• <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 ?

    Fuzzy Control Based Resource Scheduling in IoT Edge Computing

    2022-08-23 02:17:32SamahAlhazmiKailashKumarandSohaAlhelaly
    Computers Materials&Continua 2022年6期

    Samah Alhazmi,Kailash Kumarand Soha Alhelaly

    College of Computing and Informatics,Saudi Electronic University,Riyadh,Kingdom of Saudi Arabia

    Abstract: Edge Computing is a new technology in Internet of Things (IoT)paradigm that allows sensitive data to be sent to disperse devices quickly and without delay.Edge is identical to Fog,except its positioning in the end devices is much nearer to end-users,making it process and respond to clients in less time.Further,it aids sensor networks,real-time streaming apps,and the IoT,all of which require high-speed and dependable internet access.For such an IoT system, Resource Scheduling Process (RSP) seems to be one of the most important tasks.This paper presents a RSP for Edge Computing(EC).The resource characteristics are first standardized and normalized.Next,for task scheduling,a Fuzzy Control based Edge Resource Scheduling(FCERS)is suggested.The results demonstrate that this technique enhances resource scheduling efficiency in EC and Quality of Service (QoS).The experimental study revealed that the suggested FCERS method in this work converges quicker than the other methods.Our method reduces the total computing cost, execution time, and energy consumption on average compared to the baseline.The ES allocates higher processing resources to each user in case of limited availability of MDs;this results in improved task execution time and a reduced total task computation cost.Additionally,the proposed FCERS m 1m may more efficiently fetch user requests to suitable resource categories,increasing user requirements.

    Keywords:IoT; edge computing; resource scheduling; task scheduling; fuzzy control

    1 Introduction

    The collection of wireless interconnected items such as mobiles,sensors,Radio-Frequency Identification (RFID) tags and others form Internet of Things (IoT).These devices can utilise a unique addressing mechanism,but they have limited computational capacity and battery power.IoT devices can create enormous data in real-time and transfer it to cloud computing data centers located far away.Despite this, there is massive traffic and significant waits.As a result, more processing power and storage resources are needed by the IoT networks.Multi-access Edge Computing (MEC) is a novel technique that addresses these needs by providing a distributed computing model for networking,task processing, and data storage at IoT network edges [1].MEC can fulfil the resource needs of IoT applications while also lowering the latency in communication.Each MEC server consists of a virtualized environment that includes a computing device,data storage and wireless communication unit.The MEC servers housed several virtual resources that could be used to service stationary IoT or Mobile Devices(MD).A single-hop wireless connection,such as 4G LTE devices,Wi-Fi,Bluetooth,and other wireless interfaces,allows an MD to interact directly with MEC servers.The internet that helps to connect the cloud infrastructure is used by the MEC Servers [2].Fig.1 shows the MEC’s architecture,including wireless connections connecting IoT devices to the MEC[3,4].

    Figure 1:MEC architecture

    End-users, objects, and sensors are closer to EC’s storage, processing, control, and networking resources.An Edge Node can be as small as a smartphone, an Access Point (AP), a Base Station(BS),or even a cloud.A smartphone links wearable gadgets to the Cloud,a home gateway connects the Cloud with MD and the MDs to the core network.Edge Computing (EC) provides several outstanding features,including location awareness,resource pooling,distributed caching,processing of data analysis, scaling of resources, increased privacy and security, and dependable connection by delivering resource flexibility and intelligence.EC is a critical component of low-latency and good reliability.[5] contain many EC advantages and various use cases (e.g., media advertising, medical,offloading,smart homes,caching,and others).

    IoT support, Network architecture design, service deployment, Resource Scheduling Process(RSP) and administration, programming models and abstractions, privacy and security, incentive design, edge device dependability, and sustainability are just a few of the additional issues that EC confronts [6,7].The topic of Edge Computing Resource Allocation (RA) is the focus of this study.Unlike cloud computing, which has essentially infinite computational capacity and high network latency owing to the restricted computation capacity of Edge Nodes(EN),EC has comparatively less network delay but significant delay in processing because of the restricted computation capacity of EN.In addition,there are many small nodes in DC,equivalent to a lesser number of big Distributed Computing(DC).ENs can also be of various sizes(for example,the number of processing elements)and configurations (e.g., computing speed), extending from an MD towards 10’s/100’s of ES.These nodes are distributed across many sites,resulting in variable network and service delays for end-users.EC systems would most likely be made up of disparate communication, processing resources and Wi-Fi devices(e.g.,devices that differ in computing capabilities,battery states,and types of computational tasks).Before adopting compute offloading paradigms,three key issues must be solved.

    (a) First,effective TS algorithms must consider the various features of computational tasks and the heterogeneity of communication and computing resources.

    (b) Second, efficient administration of heterogeneous communication resources, which will be utilized for data transmission between end users’devices and external computer resources,is required.

    (c) Finally, efficient management of heterogeneous computing resources will be necessary to complete the computational tasks offloaded by various MDs.

    These three issues must be handled simultaneously, which is a problematic undertaking in extremely diverse EC systems.

    Furthermore, Edge Servers (ES) frequently have restricted processing resources in the given deployment’s cost benefits and scalability[8].One rationale is that the number of ES is installed mainly in numbers,demanding consideration of the deployment’s financial help.As a result,a particular ES may not require the same number of resources as a CS and cannot offer them.Following that,the ES will be unable to perform all tasks.The work will have a low processing efficiency and a long processing time when every task without any distinction is delivered to the ES.As a result,it’s important to find how to offload work based on demand.The act of transferring computing work from a MD to a Cloud Server(CS)or an ES is known as offloading[9–11].EC’s fundamental and crucial issue is developing an offloading technique and offloading work to an ES,resulting in increased latency while consuming less power than local execution.As a result, MD must pick the best offloading technique for their purposes,such as balancing delays and power consumption[12,13], decreasing task processing time[14–16]and energy consumption[17,18].

