• <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 ?

    Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

    2022-08-24 03:28:40TahirAlyasIqraJavedAbdallahNamounAliTufailSamiAlshmranyandNadiaTabassum
    Computers Materials&Continua 2022年5期

    Tahir Alyas,Iqra Javed,Abdallah Namoun,Ali Tufail,Sami Alshmrany and Nadia Tabassum

    1Department of Computer Science,Lahore Garrison University,Lahore,54000,Pakistan

    2Faculty of Computer and Information Systems,Islamic University of Madinah,Madinah,42351,Saudi Arabia

    3Department of Computer Science,Virtual University of Pakistan,Lahore,54000,Pakistan

    Abstract: Efforts were exerted to enhance the live virtual machines (VMs)migration, including performance improvements of the live migration of services to the cloud.The VMs empower the cloud users to store relevant data and resources.However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs.Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process.Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent.By virtualizing the network resources,the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing.Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in crossdata-center situations.This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold,thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement(SLA).In this research,we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes.This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.

    Keywords: Cloud computing;IaaS;data centre;storage;performance analysis;live migration

    1 Introduction

    Infrastructure as a service (IaaS) is a network service that enables access to high-level APIs in order to substructure physical resources, such as computing resources, security, data partitioning,scaling,and backup.System virtualization is the basic methodology in the Infrastructure as a Service cloud computing model, where the virtual machines are executed within a hypervisor, for example,Oracle VirtualBox and VMware ESX/ESXi.The hypervisor pool of resources can take the form of an operating system or a computing resource that supports many virtual machines.It can be based on different customer needs and reduce the overhead of services.IaaS typically involves the use of cloud computing technologies, such as “OpenStack and Apache CloudStack”.Multiple virtual machines(VMs)can share the underlying physical machines to virtualization tools.Multiple virtual machines(VMs) operating on the same physical disk, known as VM consolidation, will increase cloud data center resource usage.Since the cloud infrastructure has become a popular solution,it is more critical than ever to take advantage of success opportunities and boost the cloud platform productivity[1].

    The choice of major drivers is Linux containers that run on separate partitions in a single Linux kernel,running directly on the physical hardware.Linux kernels and namespaces are the core technologies of Linux kernels used to separate,protect,and manage containers.The included offers better performance than virtualization,as there is no cost.In addition,the container capacity increases dramatically and automatically with the calculation method, eliminates the problem of oversupply,and achieves usage-based payment.IaaS clouds typically provide a myriad of resources,for example VM disk-image library, VLANs, firewalls, resources storage, load balancers, and software bundles.Other services include basic processing, storage, network and computing resources to enable users to run any software, including operating systems.Although the users do not control or manage the underlying cloud structures, they can control the operating system, distribution applications and limited management of some network components[2].

    According to the“Internet Engineering Task Force(IETF)”,the basic model of a cloud service is a model that provides subscribers with an IT infrastructure(virtual machines and other resources)as a service provider.Cloud service providers offer the infrastructure services as computing resources that are needed from the high capacity of Linux containers.For longer connections, customers can use the Internet or a cloud operator (i.e., dedicated virtual network).To deploy applications, cloud users install their operating system images and application software on the cloud infrastructures.In the IaaS service model, cloud users can rent and maintain an operating system, virtual servers,and various types of networking software.Cloud service providers usually charge for IaaS services based on utility calculations where the costs reflect the amount of capital allocated and resources used.Infrastructure as a service (IaaS) is a form of cloud computing that provides users with basic computing, networking, and storage resources on-demand, over the Internet, and for a fee.IaaS enables end users to expand their resources as needed,reducing the need for high capital expenditures or unnecessary“clean”infrastructure,especially during peak hours.Compared to the PaaS and SaaS models(even newer computer models such as containers and without a server),IaaS offers the lowest level of cloud resource management[3].

    IaaS is a major part of modern cloud computing and has become the mainstream abstract model for many types of loads.With the advancement of new technologies such as containers and the Internet and the proliferation of small-scale applications, IaaS remains the foundation, but more prevalent than ever.Businesses choose IaaS because it is usually easier,faster,and cost-effective to implement workloads without the need to purchase,manage,and support the hardware infrastructure.With IaaS,companies can rent or lease the required infrastructure from other companies[4].

    IaaS is an effective model for dealing with temporary, experimental, or unexpectedly altered workloads.For example,if a company develops a new software product,it may be more cost-effective if an IaaS provider is hired to maintain and test the applications.Once the new software is thoroughly tested and optimized,organizations may remove it from the IaaS environment for a more traditional use.Moreover,companies can opt for the long-term use of IaaS of their software,especially that longterm hosting costs are usually lower.Normally,IaaS customers pay for each use,usually on an hourly,weekly,or monthly basis.Some IaaS providers also charge customers based on the number of hours spent using the virtual machines.Licenses as such exclude the cost of financing the internal use of hardware and software.A VM’s memory operations are divided into four categories:disk write,disk read, memory write, and memory read.In the pre-copy and post-copy models, we summarize the performance impacts of various processes during migration[5].

    We have witnessed a constant increase in the adaptation of the cloud computing environments to host a diverse range of software applications,including web files,virtual reality,big data,and scientific computing[6].Subsequently,the provision of cloud services that satisfy a certain level of quality(i.e.,QoS) has become a critical matter with time.Recent works focused on optimizing energy use and reducing service latency dynamically to ensure maximum benefits are delivered to the cloud computing providers and users.In this research,we aim to design an architecture that can strengthen and enhance the performance of cloud computing during the migration of data in data centres[7].Cloud service providers managing and maintaining data that has been migrated to the one data center to another data center.In the cloud system, storage services are available on demand, with capacity increasing and reducing as needed.Cloud storage eliminates the need for businesses to purchase, operate, and maintain their own storage infrastructure.In Fig.1 depicts a memory representation in virtualization,where the guest OS and hypervisor are hosted on a host machine, which shares the guest virtual memory and guest physical memory.

