神經(jīng)網(wǎng)絡(luò)課程報(bào)告

1、File Access Prediction Using Neural NetworksPrashanta Kumar PatraBackgroundBackground存儲(chǔ)介質(zhì)存儲(chǔ)介質(zhì)讀延遲讀延遲寫延遲寫延遲擦除擦除延遲延遲功耗功耗運(yùn)行空閑DRAM55ns55ns無(wú)878mW80mWFlash（SLC）25us200us1.5ms27mW27uWHDD8.5ms9.5ms無(wú)13W9.3mW系統(tǒng)I/O瓶頸：內(nèi)存磁盤解決方案： 緩存緩存：緩存經(jīng)常訪問(wèn)的文件或數(shù)據(jù)塊 預(yù)預(yù)取?。侯A(yù)取將要訪問(wèn)的文件或數(shù)據(jù)塊File Access PredictionFile Access Predictionl靜態(tài)文

2、件訪問(wèn)預(yù)測(cè) First Successor (FS) Last Successor (LS) Stable Successor (SS) Recent Popularity (RP) First Stable Successor (FSS) Predecessor Position (PP)文件請(qǐng)求順序LSSSRPA-AB-ABAB-ABAC-ABACD-ABACDAB-ABACDACD-ABACDACACCCABACDACADA-ABACDACADADCCABACDACADADAA-ABACDACADADADDDABACDACADADAB-ABACDACADADABABDD預(yù)測(cè)精度預(yù)測(cè)覆蓋L

3、S33.3%64.3%SS20%35.7%RP0%28.6%File Access PredictionFile Access Predictionl動(dòng)態(tài)文件訪問(wèn)預(yù)測(cè) neural-network-based file access predictor (NN-based predictor) 目標(biāo)：不同階段選擇最優(yōu)的預(yù)測(cè)算法NN-based PredictorNN-based PredictorNN-based PredictorNN-based Predictor測(cè)試數(shù)據(jù)集測(cè)試數(shù)據(jù)集說(shuō)明說(shuō)明Mozart個(gè)人工作站ives多用戶服務(wù)器barber系統(tǒng)每秒處理很多系統(tǒng)調(diào)用NN-based Pr

4、edictorNN-based Predictor=0 NN預(yù)測(cè)精度比其它三個(gè)預(yù)測(cè)策略要高 當(dāng)不對(duì)錯(cuò)誤的預(yù)取進(jìn)行懲罰時(shí)，NN有效出錯(cuò)率偏高NN-based PredictorNN-based Predictor=1=0.5 當(dāng)對(duì)錯(cuò)誤的預(yù)取進(jìn)行懲罰時(shí)，NN有效丟失率相比其它三個(gè)預(yù)測(cè)策略偏低NN-based PredictorNN-based Predictor 預(yù)測(cè)精度隨著閾值增大而線性增長(zhǎng)NN-based PredictorNN-based Predictor 當(dāng)閾值增大到一定程度，成功預(yù)測(cè)占全部請(qǐng)求的比例急劇下降NN-based PredictorNN-based Predictor=1 當(dāng)

5、閾值增大到一定程度，EMR急劇上升 當(dāng)隱含層數(shù)量由4增長(zhǎng)到40時(shí)，對(duì)NN-based predictor 預(yù)測(cè)精度影響不大 當(dāng)歷史后繼由4個(gè)增長(zhǎng)到10個(gè)時(shí)，對(duì)NN-based predictor 預(yù)測(cè)精度影響不大NN-based PredictorNN-based Predictor進(jìn)一步，直接預(yù)測(cè)將要預(yù)取的文件：l 徑向基網(wǎng)絡(luò)（RBFNN）-高端系統(tǒng)l 概率神經(jīng)網(wǎng)絡(luò)（PNN）-資源有限的系統(tǒng)l 多層感知機(jī)（MLP）-有很多計(jì)算資源的系統(tǒng)RBFNN predictorMLP-based predictorProbabilistic predictorConclusionsConclusionsl提出了NN-based predictor模型，能夠綜合LS SS RP優(yōu)點(diǎn)，使預(yù)測(cè)精度更高，預(yù)測(cè)覆蓋率更大l分別驗(yàn)證了徑向基網(wǎng)絡(luò)、多層感知機(jī)以及概率神經(jīng)網(wǎng)絡(luò)這三個(gè)實(shí)現(xiàn)模型的最佳應(yīng)用場(chǎng)景l(fā)文中沒(méi)有說(shuō)明這種機(jī)制帶來(lái)的系統(tǒng)性能開(kāi)銷，實(shí)現(xiàn)在內(nèi)核工作量太大l測(cè)試數(shù)據(jù)集是1992年之前收集的，數(shù)據(jù)訪問(wèn)特性有可能發(fā)