本文選題：云計算 + 平臺即服務　； 參考：《計算機集成制造系統(tǒng)》2017年05期

【摘要】：為了彌補現(xiàn)有的容器化PaaS平臺監(jiān)控方式在監(jiān)控粒度和深度上的不足,研究并實現(xiàn)了一種智能監(jiān)控方法。在該方法中,監(jiān)控模塊通過動態(tài)調整采樣周期,使采樣點集中于應用系統(tǒng)資源占用狀況波動幅度較大的時段;對于波動幅度超過閾值的情況,采集應用系統(tǒng)調用級別的數(shù)據(jù)并加以分析;將統(tǒng)計分析結果及原始監(jiān)測數(shù)據(jù)按時序整合并對外提供查詢接口,進而為用戶定位異�；虬l(fā)現(xiàn)性能瓶頸提供幫助。相較于現(xiàn)有的監(jiān)控方案,該方法能更加細粒度地反映應用運行時狀態(tài)的變化,同時動態(tài)的數(shù)據(jù)采集機制能有效節(jié)省計算和存儲資源的開銷。
[Abstract]:In order to make up for the deficiency of the existing monitoring methods of container PaaS platform in monitoring granularity and depth, an intelligent monitoring method is studied and implemented. In this method, the monitoring module dynamically adjusts the sampling period to make the sampling point concentrate on the period in which the resource occupation of the application system fluctuates greatly, and for the situation where the fluctuation amplitude exceeds the threshold, The data of application system call level are collected and analyzed, and the statistical analysis results and the original monitoring data are integrated according to the time sequence and the query interface is provided to help the user locate the exception or find the performance bottleneck. Compared with the existing monitoring scheme, this method can reflect the change of the application runtime state more fine-grained, and the dynamic data acquisition mechanism can effectively save the cost of computing and storage resources.
【作者單位】： 浙江大學計算機科學與技術學院;衢州職業(yè)技術學院信息工程學院;
【基金】：浙江省重大科技專項資助項目(2015C01027) 浙江省自然科學基金資助項目(LY17F020014)~~
【分類號】：TP277;TP393.09

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