• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    QoS Prediction Model of Cloud Services Based on Deep Learning

    2022-01-26 00:36:18WenJunHuangPeiYunZhangYuTongChenMengChuZhouYusufAlTurkiandAbdullahAbusorrah
    IEEE/CAA Journal of Automatica Sinica 2022年3期

    WenJun Huang,PeiYun Zhang,,YuTong Chen,MengChu Zhou,,Yusuf Al-Turki,,and Abdullah Abusorrah,

    Dear editor,

    This letter presents a deep learning-based prediction model for the quality-of-service (QoS) of cloud services.Specifically,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual evaluations.The multi-metric features include global,local,and individual ones.Experimental results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

    Cloud computing provides users with fast and secure cloud services,called “service” for short.With the rapid development of cloud computing,the number of cloud-based services continues to increase.However,it is difficult for users to choose services from lots of candidates to meet their needs.In this case,users must compare their QoS,and then determine the best ones.

    QoS can describe non-functional attributes of a service,which is a key indicator often used to evaluate service performance in cloud computing.Due to the uncertainty of user information (such as network status and personal preferences),when different users call the same services,their QoS may differ.Therefore,accurate prediction of QoS values of services is thus required in order to help users choose the most suitable cloud services.

    Many methods have emerged to predict QoS,most of which are inspired by collaborative filtering for service recommendation.These methods predict missing QoS values by collecting historical information of users or services.However,they only use information from an original user-service QoS matrix,which may ignore some important factors that affect QoS,such as locations.Differences in user information,service characteristics,and network status lead to different QoS.

    With the rapid development of deep learning and computing environments,deep neural network (DNN) technologies have significantly impacted many fields,such as computer vision,data mining,and natural language processing.A DNN has a strong nonlinear fitting ability,which can approximate any nonlinear continuous function.It can extract advanced features from original data after statistical learning on a large amount of data.Thus,it is widely used in many artificial intelligence applications to provide the highest prediction accuracy.DNNs can also be used to accurately predict cloud-service QoS.

    Related work:Significant studies have been devoted to solving this problem in recent years.They result in the four main types of methods: Memory-based,model-based,hybrid collaborative filtering,and neural network-based ones.

    Memory-based collaborative filtering methods only use an original user-service QoS matrix to predict QoS.Zhanget al.[1] propose a QoS prediction method in the field of cloud computing.It learns user features via non-negative matrix factorization (NMF) and utilizes the QoS of similar users to improve prediction accuracy.Although memory-based collaborative filtering methods are easy to implement,they are easily affected by data sparsity.Meanwhile,they have problems such as cold start and poor scalability.

    Model-based collaborative filtering methods are widely used to solve the problems mentioned above.Aiming at predicting candidate services for a real-time service adjustment,Zhuet al.[2] propose an adaptive matrix factorization method for online QoS prediction.

    Hybrid collaborative filtering methods combine memory-based and model-based methods.Since collecting QoS values may cause privacy problems,the studies [3],[4] propose privacy protection strategies to obtain high QoS prediction accuracy while protecting user privacy.These methods offer the advantages of both memory based and model-based methods.However,they have the problem of high computational complexity.

    In recent years,with the development of artificial intelligence,neural networks have been applied to the field of QoS prediction.Using the time correlation of QoS,Xionget al.[5] propose a novel personalized matrix factorization method based on Long Short-Term Memory (LSTM) for online QoS prediction.Chenet al.[6] combine an empirical mode decomposition and multivariate LSTM model to propose a hybrid QoS prediction method.However,their network structures and QoS prediction accuracy have much room for improvement in their data preprocessing and feature extraction.This work aims to make such important improvements.

    Problem statement:Usually,a user can call multiple cloud services,and a cloud service can be called by different users.As the number of cloud services continues to increase,many services offer similar functions.Users hope to choose a service that meets their needs,which can be achieved by choosing the one with the best QoS from similar services.

