As the extensive use of cloud computing raises questions about the security of any personal data stored there,cryptography is being used more frequently as a security tool to protect data confidentiality and privacy i...As the extensive use of cloud computing raises questions about the security of any personal data stored there,cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment.A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware.The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment.An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance;Each hypervisor should be examined to meet specific needs.The main objective of this study is to provide accurate results to compare the performance of Hyper-V and Kernel-based Virtual Machine(KVM)while implementing different cryptographic algorithms to guide cloud service providers and end users in choosing the most suitable hypervisor for their cryptographic needs.This study evaluated the efficiency of two hypervisors,Hyper-V and KVM,in implementing six cryptographic algorithms:Rivest,Shamir,Adleman(RSA),Advanced Encryption Standard(AES),Triple Data Encryption Standard(TripleDES),Carlisle Adams and Stafford Tavares(CAST-128),BLOWFISH,and TwoFish.The study’s findings show that KVM outperforms Hyper-V,with 12.2%less Central Processing Unit(CPU)use and 12.95%less time overall for encryption and decryption operations with various file sizes.The study’s findings emphasize how crucial it is to pick a hypervisor that is appropriate for cryptographic needs in a cloud environment,which could assist both cloud service providers and end users.Future research may focus more on how various hypervisors perform while handling cryptographic workloads.展开更多
Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.How...Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.展开更多
Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay ...Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).展开更多
Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the chall...Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.展开更多
This study investigates how cybersecurity can be enhanced through cloud computing solutions in the United States. The motive for this study is due to the rampant loss of data, breaches, and unauthorized access of inte...This study investigates how cybersecurity can be enhanced through cloud computing solutions in the United States. The motive for this study is due to the rampant loss of data, breaches, and unauthorized access of internet criminals in the United States. The study adopted a survey research design, collecting data from 890 cloud professionals with relevant knowledge of cybersecurity and cloud computing. A machine learning approach was adopted, specifically a random forest classifier, an ensemble, and a decision tree model. Out of the features in the data, ten important features were selected using random forest feature importance, which helps to achieve the objective of the study. The study’s purpose is to enable organizations to develop suitable techniques to prevent cybercrime using random forest predictions as they relate to cloud services in the United States. The effectiveness of the models used is evaluated by utilizing validation matrices that include recall values, accuracy, and precision, in addition to F1 scores and confusion matrices. Based on evaluation scores (accuracy, precision, recall, and F1 scores) of 81.9%, 82.6%, and 82.1%, the results demonstrated the effectiveness of the random forest model. It showed the importance of machine learning algorithms in preventing cybercrime and boosting security in the cloud environment. It recommends that other machine learning models be adopted to see how to improve cybersecurity through cloud computing.展开更多
The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource pr...The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.展开更多
Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this r...Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this regard. The findings have shown that many challenges are linked to edge computing, such as privacy concerns, security breaches, high costs, low efficiency, etc. Therefore, there is a need to implement proper security measures to overcome these issues. Using emerging trends, like machine learning, encryption, artificial intelligence, real-time monitoring, etc., can help mitigate security issues. They can also develop a secure and safe future in cloud computing. It was concluded that the security implications of edge computing can easily be covered with the help of new technologies and techniques.展开更多
Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concer...Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.展开更多
Serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud programming.With serverless computing,developers only provide function code to serverless platform,and these functions are ...Serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud programming.With serverless computing,developers only provide function code to serverless platform,and these functions are invoked by its driven events.Nonetheless,security threats in serverless computing such as vulnerability-based security threats have become the pain point hindering its wide adoption.The ideas in proactive defense such as redundancy,diversity and dynamic provide promising approaches to protect against cyberattacks.However,these security technologies are mostly applied to serverless platform based on“stacked”mode,as they are designed independent with serverless computing.The lack of security consideration in the initial design makes it especially challenging to achieve the all life cycle protection for serverless application with limited cost.In this paper,we present ATSSC,a proactive defense enabled attack tolerant serverless platform.ATSSC integrates the characteristic of redundancy,diversity and dynamic into serverless seamless to achieve high-level security and efficiency.Specifically,ATSSC constructs multiple diverse function replicas to process the driven events and performs cross-validation to verify the results.In order to create diverse function replicas,both software diversity and environment diversity are adopted.Furthermore,a dynamic function refresh strategy is proposed to keep the clean state of serverless functions.We implement ATSSC based on Kubernetes and Knative.Analysis and experimental results demonstrate that ATSSC can effectively protect serverless computing against cyberattacks with acceptable costs.展开更多
Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the ...Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.展开更多
With the rapid development of the Internet of Things(IoT),there are several challenges pertaining to security in IoT applications.Compared with the characteristics of the traditional Internet,the IoT has many problems...With the rapid development of the Internet of Things(IoT),there are several challenges pertaining to security in IoT applications.Compared with the characteristics of the traditional Internet,the IoT has many problems,such as large assets,complex and diverse structures,and lack of computing resources.Traditional network intrusion detection systems cannot meet the security needs of IoT applications.In view of this situation,this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance.Usually,traditional intrusion detection algorithms require considerable time for training,and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes;therefore,it is necessary to study intrusion detection algorithms with low weights,short training time,and high detection accuracy for deployment and application on cloud nodes.An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats.This paper discusses the problems related to IoT intrusion prevention in cloud computing environments.Based on the analysis of cloud computing security threats,this study extensively explores IoT intrusion detection,cloud node monitoring,and intrusion response in cloud computing environments by using cloud computing,an improved extreme learning machine,and other methods.We use the Multi-Feature Extraction Extreme Learning Machine(MFE-ELM)algorithm for cloud computing,which adds a multi-feature extraction process to cloud servers,and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes.In our simulation experiments,a classical dataset for intrusion detection is selected as a test,and test steps such as data preprocessing,feature engineering,model training,and result analysis are performed.The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes.Furthermore,it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time,so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.展开更多
Through the use of the internet and cloud computing,users may access their data as well as the programmes they have installed.It is now more challenging than ever before to choose which cloud service providers to take...Through the use of the internet and cloud computing,users may access their data as well as the programmes they have installed.It is now more challenging than ever before to choose which cloud service providers to take advantage of.When it comes to the dependability of the cloud infrastructure service,those who supply cloud services,as well as those who seek cloud services,have an equal responsibility to exercise utmost care.Because of this,further caution is required to ensure that the appropriate values are reached in light of the ever-increasing need for correct decision-making.The purpose of this study is to provide an updated computational ranking approach for decision-making in an environment with many criteria by using fuzzy logic in the context of a public cloud scenario.This improved computational ranking system is also sometimes referred to as the improvised VlseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)method.It gives users access to a trustworthy assortment of cloud services that fit their needs.The activity that is part of the suggested technique has been broken down into nine discrete parts for your convenience.To verify these stages,a numerical example has been evaluated for each of the six different scenarios,and the outcomes have been simulated.展开更多
This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits ...This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.展开更多
Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applic...Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.展开更多
Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity...Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years now.Confidential information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the IIoT.There are also significant security risks,concerns,and difficulties associated with cloud computing.To address these challenges,we propose merging BC and SDN into a cloud computing platform for the IIoT.This paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT applications.The proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and scalable.Customers in the industrial sector benefit from the dispersed or decentralized,and efficient environment of BC.Additionally,we described an SDN method to improve the durability,stability,and load balancing of cloud infrastructure.The efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.展开更多