    RA issues have been discovered in much previous work,and the majority of them are concerned with computing or communication of TA for ES.Some researchers looked at how MDs allocate computational resources and control transmission power [19].In reality, MD-RA and ES-RA will work together to produce an offloading strategy.As a result, the link between offloading decision creation and computing RA must be investigated.

    This paper provides a dynamic Resource Scheduling Process(RSP)based on fuzzy control theory.The following are the paper’s main contributions:

    a) An RSP for EC.

    b) A model of allocation of essential resources is created depending on consumers’long-term and ongoing resource requirements.

    c) To build the RSP based on fuzzy control theory,the RSP is carried out.The scheme splits user needs and available resources into numerous resource clusters,making it easier to process fuzzy user requirements and improving cloud computing QoS.

    The remainder of the work is structured in the following manner.The present RSP and related research efforts are discussed in Section 2.The suggested model based on fuzzy control is presented in Section 3,and a new Fuzzy Control based Edge Resource Scheduling(FCERS)is given in Section 4.The carried out experiments and findings are evaluated in Section 5.To conclude,we make a list of future research work.

    2 Related Works

    Since the concept of EC was recommended, academics have been interested in computing Task Scheduling(TS)as an essential research topic in EC.There has been a slew of similar study findings recently.The primary objective of the TS offload approach is to find if a task created by a user terminal should be offloaded and where should it be scheduled.By examining TS techniques’present research state,this part recognises the task schedule’s research direction.Aazam et al.2021[20]propose a compute migration strategy for next-generation networks.A MEC approach based on Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies, as well as multi-attribute decision making and compute migration,is also presented.The authors used MATrix LABoratory(MATLAB)to conduct their tests,demonstrating multi-attribute decision making based on SDN and NFV that could pick the right MEC center,minimize server response time,and enhance Quality of Service(QoS).

    The authors[21]sought to deal with RA on NFV-enabled MECs,to reduce mobile service latency and MEC expenses.They presented a dynamic RA technique based on operational cost, consisting of a quick incremental allocation mechanism.They demonstrated that, compared to stationary MECs,their approach could deploy resources to ensure applications’low latency needs while saving money.Pham’s approach aims to improve gateway placement and multi-hop routing in NFV-enabled IoT (NIoT) and service placement at the MEC and cloud levels.To deal with a big NIoT system and optimise routing, RA for service functions, and gateway deployment, created approximation algorithms such as Service Placements Algorithm(SPA-1 and SPA-2),Gateway Placement,and Multihop Routing Algorithm (GPMRA).According to the authors, their approximation methods can minimize computing time while achieving near-optimal outcomes.

    Literature [22–24] developed a QoS decision engine that efficiently offloads to optimize performance and delay in response to the requirement in EC for real-time optimization.The work [25]presents a game theory technique for calculating unload, explains the nature of the equilibrium distribution, establishes its presence, and calculates equilibrium using a polynomial-time dispersion approach.However, the level of difficulty is higher.A multi-user distributed online task migration system is proposed in the literature [26–28], which considers the user’s selfish traits and is centred on the optimization theory presented by Lyapunov and an incentive scheme based onpeer-to-peerfile transfer.It providesmulti-hopuser involvement and common processing activities in the delay network.The present single-hop centralised collaboration method may considerably lower system power usage and enhance system performance.

    A Task Scheduling Algorithm (TSA) by Literature [29] proposed a TS method for task and resource features, and then recommends a Genetic Algorithm (GA) firework detection mechanism and uses the concepts of explosion radius,that allow load balancing and decrease TS time.Noghani et al.[30] proposed the allotment of resources for multi-user mobile EC environments regarding the Time Division Multiple Access (TDMA) and Orthogonal Frequency Division Multiple Access(OFDMA);they solved the mixed-integer issue multi-objective optimization problem and successfully minimized the consumption of total weighted mobile energy.Yang et al.[31] introduced an energyoptimizing offloading technique with guaranteed latency for global optimization.This method uses an artificial algorithm based on a fish swarm while also looking at the interaction between the fronthaul and backhaul network states[32].

    A work classification and resource usage-based task consolidation approach for a cloud computing environment was invented in 2016[33].They also devised a method of VM aggregation to balance power usage and TS processing time.A TS method in mobile cloud resources by dynamic clustering and the improved FCM method to reduce the number of corresponding criteria in the search was proposed in 2016 by Qiang et al.[34].The experiment demonstrates that the matching strategy may be proactively modified depending on the matching value and reinforcement training.Advanced Network Credit Scheduler(ANCS),proposed by Ni et al.2017[35],is a new approach for maintaining QoS in virtualization using dynamic network allocation of resources.Proportional sharing based on weight,reserving the minimum bandwidth and restricting the maximum bandwidth limitation are among the performance standards that ANCS aims to offer at the same time to meet the demands of the diverse network and cloud users.EC provides services to MDs who are close to each other.EC allows widely dispersed edge computing clusters to share resources or cache data.Following that,we feature a panel of specialists that specialize in resource management and EC.

    The deployment of the Steiner tree to examine the resource caching technique in EC is proposed by Su et al.[36].When an EC server stores resources to decrease overall route weight,it first builds a Steiner tree;the tree helps to lower resource cache costs.The paper results demonstrate that the Steiner tree approach outperforms the shortest path strategy.A dynamic RA was proposed in 2017 by Rahbari et al.[37].Its aim is resource management while screening,preparing,and encrypting data.For this aim,in an EC environment,the study provided resource management.Many factors that lead a client to quit,such as the service type,the cost of service,and the various waiver likelihood modifications,are considered in this article.The experiment results show that with the help of these characteristics,the service provider can estimate the number of resources needed[38].