    Figure 1:A memory representation in the cloud environment

    A society cloud in figuring is a communication effort in which the structure is shared between a couple of relationships from a specific gathering with fundamental concerns(e.g.,security,consistency,ward,etc.),paying little respect to whether regulated inside or by a pariah and encouraged inside or remotely.This is controlled and used by a social occasion of affiliations that have shared interest,the costs are spread over fewer customers than an open cloud (however more than a private cloud), so only a segment of the cost venture stores capacity of dispersed registering make sense of it[8].

    From a migration point of view, a VM can be separated into three components namely:1)operating states(including memory data,CPU states,all external system states,and so on),2)storage data(e.g.,disk data),and 3)network links with the VM and its users.In essence,the live VM migration endeavours to move the afore-named components from a source to a destination.Data and real-time status transfer from the memory of a VM to the new host must be accomplished to prevent any interruption to the services running in the migrated VM.These data include CPU states, memory data,external system buffer data,and so on.Memory storage migration is the term used to describe the transition of operating states.Data transfer from storage involves moving a VM’s disk image to a new host.This task is required when the source and destination hosts do not share a storage pool.When a virtual machine(VM)is relocated to a different site,a plan is required to redirect the users’network connections to the new location and maintain the network link[9].

    The live migration in a cloud environment from a source host to a destination host is highly reliant on the network connection continuity between the user end and virtual machine.The data migration process shifts the data from the source host to the destination host as shown in Fig.2.The main dependency is connectivity and complete migration of data from the source host to the destination host.When a server is overloaded,its lifetime is shortened and quality of its services(QoS)deteriorates as a result.On the other hand, servers that are underutilised cause a waste of resources.Live VM migration means that all servers in a data center run at the same time with no degradation to the QoS.When a live VM replication over a WAN is carried out,load balancing can also be achieved between many geo-distributed data centers[10].

    Figure 2:Cloud live migration from a source host to a destination host

    In a data center,virtual machines are built and dismantled on a regular basis.Furthermore,any of the VMs may be inactive or suspended.If the servers in a data center are not sufficiently consolidated,the VMs would be in a mess.VMs are live migrated during server consolidation for energy efficiency(using as few servers as possible)or connectivity(locating the VMs communicating heavily with each other on the same server to reduce the network traffic)[11].

    2 Related Works

    In[12],authors identified specific variables that affect real-time migration performance using the OpenStack platform in the system and network environments, such as real-time static adaptation metric comparisons, parallel migration performance comparisons, and SDN sequencing.From a QoS perspective, response time and server response patterns, duplication, hybridization, and selfconvergence-based migration need to be evaluated.A real-time mathematical model of the final transmission can be created to understand the static adjustment algorithm in OpenStack and the parallel and continuous transmission costs on the same network path.

    In order to adjust the working time,the idle settings(maximum idle time,adjustment steps,and delay) need to be adjusted.This can help to get the best transmission results.If off-network costs(such as pre and post-transmission workload)account for a large proportion of the total transmission time, simultaneous transmission should be chosen to reduce response time and idle time.In cloudbased software centers with a software-defined network (SDN), real-time VM transmission is a key technology for promoting resource management and tolerance.Although many studies focus on the transmission of a network-sensitive real-time virtual machine to cloud computing, however, some variables such as service level objective for backup storage data,increase resource usage,load balancing of VMs that affect real-time transmission performance are largely ignored.In addition, while SDN offers more traffic flexibility,SDN delays directly affect live broadcasts[13].

    Authors in [14] argued that live migration of VMs and virtual network functions (VNFs) in the SDN enabled cloud data centres is the most widely used approach.However, they claimed that there was a risk of performance degradation if this live migration was performed in a random order.Therefore,they suggested a group of algorithms to come up with a scheduled live migration.

    In another article [15], authors reviewed real-time migration literature and analyzed various guidelines.Firstly,they classified real-time VM migration types(single,multi,and hybrid).Next,they classified VM migration technology based on copy methods(copying,duplication,aggregation,and compression)and contextual awareness(dependency,software pages,dirty pages,and default pages),and lastly, they evaluated different VM real-time migration technologies.Moreover, they discussed various performance metrics,such as application service downtime,total transmission time,and the total amount of data sent.A similar study was conducted in [16] where authors proposed an autoscaling technique which they called Robust Hybrid Auto-Scaler (RHAS).They argued that RHAS could predict the future load on cloud infrastructure that in turn could help manage the resources optimally and could have positive impact on cost,response time,SLA violations and CPU utilization.

    Authors in [17] briefly introduced the security threats associated with real-time migration and divided them into three different categories i.e.,surveillance aircraft,data aircraft and costume units.They also explained some crucial security requirements and possible solutions to reduce the potential attacks.To improve the performance of real-time migration,specific gaps and research challenges were identified.The importance of this study is that it creates a context for real-time migration technology and conducts an in-depth insights that helps cloud professionals and researchers to further explore the challenges and provide the best solutions [18].Similar efforts were seen in [19] where authors highlight the vulnerability of VM against co-resident attack.Authors in[20]proposed a probabilistic model to defend against these attacks with the help of optimal early warning mechanisms.Whereas in[21]authors proposed optimal data partition and protection policy which is based on probabilistic models.They claimed that their proposed model could provide protection against data theft in cloud environment.