    After a user calls a cloud service,its QoS value is collected by a cloud system and stored in an original user-service QoS matrix,which is denoted asQ.InQ,rows and columns representusers and services,respectively.Items represent QoS values.qij∈Qrepresents the QoS value of servicejdeployed by useri.Fig.1 shows the QoS values provided after three users call five services.

    Fig.1.An original user-service QoS matrix Q.

    Given that not all users call all cloud services in a cloud system,Qmay have some missing items,which may result in a sparseQ.Due to the similarity among services and among users,missing items can be predicted by using existing/known items inQ.The predicted items are shown in bold in Fig.2.To accurately predict QoS values,we propose a QoS prediction model based on deep learning,which adopts multi-staged multi-metric feature fusion with individual evaluations for the first time.

    Basic concepts:

    ● Multi-metric features: They include global,local,and individual ones.

    Fig.2.A user-service QoS matrix with predicted items.

    ● Local feature matrixG: Based on distance similarity,similar users and similar cloud services are extracted fromand thenGis generated.Local features ? can be obtained fromG.

    ● Individual feature matrices: There are two types of individual feature matrices including matrixUfor users and matrixSfor cloud services.They are obtained by performing NMF on matrixQ.Individual features ? can be extracted from them.

    ● Individual evaluation: It comes from matrix.If the proposed model predicts a QoS value of cloud servicejfor useri,serves as an individual evaluation.

    Proposed prediction model:A new DNN is designed to predict QoS values,which is called multi-staged multi-metric-feature DNN(MM-DNN),as shown in Fig.3.It has four stages.Multi-metric features are fused in different concatenation layers.Stages 1–3 serve to fuse global,local,and individual features,respectively.In each stage,an individual evaluation is used to modify features,which makes the output more accurate.If these features are input together into MM-DNN at the same time in Stage 1,it may cause a problem of excessive values.Before outputting a final predicted QoS value in Stage 4,an individual evaluation is input to further improve the value.A detailed analysis of the four stages is shown as follows:

    Stage 1: Global features are input to the proposed model.Then information with the same size as that of local features is further extracted throughLfully connected layers.The features are modified by concatenating an individual evaluation in a concatenation layer.The forward propagation process at this stage can be expressed as

    where φ() denotes a rectified linear unit,i.e.,φ(x) = max(0,x).? is the concatenation operation.y0is the input of Stage1 in MM-DNN.y2is obtained through the fully connected layer after concatenatingy1and.ykis the output of thekth fully connected layer of Stage 1.αkandβkrepresent the weight and bias of thekth fully connected layer,respectively.yLis the output of Stage 1.

    Stage 2: It consists of two concatenation layers andMfully connected layers.Local features are concatenated in a concatenation layer and fed into a fully connected layer.After concatenating an individual evaluation in a concatenation layer,they are learned through fully connected layers.The process is expressed as follows:

    whereyLand ? are the inputs of Stage 2 in MM-DNN,yL+1is obtained through the fully connected layer after concatenatingyLand?,yL+2is obtained through the fully connected layer after concatenatingyL+1and,yL+zis the output of fully connected layer(L+z),andyL+Mis the output of Stage 2.

    Stage 3: It contains two concatenation layers andZfully connected layers.Individual features are connected in a concatenation layer and learned through a fully connected layer.An individual evaluation is then concatenated in a concatenation layer and fed into fully connected layers.The process is expressed as:

    whereyL+Mand ? are inputs of Stage 3 in MM-DNN,yL+M+1is obtained through the fully connected layer after concatenatingyL+Mand ?,yL+M+2is obtained through the fully connected layer after concatenatingyL+M+1and,yL+M+bis the output of the fully connected layer (L+M+b) of MMDNN,andyL+M+Zis the output of Stage 3.