    Traditional TS tool is straightforward to use.For data sharing in the TS of multi-layer deadlineconstrained scientific procedures, few Virtual Machines (VM), which are heterogeneous in nature,are provided with remote storage services by each supplier.One of the TS techniques that optimizes cost and makespan objectives is the earliest finish time.The authors considered the cloud service as commercial infrastructure.The Pareto front is used as a decision-making tool to assess the merits and demerits of several alternatives.The TS costs were lowered in half, but the turnaround time was increased by 5%.For spot VMs and on-demand instances, a Fault-tolerant TS [39] is addressed by bidding mechanism.This technique is inefficient due to the low-priced spot VM.The authors suggested a scheduler for real-time workflow [40].Backward shifting, resource scaling up,and shrinking facilitation were three phases in their technique that improved resource use over prior baseline algorithms.

    To solve the problems,heuristic approaches employ a set of rules.Traditional heuristics include first,best,and worst fit.Fog,Cloud,and Edge providers may use heuristic approaches to run largescale applications and processes.Mtshali et al.[41]use a heuristic method with an objective function that includes the task’s makespan and execution cost to schedule the tasks.The results show that this strategy is more efficient and has a lower needed cost when compared to other approaches.Based on cluster formation,Rodrigues et al.[42]present a coalitional game for radio access networks in the Fog.To increase network throughput, it employed an approach for dispersed TS of users.Other dynamic cloud TS approaches for vehicular are based on queue length and reaction time parameters.A stochastic Petri net and OpenStack were used to schedule activities and simulate them using the Markov single server system.In order to save energy, a heuristic method is employed in Xiaojun et al.[43]to plan simultaneous real-time activities in a heterogeneous network.In this work,nonlinear programming is utilised to choose frequencies and assign threads.

    Cloud-Fog investigated the TS and suggested a solution based on heuristics for striking a compromise between maximum completion time and Cloud resource monetary costs.Kao et al.[44]examined primary TS issues in a software-defined embedded system to support fog computing.To enhance users’experience,difficulties were framed as a mixed-integer nonlinear programming problem and provided an efficient heuristic TS technique.Nath et al.[45] introduced a fog computing RA method based on Priced Timed Petri Nets(PTPN).PTPN task model was created using fog resource characteristics.In terms of efficiency, the proposed technique beats static allocation strategies.In addition,heuristic approaches were used to achieve RSP in certain studies.

    3 The Proposed Approach

    3.1 System Model

    As indicated in Fig.2, we examine a network architecture with Edge Server and Node Mobile Devices.The MDui(i∈{1,2,...,N})and the ES have a channel bandwidth ofw.Because the distance in the middle ofuiand the edge server is generally 1-hop,the propagation latency amonguiand the ES‘c’is insignificant.The MDuihas only a limited amount of energyand the network generatesMtasks that must be performed during each time slot.Therefore,for each time slott(t∈{1,2,···,T})deviceui,generates a taskj(j∈{1,2,...,M})which is represented as below,Eq.(1)

    wheredi.j,tis the task’s data volume.The task is performed locally in a heterogeneous ES that specifies the required number of CPU cycles.is the necessary number of CPU cycles.The,‘t’is the maximum execution time that may be tolerated.Si.j,trepresents the task offloading strategies.Si.j,t=1,if the task is completed locally for the taskτi,j,t;otherwise,Si.j,t=0.

    The task information and the RA scheme for local computing are communicated to the ES.Still,after every time slot,the MD produces tasks and assigns them to the computing resources available locally.Depending on the offloading policy, the task is done locally or offloaded.The offloading policy is calculated by the ES and returned to the MD.

    3.2 A User Model

    Based on the service level,the user is the one who rents cloud services and submits the tasks.The user setUi={U1,U2,...,Um},which is a service entity with six tuples as defined below,Eq.(2)

    Uidis the unique ID of the user in cloud services.

    Unameis the name of the user.

    Figure 2:Network system model

    The user is provided with a resource type given by SL(Service Level).USL provides the user with SL.SL= {TierI,TierII,TierIII},for the account of the users,these parameters are considered.Ujobsis the set of tasks submitted by the user.The data regarding the security of users are given byUsec.

    3.3 B Task Model

    Multiple tasks are carried out in parallel, and they must continually interact with one another.Each task monopolizes VM resources.To the feasible extent,it must be ensured that all tasks in the RSP execute in parallel, preventing task block and reducing operation run time.The act of sending employment applications is demonstrated in Eq.(3).

    tasktis expressed as below Eq.(4),which has 7 tuples

    The unique ID fortasktistidandttypegives the type of tasks,small and large,as shown in Eq.(5).

    tlen=|ttasks|.tlen,gives the length of tasks;

    ttasksis the set of tasks in one taskttasks={Task-1,Task-2,...Task-k}

    To run a task,the minimum set of resources required is given bytres.

    The parametertartgives the Average Response Time for a task,which is calculated by averaging the response time of each task to evaluate operation cost and performance.The time that it takes for a task is to be added to the TS queue and start running is the average wait time.

    3.4 C Resource Model

    The resources such as(services,applications,server,data store etc.,)are shared.Resource model shows Resource= {r1,r2,...,rp}, total resource numberp= |Resource,rk|,rkwhich is the eight tuples:Computing resources that are shared and adjusted are referred to as resources(e.g.,networks,applications,data center,servers and services,etc.).The resource model demonstrates the total number of resources.,rkis a 9 tuple set shown below,Eq.(6)

    where

    ridis the unique ID of the resource

    rsupplyis the provider of the resource

    rtypeis the type of the resource

    rcompis the capacity of resources to process information

    rmemis resources available memory

    rstoris the capacity of the resource storage

    riois the capacity of IO in resources

    rnetis the resource bandwidth

    rposis the geographical resource position

    3.5 Edge Computing

    The data uplink transmission rate is required when the offloading of the taskτi,j,tto the ES is chosen by the MDui,Eq.(7)

    where channel bandwidth is represented byw, the MDui’s computation energy is given aspi, wherehiandσare the network gain and noise power,respectively.The inter-channel interference is given in denominator as cumulative term.Therefore,the uplink transmission time is,Eq.(8)

    The heterogeneous ES that takes timeτi,j,tfor the task is given in Eq.(9)

    whereis the computing resource assigned to the task in the time slot‘t’fromuithe ES;computing resources are allocated regularly,much as communication resources.The overall time spent on EC is given below as,Eq.(10):

    Many expensive computation applications have a considerably lower quantity of data than the input and neglect the return time.