    Load balancing is major difficulties in cloud computing.In this paper, author mainly target on the impact of load balancing on emerging technologies which depend on cloud computing.Further,the current methods of reliable and dependable cloud computing can be achieved by efficient load balancing on cloud with IoT, Big Data & self-learning systems.It has been proved that DBLA& NDLBA algorithms are efficient and useful for succeeding generation cloud computing.The important factor of this paper is to improve the overall attainment & to maximize re virtualization for allotment of tasks on VMs[22].

    Compared to the changes that occurred during transmission from different VM memory events,the idle experience changes relatively significantly as the application network latency changes.The apparent total transport time is closely related to the size of the migrant worker’s status.Virtualization offers a new way to cloud computing, allowing both small and large business setups to maintain their applications by renting available resources.Real-time VM forwarding allows virtual machines to be transferred from one host to another while the virtual machine is active and running.The main challenge for real-time WAN migration is to maintain an Internet connection during migration[23].

    Moving a large number of virtual machines to high-performance servers and overloading workloads typically leads to a lack of space and reduced performance.Therefore, smaller recovery techniques can be used to perform the transfer in an acceptable manner.Before the VM is transferred to the powerful server, the entire state of the VM’s memory is stored on the small host server, and after the VM is fully transferred,the rest of the powerful server is transferred.The main goal of this research[24]is to find and offer suitable solutions to improve the transmission capacity of virtual data centres.

    This effectively improves real-time transmission and reduces both transmission and idle time.To implement the design of energy-efficient integration of servers, a virtual machine with less recycling technology is used in the cloud centre.Cloud computing can provide end-users with all IT technologies,from IT quality to infrastructure,applications,business processes and personal collaboration.A cloud is a set of hardware,software,networks,services,storage,and interfaces that are connected to provide computing functions and other devices as needed[25].

    Similarly, in [26] authors highlight the importance of virtual machines placement with multiple resources.They proposed a unique approximation algorithm to solve this NP hard problem.They conducted real time tests with multiple cloud providers such as Azure and Google and proved that their algorithm performed better than the existing ones.

    Although a number of other cloud studies have been conducted in the literature on economics,privacy, and security, there is a clear lack of research on migration, server integration, and network virtualization technology.To fill this gap,we have proposed different plans for classifying researchers’common and different views based on evidence-based literature[27].

    The virtual migration engine(VM)is widely used in and between domain controllers to meet the needs of different virtual cloud environments.For example,server compression requires the transfer of virtual machines for power management.In addition, real-time migration is required to balance workload, fault tolerance, system maintenance, and to reduce SLAs.The VM transfer process is very resource-intensive and requires ingenious methods to avoid network-saturated bandwidth and to reduce server downtime.Compared to traditional computing methods,cloud computing is a promising computing paradigm.Along with the traditional computing methods,numerous organizations prefer to utilize flexible cloud services to reduce cost significantly.Virtualization is a technology widely used in today’s data centres to maximize utilization of resources and to reduce greenhouse gas emissions and costs.

    3 Proposed Methodology

    The proposed architecture is specifically designed to cater to the aims and objectives of the study of live migration in cloud environments.Our methodology is divided into two main parts:1) block live migration and 2) sequential and parallel migrations.Considering the implications of hardware maintenance, energy-saving policy, load balancing, or encountering devastating incidents, there will be a requirement to abandon different parts of the VMs from different physical hosts to other hosts by using the live VM migration at the earliest.The models used in our study will share a similar network traffic path.For instance,if there are five live migrations sharing the same path of the network along with all other sources and host’s destination.As each migration completely utilizes the bandwidth path,part of the network transmission is significantly small in contrary to the parallel migration.

    The proposed architecture consist of source and target machine to monitor and process the migration of VMs to achieve the cloud orchestration.Source machine and target machine have its own hardware and hypervisors running different VMs on it.The migration module is further divided into four major sub modules.

    ·Data Mapping Module

    ·Log Management Module

    ·QoS Parameter Module

    ·Hardware Performance Module

    To cater the performance of VMs the role of migration module is important for migration of VMs services to achieve the cloud orchestration.Data mapping module is further divided into data rate measurement,performance measurement and migration decision.

    In Fig.3 organizes the lifecycle of the VMs in a cloud computing environment from source to the target machine.The migration module is responsible for scheduling,migrating,decommissioning,and spawning VMs on request.The migration module in IaaS is facilitated through the Log management,hardware management, and data mapping module.In the data center environment source machine having own host operating system,hypervisor that running different virtual machines which needs to secure and complete the migration process.By keeping the migration track changes the role of log management is important to take migration decisions.All types of the log are recorded and helpful for quality migration and hardware performance.The hardware performance module is divided into network performance,storage performance and input/out performance.this storage continues if the VM is present and is removed after terminating the VM.

    Figure 3:Our proposed IaaS cloud model for live storage migration

    The CPU, storage devices, memory, and energy supply often decide the energy usage of the network resources in a data center.A linear relationship between energy consumption and CPU usage will adequately characterize a server’s energy usage.

    The proposed model is connected to two storages,e.g.,source storage and destination storage that are linked with a network service and images service.The network performance will keep a record the VM connected to over the migration period.The Diagnostic log will keep the session identification of the source and destination service through migration.The performance of the live migration can be achieved through QoS parameters like availability of VM, downtime of VM, and outage of VM.The objective of live migration in IaaS will achieve cloud orchestration and will happen through the dashboard of two providers who will monitor the end-to-end data transfer through log management.