To serve various tasks requested by various end devices with different requirements,end-edge-cloud(E2C)has attracted more and more attention from specialists in both academia and industry,by combining both benefits of...To serve various tasks requested by various end devices with different requirements,end-edge-cloud(E2C)has attracted more and more attention from specialists in both academia and industry,by combining both benefits of edge and cloud computing.But nowadays,E2C still suffers from low service quality and resource efficiency,due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility.To address these issues,this paper focuses on task offloading,which makes decisions that which resources are allocated to tasks for their processing.This paper first formulates the problem into binary non-linear programming and then proposes a particle swarm optimization(PSO)-based algorithm to solve the problem.The proposed algorithm exploits an imbalance mutation operator and a task rescheduling approach to improve the performance of PSO.The proposed algorithm concerns the resource heterogeneity by correlating the probability that a computing node is decided to process a task with its capacity,by the imbalance mutation.The task rescheduling approach improves the acceptance ratio for a task offloading solution,by reassigning rejected tasks to computing nodes with available resources.Extensive simulated experiments are conducted.And the results show that the proposed offloading algorithm has an 8.93%–37.0%higher acceptance ratio than ten of the classical and up-to-date algorithms,and verify the effectiveness of the imbalanced mutation and the task rescheduling.展开更多
Star sensors are an important means of autonomous navigation and access to space information for satellites.They have been widely deployed in the aerospace field.To satisfy the requirements for high resolution,timelin...Star sensors are an important means of autonomous navigation and access to space information for satellites.They have been widely deployed in the aerospace field.To satisfy the requirements for high resolution,timeliness,and confidentiality of star images,we propose an edge computing algorithm based on the star sensor cloud.Multiple sensors cooperate with each other to forma sensor cloud,which in turn extends the performance of a single sensor.The research on the data obtained by the star sensor has very important research and application values.First,a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition,which can reduce the amount of data computation.Then,a mappingmodel between content and space is constructed to achieve low-rank image representation and efficient computation.Finally,the data collected by the wireless sensor is delivered to the edge server,and a differentmethod is used to achieve privacy protection.Only a small amount of core data is stored in edge servers and local servers,and other data is transmitted to the cloud.Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage,and has strong privacy.展开更多
In recent years,statistics have indicated that the number of patients with malignant brain tumors has increased sharply.However,most surgeons still perform surgical training using the traditional autopsy and prosthesi...In recent years,statistics have indicated that the number of patients with malignant brain tumors has increased sharply.However,most surgeons still perform surgical training using the traditional autopsy and prosthesis model,which encounters many problems,such as insufficient corpse resources,low efficiency,and high cost.With the advent of the 5G era,a wide range of Industrial Internet of Things(IIOT)applications have been developed.Virtual Reality(VR)and Augmented Reality(AR)technologies that emerged with 5G are developing rapidly for intelligent medical training.To address the challenges encountered during neurosurgery training,and combining with cloud computing,in this paper,a highly immersive AR-based brain tumor neurosurgery remote collaborative virtual surgery training system is developed,in which a VR simulator is embedded.The system enables real-time remote surgery training interaction through 5G transmission.Six experts and 18 novices were invited to participate in the experiment to verify the system.Subsequently,the two simulators were evaluated using face and construction validation methods.The results obtained by training the novices 50 times were further analyzed using the Learning Curve-Cumulative Sum(LC-CUSUM)evaluation method to validate the effectiveness of the two simulators.The results of the face and content validation demonstrated that the AR simulator in the system was superior to the VR simulator in terms of vision and scene authenticity,and had a better effect on the improvement of surgical skills.Moreover,the surgical training scheme proposed in this paper is effective,and the remote collaborative training effect of the system is ideal.展开更多
Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encrypt...Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed.In cloud computation,data processing,storage,and transmission can be done through laptops andmobile devices.Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients.The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data.They have to be dependent on cloud service providers for assurance of the platform’s security.Data security and privacy issues reduce the progression of cloud computing and add complexity.Nowadays;most of the data that is stored on cloud servers is in the form of images and photographs,which is a very confidential form of data that requires secured transmission.In this research work,a public key cryptosystem is being implemented to store,retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman(RSA)algorithm for the encryption and decryption of data.The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment.To enhance the user data security level,a neural network is used for user authentication and recognition.Moreover;the proposed technique develops the performance of detection as a loss function of the bounding box.The Faster Region-Based Convolutional Neural Network(Faster R-CNN)gets trained on images to identify authorized users with an accuracy of 99.9%on training.展开更多