    Stage 4: It consists of a concatenation layer and a fully connected layer.The goal of the proposed model is to predict the QoS value of cloud servicejfor useri.Thus,an individual evaluationis input and connected in the last concatenation layer and learned to further improve the prediction result through the last fully connected layer.The predicted QoS values are then output.The process is expressed as:

    whereyL+M+Zandare inputs of Stage 4.yL+M+Z+1is obtained through the fully connected layer after concatenatingyL+M+Zand.yL+M+Z+1is the output of Stage 4.It is also the output of MM-DNN.pijis a QoS prediction value of cloud servicejfor userifrom the output of MM-DNN.

    Experiments:Our experiments use an Intel Core i7-11700KF CPU @ 3.60GHz,NVIDIA GeForce RTX3090 GPU,and Windows 10 64bit.We use Python 3.7 and Pytorch 1.8.0 to realize MM-DNN.

    To evaluate the performance of the proposed model,experiments are conducted on a real-world QoS data set of services,which is called WS-DREAM [7].Letμbe the matrix density:

    where ξ is the number of existing items in an original user-service QoS matrixQ.|Q| is the total number of entries inQ.Mean absolute error (MAE) and root mean square error (RMSE) are used as indicators to evaluate prediction accuracy.

    The proposed model is compared with the following methods:Probabilistic matrix factorization (PMF) [8],neighborhood-integrated deep matrix factorization (NDMF) [9],and covering-based web service quality prediction via neighborhood-aware matrix factorization (CNMF) [10].

    Fig.3.The structure of MM-DNN.

    Experimental parameters are set in Table 1.They are obtained through lots of experiments.Given four different matrix densities(5%,10%,15%,and 20%),the MAE and RMSE of the four methods are compared.Tables 2 and 3 show the MAE and RMSE of the response time and throughput (i.e.,two kinds of QoS) of the four methods,respectively.MM-DNN outperforms its peers in terms of prediction accuracy with different matrix densities.The results clearly show that MM-DNN outperforms its peers by 6.1% to 21.2%in response time and by 0.2% to 6.8% in throughput.

    Table 1.Experimental Parameters

    Table 2.Comparison of Response Time

    Table 3.Comparison of Throughput

    Conclusions:This paper presents a QoS prediction model for cloud services based on deep learning and multi-staged multi-metric feature fusion with individual evaluations.A new deep neural network model is constructed to fuse the extracted multi-metric features in multiple stages.At each stage of the model,individual evaluations are used to modify features to improve prediction accuracy.Experimental results show that the proposed method can predict QoS values more accurately than the three compared methods.Our future work plans to use time information to improve the proposed model.Since MM-DNN needs a large amount of data for training,it has limitations when facing a highly dynamic environment.More studies are needed to deal with the related issues [11]–[12].

    Acknowledgments:This work was in part supported by the National Natural Science Foundation of China (61872006),the Startup Foundation for New Talents of NUIST,Institutional Fund Projects (IFPNC-001-135-2020),and the Deanship of Scientific Research (DSR) at King Abdulaziz University,Jeddah,Saudi Arabia under grant no.GCV19-37-1441.