    4 Resource Scheduling Algorithm Design

    Initially,computer RA is done for all tasks.The offloading of all tasks cannot be considered the ultimate offloading choice owing to timeline and energy restrictions,and it is not fair to use the default offloading to allocate the computation.As a result,we present a Fuzzy Control based Edge Resource Scheduling(FCERS)approach in this part to arrive at appropriate offloading options.The first step is to apply a constraint-based filter.The new offloading technique is denoted by s′,and its initial values ares′=s.It should be noticed that one of the requirements for filtering is a time limitation.We may retrieve the entire duration of edge executionafter getting the RA resultAnd IfThen s’(i,j,t)is set to 1.Furthermore,a local computational RSP is presented with energy restrictions,even though edge computations consume less energy than local computations.As a result,s′(i,j,t)=1.

    Algorithm:1 of Constraint-Based Filtering Step 1.Input τ,s Step 2.Output s′Step 3.Begin Step 4.s′←s Step 5.While(Any_Task,τi,j,t)not_processed Do Step 6.Calculate tE i,j,t =(di,j,t ri,j,t)+(cEi,j,kfEi,t )Step 7.Calculate eE i,j,t =pitt rans i,j,t using pittransi,j,t =pi(di,j,tri,j,t)Step 8.End While Step 9.If tE i,j,t >tdeadlinei,j,t or edeadlinei,j,t >eU i,j,t Then Step 10.s′i,j,t =1 Step 11.End If Step 12.End

    User needs may be classified into several categories.The user’s demands are matched with the resources in the class after locating the suitable resource category.The resource scale in the RSP is lowered after a fair split of resources.Simple weight matching is used to complete the RSP in this article.The following is the weight matching formula,Eq.(11):

    whereREQIdenotes the attribute of the user’s demands,Rset.irepresents the resource sets,andWeightimeans the attribute weights.

    The user’s demand varies in different volumes of resources.Computing needs, bandwidth and storage requirements may be split into three categories for distinct task preferences.Each task, like resources,has three characteristics,each of which has a distinct weight.The user-required attribute and the resource attribute are combined in the formula mentioned earlier,and the highest score achieved is returned to the user as the outcome of the RSP.The following is the pseudo-code for the RSP algorithm Fuzzy Control based Edge Resource Scheduling(FCERS).

    Algorithm:2 of Fuzzy Control based Edge Resource Scheduling(FCERS)Step 1.Input:Resource Set{RSet1,RSet2,...RSetm};Task Set{TSet1,TSet2,...TSetn}n,φ,m Step 2.Output Ranking rank,similarity result S Step 3.On the resource,set Do clustering Step 4. RConRS(M,L)=User&Resource_Clusters Step 5. R(s)=Size(R)Step 6.While(Test <1)Step 7. Test=Test+1 Step 8. xj =x Step 9.Process xn Step 10. Computed tE i,j,t Step 11. l=1 Step 12.While(l <=l)Do Step 13.l=l+1 Step 14.j=j+1 Step 15.While j=<m Do Step 16.j=j+1 Step 17.Space(k,i)=Sp(Information(j,:)Step 18.End While Step 19.End While Step 20.Measure xJ Step 21.If Result(xJ-x,Data)<φ Then Step 22.Break Step 23.End If Step 24. x ←xJ Step 25.End While Step 26.Obtain Resource Clusters Step 27.Measure Rank(MS)=REQI,Rseti,ωi Step 28. MR=TotalResourece(Result(REO1-Rseti)TotalWeighti Step 29.Return Similarity Measure S Step 30.End If Step 31.End

    5 Experimental Outcomes for Fuzzy Control Based Edge Resource Scheduling

    To assess FCERS’s resource allocation efficiency, we evaluated several methods, including their distribution of resources methods.We assign tasks of varying lengths to distinct Virtual Machines(VMs).The proposed FCERS model is compared with three well-developed models presented in Tab.1 below:

    In various VM,the execution speed of the Central Processing Unit(CPU)is analyzed and given in Fig.3.It shows that the proposed FCERS algorithm performs efficient RSP among corresponding VM based on the duration of tasks.When task durations are increased, the VM’s execution speed increases.The trend is more apparent than other algorithms.Improved Genetic Algorithm for Fuzzy c-means Clustering Algorithm Method (IGAFCM) and PTPN cannot adapt fast to task duration as FCERS.Additionally, the Advanced Network Credit Scheduler (ANCS) has a limited ability to respond to task characteristics.

    Table 1: Various models for RA

    Figure 3:CPU execution speed among different VMs

    Fig.4 illustrates the time required to complete a task using various techniques,and there are five VMs.The execution speed is lower in the ANCS, and the tasks are assigned to VM in an ordered fashion, resulting in resource shortages and insufficient utilization.The task execution efficiency is higher in PTPN,and the execution time is comparatively less when ANCS is used.However,tasks are not categorized, resulting in high-configuration VMs performing trivial activities and underutilized resources.When IGAFCM is used, each processor unit optimizes the available resources based on the task duration and overall execution time,decreasing the implementation latency between various VMs.When FCERS is used, process execution time is minimized.When FCERS is used, resource availability across VMs is constantly changed in response to task demand,execution time is effectively decreased,and the processing time-interval amongst processing units is considerably minimized.