    There are two types of VM migration:1)non-live migration and 2)live migration.Live migration is only possible if the operating systems are not disrupted,while non-live migration is not restricted in this way.A VM is first paused or shut down before a non-live migration can take place,depending on whether it can continue to operate the services after migration or not.Live VM migration has received substantial coverage as one of the most important techniques for increasing data center utilization by efficiently allocating the resources, leading to more reliable services.In data center, performance optimization includes hardware,data mapping and QoS parameters,but no limited to,reduced power demand and less unutilized space.Moreover,in the event of repairs or breakdowns,there will be fewer service interruptions.

    The aim of a live VM migration is to copy the VM’s state,memory,and disk with the least amount of downtime possible, ensuring non-disruption to the VM’s services.Live VM transfer approaches,including Pre-download,Post-copy,and Hybrid-copy[11],make a copy of the VM’s memory states to the destination host whilst the VM is still running on the source host.Modern cloud-based applications will take advantage of live relocation,which refers to the method of moving a running program to a new physical location with minimized downtime.Furthermore,the live migration between various cloud service providers gives cloud users a new degree of flexibility,allowing them to switch their workloads around to achieve their business goals without being bound to a single provider.While this vision is not new,there are just a few solutions and proof-of-concepts available.Live migration is a technique for moving a virtualized operating service between hosts in a smooth manner,allowing networks to respond to changes quickly.The idea behind this technology is to keep the downtime of the service to a minimum so that the end-user is unaware of the migration.Docker for containers and KVM for virtual machines are the two most common off-the-shelf virtualization technologies.However,there is a lack of knowledge about how these virtualization technologies perform during real-time migration.

    4 Simulation and Results

    In this section, the Mamdani Fuzzy I nference System (i.e., MFIS) is used to simulate the performance of cloud storage services.In short,a Mamdani fuzzy inference system is a method that uses a set of if-then fuzzy rules defined by domain-expert operators to make decisions about the outputs of the system’s behavior.There are many benefits to using the MFIS,including its widespread acceptance, intuitiveness, suitability for human input, and ease of interpretability.The description of fuzzy inference system for predicting the cloud storage service is depicted in Fig.4.Overall, the below Mamdani FIS takes three inputs, namely storage service availability, downtime, and outage.The output of the Mamdani system represents the performance of storage services.

    Figure 4:Performance prediction of cloud storage service for MFIS

    In essence,the input parameter variables are statistical values that are used to calculate the storage performance of cloud services.Tab.1 presents the input variables.Through migration from source to target machine the total number of Available of VM on source machine which needs to migrate.Downtime of VM is calculated if the VMs become unavailable during migration process and outage of VM means the how many times the out of migrated VMs.The range values of the input variables is set according to the availability of VM,if the availability of VM is 100%then its means the virtual Machines are remained highly available during migrations.Similarly the Down time is mapped with linguistic value if no down time then the availability is 100%otherwise high downtime leads the poor performance.Outage of VMs means the total number of time the concerned VM remains out of connection during migration process.In cloud infrastructure.

    Table 1:Input variables and their ranges;VMs=virtual machines

    Tab.2 lists the membership functions that we used to map the input variables to a particular set,where the membership output value is always limited between 0 and 1.The below functions are used to fuzzify or defuzzify the inputs to/from some linguistic terms.As seen in Tab.2, our membership functions are triangular, typically limited by a lower and upper value for each fuzzy set.For each input variable,our membership functions produce three fuzzy set outputs.We have used the MATLAB simulation software to implement the below triangular functions and visualize the depicted graphs.

    Table 2:The membership functions of our mamdani FIS;Av=availability,D=downtime,Ou=Outage,and P=performance

    The fuzzy output variable(i.e.,performance of VMs)is determined based on the values of input variables(i.e.,availability,downtime,outing).Tab.3 details of output ranges and linguistic variables,mainly low,medium,and high.

    Table 3:MFIS performance output ranges

    Eq.(1)outlines the mathematical equation for our MFIS defuzzier

    The membership function of performance depends on the minimum values of membership of availability,downtime,and outage time.The mathematical equation is shown in Eq.(2)

    Eq.(2)can also be written as

    Similarly,Eq.(3)can be written as shown in Eq.(4)

    MFIS must be designed on top of a clear rule-based engine.There are 81 rules were formulates by keeping the input and output variables.On the basis of our input three input variables, the VM performance output values were calculated using the assigned rule listed.Few rules are displayed in Tab.4.

    The simulation results (Figs.5–7) are produced using the MATLAB R2021a software.Fig.5 depicts the lookup diagram for the low storage performance,particularly the relationship between the system performance and low input values of availability,downtime,and outage.This diagram shows that the storage performance is low(e.g.,20),when the VM availability is low(e.g.,9),VM downtime is medium(e.g.,50),and outage time is low(e.g.,0.37).Fig.5 indicates that output performance values equal to or below 20 are perceived as a poor cloud storage performance.

    Fig.6 depicts the lookup diagram for the medium storage performance.The performance is in the medium range when the system availability is medium(e.g.,36),downtime is high(e.g.,76),and outage is low(e.g.,2.9).Performance values approaching 50 are considered to exhibit a medium cloud storage performance.

    Table 4:The rule base engine for the VM performance measurement

    Figure 5:The lookup diagram for the low VM storage performance ranges

    Figure 6:The lookup diagram for the medium storage performance ranges

    Fig.7 depicts the lookup diagram for the high storage performance.The system performance is considered high when the VMs availability ranges in the high values(e.g.,90),downtime is low(e.g.,16),and outage value is low(e.g.,0.5).Output values greater than 80 are considered to exhibit a high performance of the cloud storage.