    香蕉精品网在线| 男男h啪啪无遮挡| 国产亚洲91精品色在线| 高清av免费在线| 听说在线观看完整版免费高清| 久久久久九九精品影院| 国产精品熟女久久久久浪| 嫩草影院入口| 视频中文字幕在线观看| av又黄又爽大尺度在线免费看| 日韩制服骚丝袜av| 午夜福利在线在线| 亚洲欧美精品自产自拍| 全区人妻精品视频| 国产熟女欧美一区二区| 夜夜看夜夜爽夜夜摸| 免费看不卡的av| 男女那种视频在线观看| 日韩电影二区| 22中文网久久字幕| 色哟哟·www| 久久久色成人| 国产色婷婷99| 久久久久久九九精品二区国产| xxx大片免费视频| 婷婷色综合www| 亚洲精品国产av蜜桃| 国产一区二区三区综合在线观看 | 久久久久久国产a免费观看| 99久国产av精品国产电影| 国产综合懂色| 人妻夜夜爽99麻豆av| 久久久久久伊人网av| 18禁在线无遮挡免费观看视频| 亚洲国产成人一精品久久久| 亚洲成人av在线免费| 亚洲国产av新网站| 国产亚洲精品久久久com| 乱码一卡2卡4卡精品| 国产男女超爽视频在线观看| 亚洲精品色激情综合| 最后的刺客免费高清国语| 久久精品国产a三级三级三级| 18+在线观看网站| 久久久久久久久久人人人人人人| 亚洲av日韩在线播放| 日韩人妻高清精品专区| 亚洲精品视频女| 三级国产精品欧美在线观看| 国产精品一区二区性色av| 日本-黄色视频高清免费观看| av天堂中文字幕网| 岛国毛片在线播放| 伦理电影大哥的女人| 国产白丝娇喘喷水9色精品| 网址你懂的国产日韩在线| 亚洲色图av天堂| 你懂的网址亚洲精品在线观看| 九九久久精品国产亚洲av麻豆| 色播亚洲综合网| 婷婷色av中文字幕| 久久久久久久久久久免费av| 亚洲美女视频黄频| 午夜福利视频1000在线观看| 赤兔流量卡办理| 男人舔奶头视频| 久久ye,这里只有精品| 三级国产精品片| 国产伦理片在线播放av一区| 少妇被粗大猛烈的视频| 婷婷色综合www| 免费少妇av软件| 日韩欧美 国产精品| 精品久久久久久久久av| 日韩 亚洲 欧美在线| 亚洲av中文字字幕乱码综合| 亚洲成人中文字幕在线播放| 亚洲国产精品成人综合色| 免费黄网站久久成人精品| 国产精品一区二区三区四区免费观看| www.色视频.com| 一区二区三区乱码不卡18| 在线观看人妻少妇| 亚洲一区二区三区欧美精品 | 久久亚洲国产成人精品v| 免费看a级黄色片| 黄色怎么调成土黄色| 欧美 日韩 精品 国产| 99re6热这里在线精品视频| av免费观看日本| 欧美zozozo另类| 别揉我奶头 嗯啊视频| 亚洲成色77777| 亚洲av男天堂| 国产中年淑女户外野战色| 亚洲精品一二三| 中文欧美无线码| 国产高清有码在线观看视频| 老女人水多毛片| 久久女婷五月综合色啪小说 | 又爽又黄无遮挡网站| 91久久精品国产一区二区成人| 国产精品99久久99久久久不卡 | 日日摸夜夜添夜夜爱| 国产精品蜜桃在线观看| 99久久九九国产精品国产免费| 亚洲怡红院男人天堂| 日韩欧美精品免费久久| 国产精品人妻久久久久久| 国产 一区精品| 91精品伊人久久大香线蕉| videos熟女内射| 日本黄大片高清| 国产男女内射视频| a级毛色黄片| 看免费成人av毛片| 一本—道久久a久久精品蜜桃钙片 精品乱码久久久久久99久播 | 国产成人精品福利久久| 成人国产av品久久久| 日韩人妻高清精品专区| 一二三四中文在线观看免费高清| 亚洲在线观看片| 国内少妇人妻偷人精品xxx网站| 亚洲最大成人av| 免费看日本二区| .