    Figure 4:Comparing task execution time for different VMs

    As seen in Fig.5, the algorithm’s overall energy consumption to accomplish all operations,including computation and transmission energy utilization,is primary since the algorithm completes tasks at the quickest.The lowest general energy usage is determined using CloudSim’s built-in energy consumption model.

    As illustrated in Fig.6, the usage of memory by the proposed FCERS algorithm is comparable to that of the IGAFCM, PTPN and ANCS algorithms, completely fulfilling the memory space constraint for EC nodes.As a result,the proposed method may be implemented without compromising performance on EC nodes.

    Figure 5:Total task energy consumption

    Figure 6:Memory usage

    For Figs.7–9, we consider alternative combinations of offloading strategies and their impacts on overall computation cost, time consumption, and energy consumption, all of which vary by the number of MDs used.

    Figure 7:Total computation cost against increase in users

    Figure 8:Time consumption against increase in users

    Figure 9:Energy consumption against number of users

    To demonstrate, we evaluated two baselines.Our method reduces the total computing cost,execution time,and energy consumption on average compared to the baseline.The ES allocates higher processing resources to each user in case of limited availability of MDs;this results in improved task execution time and a reduced total task computation cost.As for EC, the energy consumption is ordered a few times or ten times that of local execution,which results in additional total computation costs.

    6 Conclusion and Future Work

    The purpose of this paper was to investigate the resource scheduling challenges in fog computing.First,we grouped the fog resources,which significantly reduces the range of user needs for resource matching.Additionally,we propose the Fuzzy Control based Edge Resource Scheduling algorithm for resource allocation.Finally,an experimental study revealed that the suggested FCERS method in this work converges quicker than the other methods.Additionally,the proposed FCERS algorithm may more efficiently fetch user requests to suitable resource categories,increasing user requirements.

    In future studies,we will address dynamic resource changes and offer a novel RSP for optimizing resource use and ensuring user requirements.

    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 the present study.