    Figure 7:The lookup diagram for the high storage performance ranges

    The performance of the cloud infrastructure as a service (IaaS) is checked through a Mamdani fuzzy inference system.The results were analyzed based on the network performance of the source and destination host.The cloud storage service offered by different cloud service providers,in terms of its availability time,downtime and outage time,is simulated and tested.Traditional virtualization systems are unable to effectively isolate shared micro-architectural resources among virtual machines(VMs).Different types of CPU and memory-intensive VMs competing for these common resources would result in varying degrees of performance loss, thus lowering device stability and QoS in the cloud.

    5 Implications and Limitations

    A live migration is a technology that allows a complete virtual machine to be relocated from one physical system to another while it is still running.Active memory and execution state are transmitted from the source to the destination when a VM is migrated as a whole.This provides for seamless online service migration without the need for clients to reconnect.Through fuzzification of the possible research problem is plotted with three input variables.However it can be improved with other variables like link speed,size of VM and page dirty rate.Due to heterogenous structure of cloud computing,it is difficult to map the real scenario of two different data center.

    6 Conclusion and Future Works

    Live migration mechanisms in cloud computing have many compatibility issues with pre-copy migration of the optimization of VM migration.Performance models for cloud storage during the migration procedure is designed,from which several performance features are tested and monitored the high performance.It not only considers users’requirements for migration efficiency but also solves the storage convergence issue.By modifying the storage in live migration parameters like availability,downtime and outage from the source to destination host are tested.By adjusting the network rate of the migrated VM or/and the network bandwidth for migration, our proposed model demonstrated feasibility.This is beneficial in terms of improving cloud management in storage dashboard, are specific about the consistency they may expect before migrating their virtual machines.In the future,we aim to conduct further studies to improve the migration time migration and performance due to geographical limitations.

    Acknowledgement:Thanks to our colleagues,who provided moral and technical support.

    Funding Statement:The authors received no specific funding for this study.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding this study.