In the cloud environment,the transfer of data from one cloud server to another cloud server is called migration.Data can be delivered in various ways,from one data centre to another.This research aims to increase the ...In the cloud environment,the transfer of data from one cloud server to another cloud server is called migration.Data can be delivered in various ways,from one data centre to another.This research aims to increase the migration performance of the virtual machine(VM)in the cloud environment.VMs allow cloud customers to store essential data and resources.However,server usage has grown dramatically due to the virtualization of computer systems,resulting in higher data centre power consumption,storage needs,and operating expenses.Multiple VMs on one data centre manage share resources like central processing unit(CPU)cache,network bandwidth,memory,and application bandwidth.Inmulti-cloud,VMmigration addresses the performance degradation due to cloud server configuration,unbalanced traffic load,resource load management,and fault situations during data transfer.VMmigration speed is influenced by the size of the VM,the dirty rate of the running application,and the latency ofmigration iterations.As a result,evaluating VM migration performance while considering all of these factors becomes a difficult task.Themain effort of this research is to assess migration problems on performance.The simulation results in Matlab show that if the VMsize grows,themigration time of VMs and the downtime can be impacted by three orders ofmagnitude.The dirty page rate decreases,themigration time and the downtime grow,and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VMtransfer is completed.All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.展开更多
文摘As the extensive use of cloud computing raises questions about the security of any personal data stored there,cryptography is being used more frequently as a security tool to protect data confidentiality and privacy in the cloud environment.A hypervisor is a virtualization software used in cloud hosting to divide and allocate resources on various pieces of hardware.The choice of hypervisor can significantly impact the performance of cryptographic operations in the cloud environment.An important issue that must be carefully examined is that no hypervisor is completely superior in terms of performance;Each hypervisor should be examined to meet specific needs.The main objective of this study is to provide accurate results to compare the performance of Hyper-V and Kernel-based Virtual Machine(KVM)while implementing different cryptographic algorithms to guide cloud service providers and end users in choosing the most suitable hypervisor for their cryptographic needs.This study evaluated the efficiency of two hypervisors,Hyper-V and KVM,in implementing six cryptographic algorithms:Rivest,Shamir,Adleman(RSA),Advanced Encryption Standard(AES),Triple Data Encryption Standard(TripleDES),Carlisle Adams and Stafford Tavares(CAST-128),BLOWFISH,and TwoFish.The study’s findings show that KVM outperforms Hyper-V,with 12.2%less Central Processing Unit(CPU)use and 12.95%less time overall for encryption and decryption operations with various file sizes.The study’s findings emphasize how crucial it is to pick a hypervisor that is appropriate for cryptographic needs in a cloud environment,which could assist both cloud service providers and end users.Future research may focus more on how various hypervisors perform while handling cryptographic workloads.
基金supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.
文摘Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).
基金jointly supported by the Jiangsu Postgraduate Research and Practice Innovation Project under Grant KYCX22_1030,SJCX22_0283 and SJCX23_0293the NUPTSF under Grant NY220201.
文摘Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.
文摘This study investigates how cybersecurity can be enhanced through cloud computing solutions in the United States. The motive for this study is due to the rampant loss of data, breaches, and unauthorized access of internet criminals in the United States. The study adopted a survey research design, collecting data from 890 cloud professionals with relevant knowledge of cybersecurity and cloud computing. A machine learning approach was adopted, specifically a random forest classifier, an ensemble, and a decision tree model. Out of the features in the data, ten important features were selected using random forest feature importance, which helps to achieve the objective of the study. The study’s purpose is to enable organizations to develop suitable techniques to prevent cybercrime using random forest predictions as they relate to cloud services in the United States. The effectiveness of the models used is evaluated by utilizing validation matrices that include recall values, accuracy, and precision, in addition to F1 scores and confusion matrices. Based on evaluation scores (accuracy, precision, recall, and F1 scores) of 81.9%, 82.6%, and 82.1%, the results demonstrated the effectiveness of the random forest model. It showed the importance of machine learning algorithms in preventing cybercrime and boosting security in the cloud environment. It recommends that other machine learning models be adopted to see how to improve cybersecurity through cloud computing.
文摘The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.
文摘Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this regard. The findings have shown that many challenges are linked to edge computing, such as privacy concerns, security breaches, high costs, low efficiency, etc. Therefore, there is a need to implement proper security measures to overcome these issues. Using emerging trends, like machine learning, encryption, artificial intelligence, real-time monitoring, etc., can help mitigate security issues. They can also develop a secure and safe future in cloud computing. It was concluded that the security implications of edge computing can easily be covered with the help of new technologies and techniques.