国产精品久久| 大码成人一级视频| 国产精品伦人一区二区| 国产精品一区二区三区四区免费观看| 一本色道久久久久久精品综合| 日韩精品有码人妻一区| 亚洲人成网站高清观看| 国产淫片久久久久久久久| videossex国产| 日韩亚洲欧美综合| 国产精品久久久久久精品古装| 国产精品久久久久久久久免| 直男gayav资源| 成人美女网站在线观看视频| 精品少妇黑人巨大在线播放| 白带黄色成豆腐渣| 春色校园在线视频观看| 美女视频免费永久观看网站| 日韩欧美一区视频在线观看 | 嫩草影院入口| 婷婷色av中文字幕| 免费av观看视频| 两个人的视频大全免费| 一本色道久久久久久精品综合| 欧美成人a在线观看| 嘟嘟电影网在线观看| 熟女av电影| 午夜福利高清视频| 777米奇影视久久| 婷婷色综合www| 日日啪夜夜爽| 成年版毛片免费区| 成人黄色视频免费在线看| 一级二级三级毛片免费看| 下体分泌物呈黄色| 久久久久久久亚洲中文字幕| 日韩在线高清观看一区二区三区| 欧美区成人在线视频| 亚洲精品自拍成人| 男女下面进入的视频免费午夜| 久久99热这里只有精品18| 日日撸夜夜添| a级毛片免费高清观看在线播放| 欧美zozozo另类| 国产精品熟女久久久久浪| 91精品国产九色| 亚洲不卡免费看| 亚洲国产精品999| 久久女婷五月综合色啪小说 | 国产黄a三级三级三级人| 亚洲av免费高清在线观看| 一级爰片在线观看| 国产一区二区在线观看日韩| 日韩亚洲欧美综合| 国产黄片美女视频| 在线观看国产h片| av线在线观看网站| 一区二区三区精品91| 成人无遮挡网站| 波多野结衣巨乳人妻| 在线观看一区二区三区| 国产人妻一区二区三区在| 特级一级黄色大片| 久久久久久国产a免费观看| 久久久久久久国产电影| 久久久亚洲精品成人影院| 亚洲,一卡二卡三卡| 成人美女网站在线观看视频| 看非洲黑人一级黄片| 99久国产av精品国产电影| 国产精品国产av在线观看| 亚洲真实伦在线观看| 激情 狠狠 欧美| 美女内射精品一级片tv| 精品亚洲乱码少妇综合久久| 黄色一级大片看看| 三级国产精品欧美在线观看| www.色视频.com| 亚洲精品影视一区二区三区av| 99热6这里只有精品| 一级爰片在线观看| 三级经典国产精品| 日韩av不卡免费在线播放| 中国三级夫妇交换| 亚洲av电影在线观看一区二区三区 | 一级a做视频免费观看| 91久久精品国产一区二区成人| 狂野欧美激情性bbbbbb| 国产伦在线观看视频一区| 久久久久久久久大av| 精品国产三级普通话版| 一级毛片我不卡| 国产又色又爽无遮挡免| 自拍欧美九色日韩亚洲蝌蚪91 | 51国产日韩欧美| 三级经典国产精品| 黄色怎么调成土黄色| 中文字幕久久专区| 免费av毛片视频| 99热6这里只有精品| 插逼视频在线观看| 免费大片18禁| 午夜免费男女啪啪视频观看| 大片电影免费在线观看免费| 老师上课跳d突然被开到最大视频| 久久99蜜桃精品久久| 日本欧美国产在线视频| 国产精品.