    ponron亚洲| 亚洲在久久综合| 成人一区二区视频在线观看| 中文字幕免费在线视频6| 久久午夜福利片| 国产精品一区www在线观看| 日本成人三级电影网站| 国产69精品久久久久777片| 中文字幕久久专区| 我要看日韩黄色一级片| 国产精品蜜桃在线观看 | 国产精品蜜桃在线观看 | 亚洲av.av天堂| 成人毛片60女人毛片免费| 最近最新中文字幕大全电影3| 免费看美女性在线毛片视频| 寂寞人妻少妇视频99o| 亚洲av免费高清在线观看| 国产精品人妻久久久影院| 国产熟女欧美一区二区| 国产精品野战在线观看| 亚洲va在线va天堂va国产| 深夜a级毛片| 欧美不卡视频在线免费观看| 简卡轻食公司| 搡老妇女老女人老熟妇| 亚洲av男天堂| 国产三级中文精品| 国产精品.久久久| 欧美一级a爱片免费观看看| 青春草国产在线视频 | 一本一本综合久久| 偷拍熟女少妇极品色| 久久亚洲精品不卡| 91久久精品电影网| 久久久a久久爽久久v久久| 午夜精品在线福利| 偷拍熟女少妇极品色| 99久国产av精品国产电影| 欧美3d第一页| 69人妻影院| av在线播放精品| 亚洲av第一区精品v没综合| 国产免费一级a男人的天堂| 在线a可以看的网站| 精品熟女少妇av免费看| 少妇人妻精品综合一区二区 | 国产精品三级大全| 国产一区二区在线av高清观看| 一边亲一边摸免费视频| 亚洲经典国产精华液单| 99精品在免费线老司机午夜| 国产黄色视频一区二区在线观看 | 久久婷婷人人爽人人干人人爱| 身体一侧抽搐| 夜夜爽天天搞| 岛国毛片在线播放| 精品久久久噜噜| 国产精品伦人一区二区| 中文资源天堂在线| 级片在线观看| 亚洲成a人片在线一区二区| 最后的刺客免费高清国语| 一级毛片电影观看 | 可以在线观看毛片的网站| 色视频www国产| 亚洲最大成人手机在线| 91久久精品国产一区二区三区| 久久亚洲精品不卡| 午夜福利在线在线| 欧美日韩一区二区视频在线观看视频在线 | 大又大粗又爽又黄少妇毛片口| av视频在线观看入口| 中文字幕人妻熟人妻熟丝袜美| 91av网一区二区| 中文亚洲av片在线观看爽| 黄色一级大片看看| 日本成人三级电影网站| 国产欧美日韩精品一区二区| 免费黄网站久久成人精品| 精品人妻一区二区三区麻豆| 秋霞在线观看毛片| 亚洲va在线va天堂va国产| 国产亚洲5aaaaa淫片| 国产熟女欧美一区二区| 日韩,欧美,国产一区二区三区 | 91久久精品国产一区二区成人| 亚洲自偷自拍三级| 少妇被粗大猛烈的视频| 国内少妇人妻偷人精品xxx网站| 精品少妇黑人巨大在线播放 | 男女那种视频在线观看| 亚洲久久久久久中文字幕| 丰满的人妻完整版| 天堂影院成人在线观看| 久久国内精品自在自线图片| h日本视频在线播放| 免费黄网站久久成人精品| 久久午夜福利片| 69人妻影院| 九九热线精品视视频播放| 又黄又爽又刺激的免费视频.| 1024手机看黄色片| 国产精品久久久久久av不卡| 国产日韩欧美在线精品| 特大巨黑吊av在线直播| 久久久久久久久久久丰满| 久久精品久久久久久久性| 久久久成人免费电影| 亚洲精品自拍成人| 亚洲中文字幕日韩| 亚洲中文字幕一区二区三区有码在线看| 国产成人91sexporn| 日韩中字成人| 欧美日韩在线观看h| 22中文网久久字幕| 国产成人精品久久久久久| 亚洲成人av在线免费| 中文资源天堂在线| 国产激情偷乱视频一区二区| 久久久久久久亚洲中文字幕| 成年av动漫网址| 干丝袜人妻中文字幕| 国产精品美女特级片免费视频播放器| 亚洲精品国产av成人精品| 嫩草影院入口| 少妇猛男粗大的猛烈进出视频 | 91麻豆精品激情在线观看国产| 五月伊人婷婷丁香| 亚洲真实伦在线观看| 久久久久久九九精品二区国产| 日韩在线高清观看一区二区三区| av在线亚洲专区| 日韩欧美国产在线观看| 亚洲美女搞黄在线观看| 日韩精品有码人妻一区| 成人特级黄色片久久久久久久| 国产精品一二三区在线看| а√天堂www在线а√下载| 在线观看美女被高潮喷水网站| 97热精品久久久久久| 在线免费十八禁| 亚洲va在线va天堂va国产| 一区福利在线观看| 啦啦啦韩国在线观看视频| 国产精品永久免费网站| 一卡2卡三卡四卡精品乱码亚洲| 亚洲欧美精品自产自拍| 中文字幕av在线有码专区| 啦啦啦韩国在线观看视频| 99热全是精品| 日韩成人av中文字幕在线观看| 国产精品久久久久久av不卡| 久久精品夜色国产| 免费看日本二区| 亚洲18禁久久av| 免费电影在线观看免费观看| 日韩欧美一区二区三区在线观看| 久久亚洲精品不卡| 国产极品天堂在线| 欧美+亚洲+日韩+国产| 国产精品一区二区在线观看99 | 亚洲一区二区三区色噜噜| 欧美区成人在线视频| 亚洲色图av天堂| 三级经典国产精品| 成年版毛片免费区| 日本熟妇午夜| 人妻制服诱惑在线中文字幕| 日韩,欧美,国产一区二区三区 | 欧美变态另类bdsm刘玥| 尤物成人国产欧美一区二区三区| 久久精品国产亚洲av天美| 别揉我奶头 嗯啊视频| 九九爱精品视频在线观看| 丝袜美腿在线中文| 国产一区二区在线观看日韩| 女同久久另类99精品国产91| 边亲边吃奶的免费视频| 一区福利在线观看| 我的女老师完整版在线观看| a级毛片免费高清观看在线播放| 美女大奶头视频| 国产精品一区二区三区四区免费观看| 国产精品久久久久久久久免| 亚洲国产精品久久男人天堂| 国产av麻豆久久久久久久| 嫩草影院新地址| 搡老妇女老女人老熟妇| 久久韩国三级中文字幕| 成人无遮挡网站| 亚洲四区av| 一级二级三级毛片免费看| 狂野欧美激情性xxxx在线观看| 国产精品精品国产色婷婷| 狠狠狠狠99中文字幕| 99久久无色码亚洲精品果冻| 久久午夜福利片| 免费观看的影片在线观看| 春色校园在线视频观看| 亚洲av免费高清在线观看| 国产老妇伦熟女老妇高清| 国产精品一区www在线观看| 一进一出抽搐动态| 免费在线观看成人毛片| 51国产日韩欧美| 在线免费十八禁| 夜夜夜夜夜久久久久| 中文字幕制服av| 九九爱精品视频在线观看| 亚洲不卡免费看| 丝袜喷水一区| 又爽又黄a免费视频| 18禁在线播放成人免费| 国产一区二区在线观看日韩| 综合色av麻豆| 国产精品三级大全| 国产精品无大码| 永久网站在线| 成人综合一区亚洲| .