文摘Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant No.61521003the National Natural Science Foundation of China under Grant No.62072467 and 62002383.
文摘Serverless computing is a promising paradigm in cloud computing that greatly simplifies cloud programming.With serverless computing,developers only provide function code to serverless platform,and these functions are invoked by its driven events.Nonetheless,security threats in serverless computing such as vulnerability-based security threats have become the pain point hindering its wide adoption.The ideas in proactive defense such as redundancy,diversity and dynamic provide promising approaches to protect against cyberattacks.However,these security technologies are mostly applied to serverless platform based on“stacked”mode,as they are designed independent with serverless computing.The lack of security consideration in the initial design makes it especially challenging to achieve the all life cycle protection for serverless application with limited cost.In this paper,we present ATSSC,a proactive defense enabled attack tolerant serverless platform.ATSSC integrates the characteristic of redundancy,diversity and dynamic into serverless seamless to achieve high-level security and efficiency.Specifically,ATSSC constructs multiple diverse function replicas to process the driven events and performs cross-validation to verify the results.In order to create diverse function replicas,both software diversity and environment diversity are adopted.Furthermore,a dynamic function refresh strategy is proposed to keep the clean state of serverless functions.We implement ATSSC based on Kubernetes and Knative.Analysis and experimental results demonstrate that ATSSC can effectively protect serverless computing against cyberattacks with acceptable costs.
文摘Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.
基金funded by the Key Research and Development plan of Jiangsu Province (Social Development)No.BE20217162Jiangsu Modern Agricultural Machinery Equipment and Technology Demonstration and Promotion Project No.NJ2021-19.
文摘With the rapid development of the Internet of Things(IoT),there are several challenges pertaining to security in IoT applications.Compared with the characteristics of the traditional Internet,the IoT has many problems,such as large assets,complex and diverse structures,and lack of computing resources.Traditional network intrusion detection systems cannot meet the security needs of IoT applications.In view of this situation,this study applies cloud computing and machine learning to the intrusion detection system of IoT to improve detection performance.Usually,traditional intrusion detection algorithms require considerable time for training,and these intrusion detection algorithms are not suitable for cloud computing due to the limited computing power and storage capacity of cloud nodes;therefore,it is necessary to study intrusion detection algorithms with low weights,short training time,and high detection accuracy for deployment and application on cloud nodes.An appropriate classification algorithm is a primary factor for deploying cloud computing intrusion prevention systems and a prerequisite for the system to respond to intrusion and reduce intrusion threats.This paper discusses the problems related to IoT intrusion prevention in cloud computing environments.Based on the analysis of cloud computing security threats,this study extensively explores IoT intrusion detection,cloud node monitoring,and intrusion response in cloud computing environments by using cloud computing,an improved extreme learning machine,and other methods.We use the Multi-Feature Extraction Extreme Learning Machine(MFE-ELM)algorithm for cloud computing,which adds a multi-feature extraction process to cloud servers,and use the deployed MFE-ELM algorithm on cloud nodes to detect and discover network intrusions to cloud nodes.In our simulation experiments,a classical dataset for intrusion detection is selected as a test,and test steps such as data preprocessing,feature engineering,model training,and result analysis are performed.The experimental results show that the proposed algorithm can effectively detect and identify most network data packets with good model performance and achieve efficient intrusion detection for heterogeneous data of the IoT from cloud nodes.Furthermore,it can enable the cloud server to discover nodes with serious security threats in the cloud cluster in real time,so that further security protection measures can be taken to obtain the optimal intrusion response strategy for the cloud cluster.
文摘Through the use of the internet and cloud computing,users may access their data as well as the programmes they have installed.It is now more challenging than ever before to choose which cloud service providers to take advantage of.When it comes to the dependability of the cloud infrastructure service,those who supply cloud services,as well as those who seek cloud services,have an equal responsibility to exercise utmost care.Because of this,further caution is required to ensure that the appropriate values are reached in light of the ever-increasing need for correct decision-making.The purpose of this study is to provide an updated computational ranking approach for decision-making in an environment with many criteria by using fuzzy logic in the context of a public cloud scenario.This improved computational ranking system is also sometimes referred to as the improvised VlseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)method.It gives users access to a trustworthy assortment of cloud services that fit their needs.The activity that is part of the suggested technique has been broken down into nine discrete parts for your convenience.To verify these stages,a numerical example has been evaluated for each of the six different scenarios,and the outcomes have been simulated.