    免费看a级黄色片| 亚洲精品久久国产高清桃花| 久久久国产成人精品二区| 美女被艹到高潮喷水动态| 国产视频一区二区在线看| 免费看a级黄色片| 亚洲无线在线观看| 亚洲人成伊人成综合网2020| 中文资源天堂在线| 日韩精品青青久久久久久| 男女床上黄色一级片免费看| 俺也久久电影网| 欧美丝袜亚洲另类 | h日本视频在线播放| 18禁黄网站禁片免费观看直播| 国产一区二区亚洲精品在线观看| 日韩有码中文字幕| 日日摸夜夜添夜夜添小说| 波野结衣二区三区在线| 中文字幕人成人乱码亚洲影| 日韩av在线大香蕉| 极品教师在线视频| 看十八女毛片水多多多| 久久99热这里只有精品18| 日本a在线网址| 最新在线观看一区二区三区| 日韩国内少妇激情av| 精品人妻1区二区| 90打野战视频偷拍视频| 又爽又黄无遮挡网站| 亚洲午夜理论影院| 亚洲国产精品sss在线观看| 欧美性猛交黑人性爽| 日韩有码中文字幕| 久久久久久久久中文| 欧美一区二区精品小视频在线| 男女床上黄色一级片免费看| 精品人妻熟女av久视频| 在线观看舔阴道视频| 嫩草影视91久久| 女生性感内裤真人,穿戴方法视频| 真人一进一出gif抽搐免费| 精品人妻一区二区三区麻豆 | 久久伊人香网站| 欧美激情久久久久久爽电影| 精品一区二区免费观看| 青草久久国产| 成人欧美大片| 日本五十路高清| 国产精品爽爽va在线观看网站| 午夜激情福利司机影院| 中文资源天堂在线| 久久精品夜夜夜夜夜久久蜜豆| 性色av乱码一区二区三区2| 色视频www国产| 中文资源天堂在线| 日韩成人在线观看一区二区三区| 日本免费a在线| 成年免费大片在线观看| 黄色配什么色好看| 免费电影在线观看免费观看| 亚洲欧美清纯卡通| 国内精品久久久久精免费| 成年女人看的毛片在线观看| 成人特级av手机在线观看| 三级毛片av免费| 精品久久久久久久人妻蜜臀av| 在线观看av片永久免费下载| 乱人视频在线观看| 美女高潮的动态| 亚洲va日本ⅴa欧美va伊人久久| 亚洲国产精品999在线| 少妇裸体淫交视频免费看高清| 欧美丝袜亚洲另类 | av国产免费在线观看| 一夜夜www| 欧美中文日本在线观看视频| 一级黄色大片毛片| 国产黄a三级三级三级人| 成人av在线播放网站| 99精品在免费线老司机午夜| 国产av在哪里看| 一级毛片久久久久久久久女| 直男gayav资源| 变态另类成人亚洲欧美熟女| 色吧在线观看| 免费在线观看成人毛片| 久久久久免费精品人妻一区二区| 美女免费视频网站| 日本 欧美在线| 午夜久久久久精精品| 欧美日韩综合久久久久久 | 淫秽高清视频在线观看| 色视频www国产| 成人高潮视频无遮挡免费网站| 欧美在线黄色| 1024手机看黄色片| 一个人看视频在线观看www免费| 91av网一区二区| 欧美在线一区亚洲| 亚洲在线自拍视频| 亚洲 国产 在线| 乱人视频在线观看| 亚洲av不卡在线观看| 少妇高潮的动态图| 十八禁网站免费在线| 国产真实乱freesex| 成人精品一区二区免费| 五月伊人婷婷丁香| 男人和女人高潮做爰伦理| 啦啦啦韩国在线观看视频| 中文字幕av成人在线电影| 成人特级av手机在线观看| 国产69精品久久久久777片| 欧美激情久久久久久爽电影| 长腿黑丝高跟| 久久精品国产99精品国产亚洲性色| 欧美日韩瑟瑟在线播放| 91麻豆精品激情在线观看国产| 美女cb高潮喷水在线观看| 亚洲在线观看片| 亚洲 国产 在线| 观看美女的网站| 免费黄网站久久成人精品 | 在线免费观看不下载黄p国产 | 亚洲成人精品中文字幕电影| 亚洲五月天丁香| 国产精品一区二区免费欧美| 亚洲自拍偷在线| 免费搜索国产男女视频| 丁香六月欧美| 精华霜和精华液先用哪个| 久久久精品欧美日韩精品| 桃色一区二区三区在线观看| 麻豆成人av在线观看| АⅤ资源中文在线天堂| 中出人妻视频一区二区| 亚洲国产高清在线一区二区三| 国产一区二区激情短视频| 97超级碰碰碰精品色视频在线观看| 国产成+人综合+亚洲专区| 精品久久久久久久久亚洲 | 淫妇啪啪啪对白视频| 中文字幕av成人在线电影| 观看美女的网站| 别揉我奶头 嗯啊视频| 成人特级黄色片久久久久久久| 床上黄色一级片| 麻豆av噜噜一区二区三区| 国内精品一区二区在线观看| 国产黄a三级三级三级人| 亚洲一区高清亚洲精品| 亚洲中文字幕日韩| 一个人免费在线观看电影| 人妻久久中文字幕网| 国产精品99久久久久久久久| 搡老熟女国产l中国老女人| bbb黄色大片| 国产精品伦人一区二区| 蜜桃亚洲精品一区二区三区| 乱码一卡2卡4卡精品| 亚洲国产日韩欧美精品在线观看| 在线观看66精品国产| 免费看a级黄色片| 国产一区二区激情短视频| 久久久色成人| 国产av一区在线观看免费| 亚洲国产精品合色在线| 亚洲精品在线观看二区| 变态另类丝袜制服| 麻豆国产av国片精品| 直男gayav资源| 国产精品乱码一区二三区的特点| 成人三级黄色视频| 久久久国产成人免费| 熟妇人妻久久中文字幕3abv| 久久人人爽人人爽人人片va | 国产免费男女视频| www.999成人在线观看| 欧美黄色淫秽网站| 色哟哟哟哟哟哟| 国产精品女同一区二区软件 | 噜噜噜噜噜久久久久久91| 美女高潮的动态| 国产视频一区二区在线看| 亚洲无线在线观看| 黄色丝袜av网址大全| 国产乱人视频| 亚洲欧美清纯卡通| 欧美+日韩+精品| 久久精品夜夜夜夜夜久久蜜豆| 麻豆成人午夜福利视频| 黄色一级大片看看| 琪琪午夜伦伦电影理论片6080| 亚洲人成网站在线播| 我的老师免费观看完整版| 一夜夜www| 99热这里只有是精品50| 婷婷色综合大香蕉| 亚洲欧美日韩高清专用| 亚洲专区国产一区二区| 国内毛片毛片毛片毛片毛片| 啦啦啦观看免费观看视频高清| 2021天堂中文幕一二区在线观| 18禁裸乳无遮挡免费网站照片| 国产精品人妻久久久久久| 久久精品夜夜夜夜夜久久蜜豆| 国产人妻一区二区三区在| 