国产精品久久| 少妇人妻一区二区三区视频| av免费在线看不卡| 九九爱精品视频在线观看| 男人狂女人下面高潮的视频| 少妇的逼水好多| 国产亚洲精品久久久久久毛片| 国产精品电影一区二区三区| 麻豆国产97在线/欧美| 网址你懂的国产日韩在线| 欧美人与善性xxx| 欧美日韩一区二区视频在线观看视频在线 | 美女大奶头视频| 国产精品一及| 我要看日韩黄色一级片| 日韩高清综合在线| 最后的刺客免费高清国语| 禁无遮挡网站| 国产麻豆成人av免费视频| 女人被狂操c到高潮| 亚洲久久久久久中文字幕| 亚洲人成网站高清观看| 99久久精品一区二区三区| 麻豆成人av视频| 三级男女做爰猛烈吃奶摸视频| 精品一区二区三区人妻视频| 久久久午夜欧美精品| 3wmmmm亚洲av在线观看| 在线免费观看的www视频| 国国产精品蜜臀av免费| 亚洲av第一区精品v没综合| 嫩草影院入口| 久久国内精品自在自线图片| 国产中年淑女户外野战色| 性色avwww在线观看| 一区二区三区免费毛片| 免费观看的影片在线观看| 秋霞在线观看毛片| 久久精品久久久久久久性| 国产v大片淫在线免费观看| 国产综合懂色| 久久人妻av系列| 欧美潮喷喷水| 久久久久国产网址| 国产黄片美女视频| 黄色一级大片看看| 免费av不卡在线播放| 国产精品乱码一区二三区的特点| 欧美人与善性xxx| 22中文网久久字幕| 久久久成人免费电影| 热99在线观看视频| 久久欧美精品欧美久久欧美| 波野结衣二区三区在线| 日韩视频在线欧美| 麻豆成人av视频| 2022亚洲国产成人精品| 成年女人看的毛片在线观看| 可以在线观看毛片的网站| av在线亚洲专区| 欧美日韩一区二区视频在线观看视频在线 | av国产免费在线观看| 国产精品一区二区性色av| 国产午夜福利久久久久久| 色综合色国产| 永久网站在线| 少妇人妻一区二区三区视频| 午夜a级毛片| 久久久精品94久久精品| 免费搜索国产男女视频| 一本一本综合久久| 极品教师在线视频| 久久这里有精品视频免费| 亚洲精华国产精华液的使用体验 | 国产精品人妻久久久影院| 青青草视频在线视频观看| 国产高清有码在线观看视频| 亚洲av免费高清在线观看| 欧美成人精品欧美一级黄| 可以在线观看毛片的网站| 日韩,欧美,国产一区二区三区 | 日韩,欧美,国产一区二区三区 | 免费av不卡在线播放| 又粗又爽又猛毛片免费看| 高清日韩中文字幕在线| 日本av手机在线免费观看| 国产精品久久视频播放| 国产麻豆成人av免费视频| 99久久成人亚洲精品观看| 精品不卡国产一区二区三区| 日韩制服骚丝袜av| 一级毛片我不卡| 亚洲在线自拍视频| 天天一区二区日本电影三级| 日本与韩国留学比较| 国产精品一及| 听说在线观看完整版免费高清| 变态另类丝袜制服| 一级二级三级毛片免费看| 夜夜爽天天搞| 亚洲国产精品成人综合色| 国产乱人偷精品视频| 悠悠久久av| 久久这里只有精品中国| 色综合站精品国产| 久久人人爽人人片av| 国产成人a∨麻豆精品| videossex国产| 日本黄色片子视频| 国产精品电影一区二区三区| .国产精品久久| 亚洲国产精品sss在线观看| 波多野结衣高清作品| 好男人视频免费观看在线| 欧美成人精品欧美一级黄| 少妇高潮的动态图| 久久精品影院6| 别揉我奶头 嗯啊视频| 国产三级中文精品| 国产黄色小视频在线观看| 非洲黑人性xxxx精品又粗又长| 亚洲三级黄色毛片| 国产熟女欧美一区二区| 婷婷六月久久综合丁香| 少妇裸体淫交视频免费看高清| 青春草国产在线视频 | 美女cb高潮喷水在线观看| 亚洲成a人片在线一区二区| 波多野结衣巨乳人妻| 久久99蜜桃精品久久| 日日干狠狠操夜夜爽| 一区二区三区四区激情视频 | 亚洲国产精品成人综合色| 好男人在线观看高清免费视频| 精品无人区乱码1区二区| 看黄色毛片网站| 1000部很黄的大片| 人体艺术视频欧美日本| 国产一区二区在线观看日韩| 欧美性猛交黑人性爽| 国产老妇伦熟女老妇高清| 一本精品99久久精品77| 亚洲电影在线观看av| 在线天堂最新版资源| 婷婷六月久久综合丁香| 亚洲va在线va天堂va国产| 精品人妻一区二区三区麻豆| 中国美白少妇内射xxxbb| 日日啪夜夜撸| а√天堂www在线а√下载| 国产精品久久电影中文字幕| 三级男女做爰猛烈吃奶摸视频| 99视频精品全部免费 在线| 午夜a级毛片| 男女视频在线观看网站免费| 国产91av在线免费观看| 色综合色国产| 黄片wwwwww| 国产黄色小视频在线观看| 免费看日本二区| 免费看光身美女| 色噜噜av男人的天堂激情| 日日干狠狠操夜夜爽| 亚洲精品456在线播放app| 两性午夜刺激爽爽歪歪视频在线观看| 身体一侧抽搐| 国产高清视频在线观看网站| 亚洲欧美日韩高清专用| 啦啦啦韩国在线观看视频| ponron亚洲| 亚洲国产欧美人成| 一进一出抽搐动态| 在线播放国产精品三级| 国产色婷婷99| ponron亚洲| 亚洲欧美日韩卡通动漫| 丝袜喷水一区| 欧美一区二区精品小视频在线| 国产精品福利在线免费观看| 色哟哟·www| 亚洲国产精品成人久久小说 | 不卡一级毛片| 我要搜黄色片| 欧洲精品卡2卡3卡4卡5卡区| 国产一级毛片七仙女欲春2| av又黄又爽大尺度在线免费看 | 黄片wwwwww| av在线天堂中文字幕| 午夜久久久久精精品| av在线蜜桃| 