基金funded by University Grant Commission with UGC-Ref.No.:3364/(NET-JUNE 2015).
文摘This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan.The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly.It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function.It works for 3-tier architecture,including cloudlet and public cloud.As cloudlets have limited resources,fuzzy logic is used for cloudlet selection using capacity and waiting time as input.Fuzzy provides human-like decisions without using any mathematical model.Firefly is a powerful meta-heuristic optimization technique to balance diversification and solution speed.It balances the load on cloud and cloudlet while minimizing makespan and execution time.However,it may trap in local optimum;levy flight can handle it.Hybridization of fuzzy fireflywith levy flight is a novel technique that provides reduced makespan,execution time,and Degree of imbalance while balancing the load.Simulation has been carried out on the Cloud Analyst platform with National Aeronautics and Space Administration(NASA)and Clarknet datasets.Results show that the proposed algorithm outperforms Ant Colony Optimization Queue Decision Maker(ACOQDM),Distributed Scheduling Optimization Algorithm(DSOA),andUtility-based Firefly Algorithm(UFA)when compared in terms of makespan,Degree of imbalance,and Figure of Merit.
基金supported by the Bio and Medical Technology Development Program of the National Research Foundation(NRF)funded by the Korean government(MSIT)(No.NRF-2019M3E5D1A02069073)supported by the Soonchunhyang University Research Fund.
文摘Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.
基金Supporting Project number(RSP2023R34)King Saud University,Riyadh,Saudi Arabia.
文摘Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years now.Confidential information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the IIoT.There are also significant security risks,concerns,and difficulties associated with cloud computing.To address these challenges,we propose merging BC and SDN into a cloud computing platform for the IIoT.This paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT applications.The proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and scalable.Customers in the industrial sector benefit from the dispersed or decentralized,and efficient environment of BC.Additionally,we described an SDN method to improve the durability,stability,and load balancing of cloud infrastructure.The efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.
基金supported by the key scientific and technological projects of Henan Province with Grant No.232102211084the Natural Science Foundation of Henan with Grant No.222300420582+2 种基金the Key Scientific Research Projects of Henan Higher School with Grant No.22A520033Zhengzhou Basic Research and Applied Research Project with Grant No.ZZSZX202107China Logistics Society with Grant No.2022CSLKT3-334.
文摘To serve various tasks requested by various end devices with different requirements,end-edge-cloud(E2C)has attracted more and more attention from specialists in both academia and industry,by combining both benefits of edge and cloud computing.But nowadays,E2C still suffers from low service quality and resource efficiency,due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility.To address these issues,this paper focuses on task offloading,which makes decisions that which resources are allocated to tasks for their processing.This paper first formulates the problem into binary non-linear programming and then proposes a particle swarm optimization(PSO)-based algorithm to solve the problem.The proposed algorithm exploits an imbalance mutation operator and a task rescheduling approach to improve the performance of PSO.The proposed algorithm concerns the resource heterogeneity by correlating the probability that a computing node is decided to process a task with its capacity,by the imbalance mutation.The task rescheduling approach improves the acceptance ratio for a task offloading solution,by reassigning rejected tasks to computing nodes with available resources.Extensive simulated experiments are conducted.And the results show that the proposed offloading algorithm has an 8.93%–37.0%higher acceptance ratio than ten of the classical and up-to-date algorithms,and verify the effectiveness of the imbalanced mutation and the task rescheduling.
基金supported by Science and Technology Rising Star of Shaanxi Youth (No.2021KJXX-61)The Open Project Program of the State Key Lab of CAD&CG,Zhejiang University (No.A2206).
