誤信息,無法給開發(fā)者提供更多的幫助。因此,云環(huán)境中軟件性能異常診斷機(jī)制研究具有重要意義。云環(huán)境中軟件性能異常診斷系統(tǒng)旨在解決軟件在線提供服務(wù)時(shí)出現(xiàn)的性能異常問題。首先,設(shè)計(jì)了系統(tǒng)調(diào)用劃分機(jī)制,能夠?qū)④浖\(yùn)行時(shí)產(chǎn)生的系統(tǒng)調(diào)用劃分為具有一定語(yǔ)義的執(zhí)行單元;然后,設(shè)計(jì)了基于自組織映射模型自動(dòng)建模方法,能夠自動(dòng)構(gòu)建軟件運(yùn)行時(shí)的系統(tǒng)調(diào)用行為模型;最后,設(shè)計(jì)了基于距離的異常執(zhí)行單元檢測(cè)方法,以及基于多數(shù)票決的異常系統(tǒng)調(diào)用推斷方法,能夠準(zhǔn)確捕捉系統(tǒng)調(diào)用行為的變化,通過比較不同執(zhí)行單元之間的差異,檢測(cè)出異常的執(zhí)行單元,推斷出性能異常的相關(guān)的系統(tǒng)調(diào)用,為開發(fā)者解決這個(gè)性能異常問題提供幫助。實(shí)驗(yàn)結(jié)果表明,云環(huán)境中軟件性能異常診斷系統(tǒng)可以有效的診斷5個(gè)開源軟件的實(shí)際性能bug,能夠在平均7分鐘之內(nèi)推斷出與性能異常最相關(guān)的系統(tǒng)調(diào)用。同時(shí),它對(duì)測(cè)試的服務(wù)軟件產(chǎn)生的運(yùn)行時(shí)開銷平均只有2.2%。
[Abstract]:Cloud service software needs to provide continuous online services, but because of the complexity of functions and the size of the code, it is difficult to avoid the existence of bugs in the software, if these bug cause abnormal performance problems, Developers will find it difficult to diagnose these performance exceptions. These performance anomalies occur only under specific conditions, such as special user input, system configuration, and so on. In other environments, it will be difficult to reproduce these problems again, and existing offline debugging tools will not be available. Performance exceptions often do not generate additional error messages and do not provide more help to developers. Therefore, it is of great significance to study the mechanism of software performance anomaly diagnosis in cloud environment. The purpose of software performance anomaly diagnosis system in cloud environment is to solve the problem of performance anomaly when software provides online service. Firstly, the partition mechanism of system call is designed, which can divide the system call generated by software runtime into execution units with certain semantics. Then, an automatic modeling method based on self-organization mapping model is designed. The system call behavior model of software runtime can be constructed automatically. Finally, the method of detecting exception execution unit based on distance and the method of extrapolation of exception system call based on majority vote are designed. Can accurately capture the system call behavior changes, by comparing the differences between different execution units, detect the exception of the execution unit, extrapolate the performance exception related system calls, Provides help for developers to solve this performance exception problem. The experimental results show that the software performance anomaly diagnosis system in the cloud environment can effectively diagnose the actual performance of five open source software bugs.It can infer the system calls most relevant to the abnormal performance in an average of 7 minutes. At the same time, it produces an average runtime overhead of only 2.2% for the service software being tested.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP311.53

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本文編號(hào)：2026506