国产国拍精品亚洲av在线观看| 51国产日韩欧美| 亚洲国产精品合色在线| 日韩三级伦理在线观看| 韩国av在线不卡| 国产精品人妻久久久久久| 国产 一区精品| 嫩草影院入口| 久久久a久久爽久久v久久| 悠悠久久av| 中文字幕熟女人妻在线| 亚洲精品456在线播放app| 在线观看免费视频日本深夜| 一级毛片电影观看 | 国产爱豆传媒在线观看| 有码 亚洲区| 成年免费大片在线观看| 亚洲人成网站在线播放欧美日韩| 欧美性猛交黑人性爽| 欧美高清性xxxxhd video| 99久久久亚洲精品蜜臀av| av视频在线观看入口| 欧美极品一区二区三区四区| 国产午夜精品论理片| 日韩欧美精品v在线| 免费搜索国产男女视频| 国产综合懂色| 久久人人精品亚洲av| 99久久精品国产国产毛片| 久久精品国产亚洲av涩爱 | 好男人视频免费观看在线| 国产单亲对白刺激| 欧美高清成人免费视频www| 国产伦精品一区二区三区视频9| 桃色一区二区三区在线观看| 中文字幕制服av| 欧美人与善性xxx| 淫秽高清视频在线观看| 国产单亲对白刺激| 日韩 亚洲 欧美在线| 久久99热6这里只有精品| 欧美日本视频| 日日撸夜夜添| 久久韩国三级中文字幕| 亚洲欧美精品综合久久99| 国语自产精品视频在线第100页| 国产男人的电影天堂91| 久久精品久久久久久久性| 在线观看66精品国产| 国产一区二区激情短视频| 亚洲欧美精品综合久久99| 久久99热这里只有精品18| 色哟哟·www| 国产亚洲av片在线观看秒播厂 | 偷拍熟女少妇极品色| 亚洲人成网站高清观看| 久久精品久久久久久噜噜老黄 | 久久久国产成人精品二区| 午夜福利在线观看免费完整高清在 | 久久国内精品自在自线图片| 人妻制服诱惑在线中文字幕| av.在线天堂| 超碰av人人做人人爽久久| 久久久久久久亚洲中文字幕| 婷婷六月久久综合丁香| 老司机福利观看| 在线天堂最新版资源| 97超视频在线观看视频| 又爽又黄无遮挡网站| 亚洲一区二区三区色噜噜| 日日干狠狠操夜夜爽| 啦啦啦观看免费观看视频高清| 草草在线视频免费看| 免费看日本二区| 2021天堂中文幕一二区在线观| 午夜精品在线福利| 欧美人与善性xxx| 麻豆国产97在线/欧美| 色尼玛亚洲综合影院| 免费看a级黄色片| 1024手机看黄色片| 黄色日韩在线| 青青草视频在线视频观看| 久久这里有精品视频免费| av又黄又爽大尺度在线免费看 | 国产一区二区在线av高清观看| 亚洲美女搞黄在线观看| 亚洲真实伦在线观看| 国内精品宾馆在线| 久久久久久久久久久免费av| 久久综合国产亚洲精品| 亚洲在线观看片| 久久精品久久久久久噜噜老黄 | 毛片女人毛片| 亚洲天堂国产精品一区在线| 国产av在哪里看| 性色avwww在线观看| 赤兔流量卡办理| 日韩制服骚丝袜av| 国产视频内射| 午夜福利在线观看免费完整高清在 | 能在线免费看毛片的网站| 91狼人影院| 少妇的逼好多水| 国产乱人偷精品视频| 国产三级在线视频| 久久午夜亚洲精品久久| 色综合色国产| 中文字幕熟女人妻在线| 国产成人精品一,二区 | 色视频www国产| 一区二区三区高清视频在线| 精品久久久久久久末码| 国产成人一区二区在线| 少妇的逼好多水| 国产在视频线在精品| 久久国产乱子免费精品| 最近2019中文字幕mv第一页| 在线天堂最新版资源| 亚洲av熟女| 中国美白少妇内射xxxbb| 天堂√8在线中文| 尤物成人国产欧美一区二区三区| 亚洲欧美日韩无卡精品| 亚洲人与动物交配视频| 日本黄色视频三级网站网址| 色播亚洲综合网| 熟妇人妻久久中文字幕3abv| 精品久久国产蜜桃| av在线老鸭窝| 午夜a级毛片| 欧美3d第一页| 亚洲精品久久久久久婷婷小说 | 亚洲av免费在线观看| 尤物成人国产欧美一区二区三区| 久久精品夜夜夜夜夜久久蜜豆| 如何舔出高潮| 亚洲精品日韩av片在线观看| 色视频www国产| av在线观看视频网站免费| 非洲黑人性xxxx精品又粗又长| 1000部很黄的大片| 一本久久中文字幕| 亚洲av二区三区四区| 亚洲av第一区精品v没综合| 看黄色毛片网站| 久久久久久国产a免费观看| 青春草视频在线免费观看| 国产男人的电影天堂91| 成人一区二区视频在线观看| 久久久久久大精品| 夜夜夜夜夜久久久久| 国内揄拍国产精品人妻在线| 毛片一级片免费看久久久久| 最近最新中文字幕大全电影3| 美女国产视频在线观看| 亚洲国产精品国产精品| 搡女人真爽免费视频火全软件| 波多野结衣高清无吗| 亚洲国产色片| 亚洲成人久久性| 国产精品久久久久久精品电影小说 | 国产午夜精品久久久久久一区二区三区| 亚洲人与动物交配视频| 国内少妇人妻偷人精品xxx网站| 麻豆成人午夜福利视频| 麻豆久久精品国产亚洲av| 男人和女人高潮做爰伦理| 天堂中文最新版在线下载 | 黄片wwwwww| 欧美成人免费av一区二区三区| 精品人妻视频免费看| 欧美3d第一页| 国产一区二区三区av在线 | ponron亚洲| 国产美女午夜福利| 日本免费一区二区三区高清不卡| 日韩高清综合在线| 美女脱内裤让男人舔精品视频 | 国产一级毛片在线| 少妇人妻一区二区三区视频| 性欧美人与动物交配| 精品人妻视频免费看| 久久精品国产99精品国产亚洲性色| 久久99精品国语久久久| 人妻系列 视频| 国产成人精品久久久久久| 老司机影院成人| 国产高潮美女av| 乱系列少妇在线播放| 国产精品综合久久久久久久免费| 国产淫片久久久久久久久| 成人毛片60女人毛片免费| 麻豆成人午夜福利视频| 男的添女的下面高潮视频| 日韩 亚洲 欧美在线| 天天躁夜夜躁狠狠久久av| 亚洲欧美日韩高清专用| av.在线天堂| 国产片特级美女逼逼视频| 91久久精品电影网| 51国产日韩欧美| 久久精品人妻少妇| 麻豆乱淫一区二区| 国产真实伦视频高清在线观看|