日本熟妇午夜| a级毛片a级免费在线| 国产成人啪精品午夜网站| 国内少妇人妻偷人精品xxx网站| 狠狠狠狠99中文字幕| 男女视频在线观看网站免费| 赤兔流量卡办理| avwww免费| 熟女人妻精品中文字幕| 亚洲第一区二区三区不卡| 高潮久久久久久久久久久不卡| 麻豆成人av在线观看| 91午夜精品亚洲一区二区三区 | 天堂动漫精品| 亚洲五月天丁香| 久99久视频精品免费| 午夜免费男女啪啪视频观看 | 99精品久久久久人妻精品| 色av中文字幕| 亚洲中文字幕日韩| 久久精品国产清高在天天线| 嫁个100分男人电影在线观看| 老师上课跳d突然被开到最大视频 久久午夜综合久久蜜桃 | 亚洲经典国产精华液单 | 可以在线观看的亚洲视频| 老熟妇乱子伦视频在线观看| 国产伦人伦偷精品视频| 成人欧美大片| 一进一出好大好爽视频| 成人亚洲精品av一区二区| 91久久精品电影网| 嫁个100分男人电影在线观看| 国产精品99久久久久久久久| 淫秽高清视频在线观看| 中文字幕久久专区| 午夜福利欧美成人| 午夜激情福利司机影院| 亚洲 欧美 日韩 在线 免费| 亚洲精品影视一区二区三区av| 午夜日韩欧美国产| 九色成人免费人妻av| 在线播放国产精品三级| 美女免费视频网站| 51国产日韩欧美| 一级黄色大片毛片| 在线十欧美十亚洲十日本专区| 日韩欧美一区二区三区在线观看| 蜜桃亚洲精品一区二区三区| 日本与韩国留学比较| 一本一本综合久久| 麻豆成人av在线观看| 天美传媒精品一区二区| 好看av亚洲va欧美ⅴa在| 一二三四社区在线视频社区8| 亚洲av电影不卡..在线观看| 日韩欧美精品v在线| 久久久久国内视频| 中文字幕人妻熟人妻熟丝袜美| 日韩精品青青久久久久久| 51午夜福利影视在线观看| 国产69精品久久久久777片| 91久久精品电影网| 国产欧美日韩一区二区精品| h日本视频在线播放| 一级毛片久久久久久久久女| 欧美日本视频| 国产高清视频在线观看网站| 亚洲久久久久久中文字幕| 久久久久亚洲av毛片大全| 成年人黄色毛片网站| 欧美激情久久久久久爽电影| 最近中文字幕高清免费大全6 | 黄色一级大片看看| 欧美性感艳星| 国产熟女xx| 哪里可以看免费的av片| 精品久久久久久久久久免费视频| 久久精品国产亚洲av香蕉五月| 最新在线观看一区二区三区| av在线观看视频网站免费| 日本 欧美在线| 日韩大尺度精品在线看网址| 动漫黄色视频在线观看| 国产av不卡久久| 99热6这里只有精品| 少妇熟女aⅴ在线视频| 久久精品国产亚洲av香蕉五月| 欧美丝袜亚洲另类 | 九色成人免费人妻av| 国产综合懂色| 免费av毛片视频| 婷婷丁香在线五月| 少妇人妻一区二区三区视频| 国产美女午夜福利| 日韩免费av在线播放| 男插女下体视频免费在线播放| 亚洲国产精品合色在线| av视频在线观看入口| 99久久99久久久精品蜜桃| 99在线人妻在线中文字幕| 九色国产91popny在线| 久久精品夜夜夜夜夜久久蜜豆| 欧美中文日本在线观看视频| 日韩欧美精品v在线| 国内精品久久久久久久电影| 91午夜精品亚洲一区二区三区 | 国产欧美日韩精品一区二区| 长腿黑丝高跟| 欧美最黄视频在线播放免费| 亚洲熟妇熟女久久| 精品国内亚洲2022精品成人| 国产黄a三级三级三级人| 久久久久久久亚洲中文字幕 | 午夜免费男女啪啪视频观看 | 99热只有精品国产| 国产在线精品亚洲第一网站| 国产精品一区二区三区四区久久| 男女那种视频在线观看| 精品乱码久久久久久99久播| 亚洲不卡免费看| 91av网一区二区| 最新在线观看一区二区三区| 久久国产乱子伦精品免费另类| 免费在线观看亚洲国产| 最好的美女福利视频网| 99久久精品国产亚洲精品| 成人国产一区最新在线观看| 免费在线观看亚洲国产| 国产三级在线视频| 久久99热6这里只有精品| 国产三级中文精品| 午夜激情福利司机影院| 精品人妻1区二区| 午夜激情欧美在线| 国产精品久久久久久亚洲av鲁大| 热99re8久久精品国产| 国产爱豆传媒在线观看| 一级作爱视频免费观看| 久久精品人妻少妇| 免费在线观看成人毛片| 好男人电影高清在线观看| 亚洲人成电影免费在线| 欧美+亚洲+日韩+国产| 熟女人妻精品中文字幕| 此物有八面人人有两片| 精品无人区乱码1区二区| 成人av一区二区三区在线看| 狂野欧美白嫩少妇大欣赏| 一区二区三区高清视频在线| 亚洲人成电影免费在线| 神马国产精品三级电影在线观看| 国内精品久久久久久久电影| 欧美日本亚洲视频在线播放| 亚洲人成伊人成综合网2020| 色在线成人网| 亚洲av电影不卡..在线观看| 九九久久精品国产亚洲av麻豆| 亚洲久久久久久中文字幕| 久久精品夜夜夜夜夜久久蜜豆| 校园春色视频在线观看| 国产高清激情床上av| 精品99又大又爽又粗少妇毛片 | 国产精品久久视频播放| 在线观看免费视频日本深夜| 免费观看的影片在线观看| 熟女电影av网| 欧美乱妇无乱码| 身体一侧抽搐| 国产成人aa在线观看| 三级国产精品欧美在线观看| 欧美乱妇无乱码| 色哟哟哟哟哟哟| 亚洲国产色片| 嫩草影视91久久| 老司机午夜十八禁免费视频| 好男人电影高清在线观看| 午夜激情欧美在线| 亚洲欧美日韩无卡精品| 老司机福利观看| 亚洲片人在线观看| 级片在线观看| 天堂√8在线中文| 精品久久久久久久久久久久久| 免费av毛片视频| 一级av片app| 少妇被粗大猛烈的视频| 日本三级黄在线观看| 国产精品av视频在线免费观看| 亚洲avbb在线观看| 色吧在线观看| 窝窝影院91人妻| 国产精品日韩av在线免费观看| 日韩欧美在线乱码| 搡老妇女老女人老熟妇| 免费av不卡在线播放| 18美女黄网站色大片免费观看| 一级黄色大片毛片| 久9热在线精品视频| 大型黄色视频在线免费观看| 麻豆av噜噜一区二区三区| 91av网一区二区| 精品久久久久久成人av| 黄片小视频在线播放| 人妻丰满熟妇av一区二区三区| 麻豆国产97在线/欧美| 中文字幕高清在线视频| 51午夜福利影视在线观看| 看十八女毛片水多多多| 亚洲成av人片免费观看| 好男人电影高清在线观看| 亚洲成a人片在线一区二区| 亚洲欧美日韩无卡精品| www.