【摘要】：隨著越來越多的在線業(yè)務(wù)被遷移到基于云的平臺上,如何檢測云平臺上在線業(yè)務(wù)的異常運(yùn)行狀態(tài)成為了一個重要的問題�，F(xiàn)有方法通過分析在線業(yè)務(wù)的實(shí)時負(fù)載數(shù)據(jù)來判斷業(yè)務(wù)是否存在異常,在應(yīng)對由程序異�；蛲话l(fā)用戶訪問引起的異常負(fù)載時存在準(zhǔn)確率低、誤報率高的問題。該文提出并實(shí)現(xiàn)了一種面向云計(jì)算在線業(yè)務(wù)的異常負(fù)載檢測方法。該方法利用小波分析技術(shù),將原始負(fù)載數(shù)據(jù)分解成頻率不同的多條曲線,并利用統(tǒng)計(jì)分析技術(shù),通過檢測各個頻率上的異常增長或降低來判斷負(fù)載是否存在異常。實(shí)驗(yàn)結(jié)果表明:同現(xiàn)有方法相比,該方法更準(zhǔn)確,同時可以大大降低誤報率。
[Abstract]:With more and more online services being migrated to cloud-based platforms, how to detect the abnormal running state of online services on cloud platforms has become an important issue. The existing methods analyze the real-time load data of online services to determine whether there is an anomaly in the service. There are some problems such as low accuracy and high false alarm rate when dealing with abnormal load caused by program exception or unexpected user access. In this paper, an anomaly load detection method for cloud computing online services is proposed and implemented. The method uses wavelet analysis technology to decompose the original load data into multiple curves with different frequencies. By using statistical analysis technology, the abnormal growth or decrease of each frequency is detected to determine whether the load is abnormal or not. The experimental results show that the proposed method is more accurate and can greatly reduce the false alarm rate than the existing methods.
【作者單位】： 清華大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)系;
【基金】：國家國際科技合作專項(xiàng)項(xiàng)目(2013DFB10070)
【分類號】：TP274;TP3

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