久久久| 欧美国产精品一级二级三级 | 香蕉精品网在线| 欧美精品人与动牲交sv欧美| 禁无遮挡网站| 亚洲成色77777| 听说在线观看完整版免费高清| 国产色爽女视频免费观看| 日本色播在线视频| 麻豆精品久久久久久蜜桃| 国产午夜福利久久久久久| 色视频在线一区二区三区| 欧美97在线视频| 26uuu在线亚洲综合色| 人妻少妇偷人精品九色| 全区人妻精品视频| av国产久精品久网站免费入址| 国产精品不卡视频一区二区| 日韩三级伦理在线观看| 极品少妇高潮喷水抽搐| 亚洲成人中文字幕在线播放| 日本黄色片子视频| 男人舔奶头视频| 久久国内精品自在自线图片| 欧美日韩综合久久久久久| 国产综合精华液| 亚洲三级黄色毛片| 亚洲av免费在线观看| 国产精品久久久久久久久免| 国产黄色免费在线视频| 黄色视频在线播放观看不卡| 免费观看在线日韩| 欧美成人一区二区免费高清观看| 国产欧美亚洲国产| 久久久久久久精品精品| 欧美老熟妇乱子伦牲交| 久久久久久久午夜电影| 联通29元200g的流量卡| 国产成年人精品一区二区| 中文字幕免费在线视频6| 久久久午夜欧美精品| 国产精品熟女久久久久浪| 成人亚洲精品一区在线观看 | av卡一久久| 欧美变态另类bdsm刘玥| 国产成人精品婷婷| 国产成人a区在线观看| 亚洲欧美成人综合另类久久久| 天堂俺去俺来也www色官网| 欧美三级亚洲精品| 最近手机中文字幕大全| 女人久久www免费人成看片| 深爱激情五月婷婷| 国产伦精品一区二区三区四那| av免费在线看不卡| 99久久人妻综合| 国产一区二区三区av在线| 狂野欧美白嫩少妇大欣赏| 寂寞人妻少妇视频99o| 午夜福利视频精品| 成年版毛片免费区| 卡戴珊不雅视频在线播放| 亚洲,欧美,日韩| 久久久久久国产a免费观看| 国产一区有黄有色的免费视频| 视频区图区小说| 啦啦啦啦在线视频资源| 国产亚洲5aaaaa淫片| 美女cb高潮喷水在线观看| 三级经典国产精品| 男男h啪啪无遮挡| 亚洲欧美成人精品一区二区| eeuss影院久久| 国产精品嫩草影院av在线观看| 久久久a久久爽久久v久久| 一个人看的www免费观看视频| 亚洲av成人精品一二三区| 国产精品久久久久久av不卡| 国产精品久久久久久精品古装| 另类亚洲欧美激情| 看黄色毛片网站| 91久久精品电影网| 午夜福利在线在线| 久久人人爽人人爽人人片va| 婷婷色综合www| 亚洲国产精品国产精品| 少妇高潮的动态图| 色吧在线观看| a级毛色黄片| 成人毛片a级毛片在线播放| 婷婷色综合www| 99精国产麻豆久久婷婷| 日韩欧美精品免费久久| 熟女电影av网| 18禁在线无遮挡免费观看视频| 人妻制服诱惑在线中文字幕| 久久综合国产亚洲精品| 精品少妇黑人巨大在线播放| av国产精品久久久久影院| 欧美日本视频| 偷拍熟女少妇极品色| 国产精品人妻久久久久久| 国国产精品蜜臀av免费| 国产淫语在线视频| 国产一区亚洲一区在线观看| 亚洲三级黄色毛片| 国产亚洲午夜精品一区二区久久 | 嫩草影院精品99| 99久久精品一区二区三区| 国产亚洲91精品色在线| 秋霞伦理黄片| 久久久久久国产a免费观看| 成年女人在线观看亚洲视频 | 高清视频免费观看一区二区| 亚洲精品国产色婷婷电影| 可以在线观看毛片的网站| 精品午夜福利在线看| 一级爰片在线观看| 一级片'在线观看视频| 亚洲av成人精品一区久久| 亚洲av.av天堂| 日本三级黄在线观看| 成人国产麻豆网| 成年av动漫网址| 日韩伦理黄色片| 亚洲精品国产av蜜桃| 久久精品国产自在天天线| 男人狂女人下面高潮的视频| av国产免费在线观看| 国产v大片淫在线免费观看| 少妇人妻一区二区三区视频| 欧美丝袜亚洲另类| 深夜a级毛片| 热re99久久精品国产66热6| 日本与韩国留学比较| 国产视频内射| 国产精品福利在线免费观看| 特大巨黑吊av在线直播| 99久久精品热视频| 