熟女人妻精品国产| АⅤ资源中文在线天堂| 日韩欧美在线乱码| 亚洲av熟女| 99久久99久久久精品蜜桃| 我要看日韩黄色一级片| 一本综合久久免费| 中亚洲国语对白在线视频| 亚洲中文字幕日韩| 国产麻豆成人av免费视频| 亚洲精品在线观看二区| 欧美极品一区二区三区四区| 高清日韩中文字幕在线| 国产激情偷乱视频一区二区| 男女之事视频高清在线观看| 又紧又爽又黄一区二区| 真人一进一出gif抽搐免费| 97碰自拍视频| 人妻久久中文字幕网| 亚洲综合色惰| 脱女人内裤的视频| av在线天堂中文字幕| 在线观看av片永久免费下载| 一级av片app| 久久99热这里只有精品18| 成人美女网站在线观看视频| 国产精品自产拍在线观看55亚洲| 日韩av在线大香蕉| 一个人观看的视频www高清免费观看| 精品一区二区三区av网在线观看| 一a级毛片在线观看| 亚洲成a人片在线一区二区| 免费在线观看影片大全网站| 精品久久久久久久末码| 日本熟妇午夜| 国产精品自产拍在线观看55亚洲| 免费观看精品视频网站| 国产精品一及| 国内久久婷婷六月综合欲色啪| 亚洲精品日韩av片在线观看| 精品无人区乱码1区二区| 人妻夜夜爽99麻豆av| 国产成人影院久久av| 欧美不卡视频在线免费观看| 国内毛片毛片毛片毛片毛片| 亚洲午夜理论影院| 国产一区二区亚洲精品在线观看| 淫妇啪啪啪对白视频| 久久精品国产清高在天天线| 欧美高清性xxxxhd video| 五月伊人婷婷丁香| 国产v大片淫在线免费观看| 人人妻,人人澡人人爽秒播| 欧美xxxx性猛交bbbb| 成人av一区二区三区在线看| 亚洲欧美清纯卡通| 在线观看66精品国产| 免费看a级黄色片| 偷拍熟女少妇极品色| 欧美最黄视频在线播放免费| 国产欧美日韩一区二区三| 色尼玛亚洲综合影院| 国产免费av片在线观看野外av| 成人一区二区视频在线观看| 性色av乱码一区二区三区2| 99视频精品全部免费 在线| 日韩 亚洲 欧美在线| 五月伊人婷婷丁香| 亚洲在线自拍视频| 少妇裸体淫交视频免费看高清| 韩国av一区二区三区四区| or卡值多少钱| 桃色一区二区三区在线观看| 特级一级黄色大片| 嫩草影院精品99| 在线观看舔阴道视频| 听说在线观看完整版免费高清| 午夜日韩欧美国产| 黄色视频,在线免费观看| 国产伦在线观看视频一区| 听说在线观看完整版免费高清| 欧美高清成人免费视频www| 九色成人免费人妻av| 精品国内亚洲2022精品成人| 亚洲熟妇中文字幕五十中出| 老师上课跳d突然被开到最大视频 久久午夜综合久久蜜桃 | 色综合亚洲欧美另类图片| 99热这里只有精品一区| 在线观看一区二区三区| 99久国产av精品| 亚洲精品成人久久久久久| 久久精品国产99精品国产亚洲性色| 国产午夜精品久久久久久一区二区三区 | 性欧美人与动物交配| 精品人妻视频免费看| 久久久国产成人精品二区| 精品人妻视频免费看| 如何舔出高潮| 热99在线观看视频| 乱码一卡2卡4卡精品| 欧美精品国产亚洲| 亚洲三级黄色毛片| 亚洲av日韩精品久久久久久密| 看黄色毛片网站| 国产高清有码在线观看视频| 12—13女人毛片做爰片一| 精品午夜福利视频在线观看一区| 天堂√8在线中文| 人妻制服诱惑在线中文字幕| 麻豆成人午夜福利视频| 在线观看一区二区三区| 真人做人爱边吃奶动态| 午夜福利在线观看吧| 国产精品久久久久久亚洲av鲁大| 国产av一区在线观看免费| or卡值多少钱| 全区人妻精品视频| 欧美丝袜亚洲另类 | 禁无遮挡网站| 91av网一区二区| 看免费av毛片| 亚洲aⅴ乱码一区二区在线播放| 一区二区三区高清视频在线| 亚洲久久久久久中文字幕| 久久久久久久午夜电影| 亚洲国产精品合色在线| 可以在线观看的亚洲视频| 国产精品av视频在线免费观看| 赤兔流量卡办理| 久久午夜福利片| 国产av在哪里看| 国产综合懂色| 性色av乱码一区二区三区2| av在线蜜桃| 色综合欧美亚洲国产小说| 亚洲熟妇中文字幕五十中出| 成人国产综合亚洲| 少妇人妻精品综合一区二区 | 欧美日韩亚洲国产一区二区在线观看| 色哟哟·www| 成人高潮视频无遮挡免费网站| 国产真实乱freesex| 日本 av在线| 成人精品一区二区免费| a在线观看视频网站| 精品人妻视频免费看| 精品国内亚洲2022精品成人| 男女视频在线观看网站免费| 亚洲精品在线美女| 狂野欧美白嫩少妇大欣赏| 婷婷亚洲欧美| 国产欧美日韩一区二区三| 国产精品永久免费网站| 国产欧美日韩一区二区三| 国产又黄又爽又无遮挡在线| 亚洲av美国av| 亚洲在线观看片| 变态另类丝袜制服| 窝窝影院91人妻| 乱人视频在线观看| 搞女人的毛片| 欧美成人a在线观看| 成人特级黄色片久久久久久久| 亚洲最大成人手机在线| 国产午夜福利久久久久久| 99在线视频只有这里精品首页| 两个人的视频大全免费| 国产淫片久久久久久久久 | 黄色女人牲交| 淫妇啪啪啪对白视频| 亚洲最大成人av| 国内精品一区二区在线观看| 日韩高清综合在线| ponron亚洲| 久久久国产成人精品二区| 国产精品久久久久久精品电影| 久久久久久久亚洲中文字幕 | 亚洲av中文字字幕乱码综合| 俄罗斯特黄特色一大片| 欧美不卡视频在线免费观看| 男插女下体视频免费在线播放| 中文字幕av成人在线电影| 老司机午夜福利在线观看视频| 久久精品夜夜夜夜夜久久蜜豆| 天堂网av新在线| 日韩欧美免费精品| 国产精品亚洲一级av第二区| 能在线免费观看的黄片| 美女免费视频网站| 男女那种视频在线观看| 国产精品自产拍在线观看55亚洲| 久久久久国产精品人妻aⅴ院| 性色avwww在线观看| 亚洲精品亚洲一区二区| 51午夜福利影视在线观看|