久久久a久久爽久久v久久| 18禁裸乳无遮挡动漫免费视频 | 听说在线观看完整版免费高清| 亚洲精华国产精华液的使用体验| 亚洲av电影在线观看一区二区三区 | 美女国产视频在线观看| 99热这里只有是精品50| 精品久久国产蜜桃| 欧美日本视频| av国产精品久久久久影院| 九草在线视频观看| 成人无遮挡网站| 国产精品久久久久久久电影| 亚洲av免费高清在线观看| 成人亚洲欧美一区二区av| 97热精品久久久久久| 国产老妇女一区| 插阴视频在线观看视频| 永久网站在线| 久久精品夜色国产| 亚洲成人精品中文字幕电影| 国产欧美日韩一区二区三区在线 | 成人欧美大片| 舔av片在线| 国产毛片在线视频| 免费观看性生交大片5| 少妇高潮的动态图| 亚洲va在线va天堂va国产| av福利片在线观看| 在现免费观看毛片| 国产黄色免费在线视频| 国产精品久久久久久久久免| 日韩制服骚丝袜av| 国产色婷婷99| 中国国产av一级| 特级一级黄色大片| 欧美成人a在线观看| 人体艺术视频欧美日本| 亚洲国产色片| 自拍偷自拍亚洲精品老妇| 青春草国产在线视频| 日韩欧美精品v在线| 晚上一个人看的免费电影| 亚洲欧洲国产日韩| 日本一本二区三区精品| a级毛片免费高清观看在线播放| 免费少妇av软件| 一级毛片 在线播放| 国产精品99久久99久久久不卡 | 一区二区三区精品91| 听说在线观看完整版免费高清| 中文字幕免费在线视频6| 身体一侧抽搐| 天天躁日日操中文字幕| 人人妻人人看人人澡| 国产成年人精品一区二区| 国产精品蜜桃在线观看| 永久免费av网站大全| 啦啦啦中文免费视频观看日本| 99久久人妻综合| 国产熟女欧美一区二区| 一级毛片久久久久久久久女| 最近最新中文字幕大全电影3| 黄片无遮挡物在线观看| 99久久精品国产国产毛片| 搞女人的毛片| 日本猛色少妇xxxxx猛交久久| 久热久热在线精品观看| 全区人妻精品视频| 69av精品久久久久久| 免费av观看视频| 在线观看三级黄色| 亚洲精品乱码久久久v下载方式| 国产极品天堂在线| 在线 av 中文字幕| 三级经典国产精品| 国国产精品蜜臀av免费| 国产高清三级在线| 少妇丰满av| 日日啪夜夜爽| 国产一区二区亚洲精品在线观看| 伦精品一区二区三区| 亚洲国产精品成人综合色| 高清午夜精品一区二区三区| 午夜激情福利司机影院| 3wmmmm亚洲av在线观看| 国产日韩欧美在线精品| 美女xxoo啪啪120秒动态图| 久久久久国产网址| 国产亚洲av嫩草精品影院| 22中文网久久字幕| 特大巨黑吊av在线直播| 丰满人妻一区二区三区视频av| 精品一区二区三卡| 99九九线精品视频在线观看视频| 亚洲av二区三区四区| 亚洲欧美清纯卡通| 日产精品乱码卡一卡2卡三| 中文字幕免费在线视频6| 一本—道久久a久久精品蜜桃钙片 精品乱码久久久久久99久播 | 日韩欧美精品v在线| 久久精品久久精品一区二区三区| 亚洲精品国产av成人精品| 中文字幕久久专区| 精品国产三级普通话版| 三级国产精品片| 在线观看av片永久免费下载| 久久久久网色| 欧美区成人在线视频| 欧美xxxx黑人xx丫x性爽| 国产探花极品一区二区| 视频中文字幕在线观看| 欧美+日韩+精品| 在线观看国产h片| 欧美高清成人免费视频www| 久久久久久久午夜电影| 男女无遮挡免费网站观看| 国产男人的电影天堂91| 男女那种视频在线观看| 七月丁香在线播放| 亚洲天堂国产精品一区在线| 久久99热这里只有精品18| 成年av动漫网址| 搡女人真爽免费视频火全软件| 国产v大片淫在线免费观看| 久久久久久久久大av| 欧美变态另类bdsm刘玥| 性插视频无遮挡在线免费观看| 成人毛片60女人毛片免费| 麻豆乱淫一区二区| 日日撸夜夜添| 亚洲精华国产精华液的使用体验| 18禁在线无遮挡免费观看视频| 午夜福利高清视频| 亚洲精品国产av蜜桃| 一级片'在线观看视频| 欧美+日韩+精品| 亚洲欧美清纯卡通| 中文欧美无线码| videos熟女内射| 舔av片在线| 亚洲成人av在线免费| 最近2019中文字幕mv第一页| 免费播放大片免费观看视频在线观看| 免费人成在线观看视频色| 一区二区三区四区激情视频| 两个人的视频大全免费| 欧美 日韩 精品 国产| 精品午夜福利在线看| 欧美老熟妇乱子伦牲交| 亚洲综合精品二区| 国产亚洲5aaaaa淫片| 日日啪夜夜爽| 成人漫画全彩无遮挡| 最近中文字幕2019免费版| 免费黄色在线免费观看| 美女国产视频在线观看| 97超碰精品成人国产| 六月丁香七月| 久久久午夜欧美精品| 久久久久久久久久久丰满| 综合色av麻豆| 国产男女超爽视频在线观看| 久久人人爽人人片av| 亚洲av男天堂| 日韩成人伦理影院| 日韩欧美精品免费久久| 久久99蜜桃精品久久| 国产精品麻豆人妻色哟哟久久| 综合色丁香网| 制服丝袜香蕉在线| 美女视频免费永久观看网站| 2018国产大陆天天弄谢| 黄色一级大片看看| 最近的中文字幕免费完整| 草草在线视频免费看| 久久久精品免费免费高清| 毛片一级片免费看久久久久| 亚洲精品自拍成人| 又爽又黄a免费视频| 一级二级三级毛片免费看| 免费观看在线日韩| 国产精品伦人一区二区| 少妇的逼好多水| 亚洲色图av天堂| 久久久久国产精品人妻一区二区| 亚洲人与动物交配视频| 精品国产乱码久久久久久小说| 免费人成在线观看视频色| 国产成人免费无遮挡视频| 天天躁夜夜躁狠狠久久av| 精品亚洲乱码少妇综合久久| 欧美性猛交╳xxx乱大交人| 日本-黄色视频高清免费观看| 91精品国产九色| 2018国产大陆天天弄谢| .国产精品久久| 国产亚洲一区二区精品| 各种免费的搞黄视频| 免费看光身美女| 人妻少妇偷人精品九色| 亚洲国产精品专区欧美| 国产午夜精品一二区理论片| 亚洲va在线va天堂va国产| 亚洲精品日韩av片在线观看| 一级毛片aaaaaa免费看小| 18禁在线无遮挡免费观看视频| 只有这里有精品99| 日韩视频在线欧美| 亚洲欧美日韩卡通动漫| 亚洲激情五月婷婷啪啪| 真实男女啪啪啪动态图| 国语对白做爰xxxⅹ性视频网站| 黄片无遮挡物在线观看| 日韩电影二区| 欧美成人一区二区免费高清观看| 亚洲成人精品中文字幕电影| 精品视频人人做人人爽| 久久午夜福利片| 亚洲欧美日韩无卡精品| 精品视频人人做人人爽| 国模一区二区三区四区视频| 欧美日韩视频精品一区| av专区在线播放| 插阴视频在线观看视频| 欧美日韩精品成人综合77777| av专区在线播放| 国语对白做爰xxxⅹ性视频网站| 日本色播在线视频| 国产 一区 欧美 日韩| 激情五月婷婷亚洲| 黄色一级大片看看| 国产成人免费无遮挡视频| 丝袜脚勾引网站| 91午夜精品亚洲一区二区三区| 免费在线观看成人毛片| 国产真实伦视频高清在线观看| 熟妇人妻不卡中文字幕| 午夜激情久久久久久久| 小蜜桃在线观看免费完整版高清| av一本久久久久| 免费大片黄手机在线观看| 大香蕉97超碰在线| 亚洲av不卡在线观看|