文摘Star sensors are an important means of autonomous navigation and access to space information for satellites.They have been widely deployed in the aerospace field.To satisfy the requirements for high resolution,timeliness,and confidentiality of star images,we propose an edge computing algorithm based on the star sensor cloud.Multiple sensors cooperate with each other to forma sensor cloud,which in turn extends the performance of a single sensor.The research on the data obtained by the star sensor has very important research and application values.First,a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition,which can reduce the amount of data computation.Then,a mappingmodel between content and space is constructed to achieve low-rank image representation and efficient computation.Finally,the data collected by the wireless sensor is delivered to the edge server,and a differentmethod is used to achieve privacy protection.Only a small amount of core data is stored in edge servers and local servers,and other data is transmitted to the cloud.Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage,and has strong privacy.
基金supported by the Yunnan Key Laboratory of Optoelectronic Information Technology,and grant funded by the National Natural Science Foundation of China(62062069,62062070,and 62005235)Taif University Researchers Supporting Project(TURSP-2020/126)Taif University,Taif,Saudi Arabia.Jun Liu and Kai Qian contributed equally to this paper。
文摘In recent years,statistics have indicated that the number of patients with malignant brain tumors has increased sharply.However,most surgeons still perform surgical training using the traditional autopsy and prosthesis model,which encounters many problems,such as insufficient corpse resources,low efficiency,and high cost.With the advent of the 5G era,a wide range of Industrial Internet of Things(IIOT)applications have been developed.Virtual Reality(VR)and Augmented Reality(AR)technologies that emerged with 5G are developing rapidly for intelligent medical training.To address the challenges encountered during neurosurgery training,and combining with cloud computing,in this paper,a highly immersive AR-based brain tumor neurosurgery remote collaborative virtual surgery training system is developed,in which a VR simulator is embedded.The system enables real-time remote surgery training interaction through 5G transmission.Six experts and 18 novices were invited to participate in the experiment to verify the system.Subsequently,the two simulators were evaluated using face and construction validation methods.The results obtained by training the novices 50 times were further analyzed using the Learning Curve-Cumulative Sum(LC-CUSUM)evaluation method to validate the effectiveness of the two simulators.The results of the face and content validation demonstrated that the AR simulator in the system was superior to the VR simulator in terms of vision and scene authenticity,and had a better effect on the improvement of surgical skills.Moreover,the surgical training scheme proposed in this paper is effective,and the remote collaborative training effect of the system is ideal.
基金This work is supported by the Natural Science Basic Research Plan in Shaanxi Province of China(Program No.2019JM-348).
文摘Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed.In cloud computation,data processing,storage,and transmission can be done through laptops andmobile devices.Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients.The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data.They have to be dependent on cloud service providers for assurance of the platform’s security.Data security and privacy issues reduce the progression of cloud computing and add complexity.Nowadays;most of the data that is stored on cloud servers is in the form of images and photographs,which is a very confidential form of data that requires secured transmission.In this research work,a public key cryptosystem is being implemented to store,retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman(RSA)algorithm for the encryption and decryption of data.The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment.To enhance the user data security level,a neural network is used for user authentication and recognition.Moreover;the proposed technique develops the performance of detection as a loss function of the bounding box.The Faster Region-Based Convolutional Neural Network(Faster R-CNN)gets trained on images to identify authorized users with an accuracy of 99.9%on training.
文摘In the cloud environment,the transfer of data from one cloud server to another cloud server is called migration.Data can be delivered in various ways,from one data centre to another.This research aims to increase the migration performance of the virtual machine(VM)in the cloud environment.VMs allow cloud customers to store essential data and resources.However,server usage has grown dramatically due to the virtualization of computer systems,resulting in higher data centre power consumption,storage needs,and operating expenses.Multiple VMs on one data centre manage share resources like central processing unit(CPU)cache,network bandwidth,memory,and application bandwidth.Inmulti-cloud,VMmigration addresses the performance degradation due to cloud server configuration,unbalanced traffic load,resource load management,and fault situations during data transfer.VMmigration speed is influenced by the size of the VM,the dirty rate of the running application,and the latency ofmigration iterations.As a result,evaluating VM migration performance while considering all of these factors becomes a difficult task.Themain effort of this research is to assess migration problems on performance.The simulation results in Matlab show that if the VMsize grows,themigration time of VMs and the downtime can be impacted by three orders ofmagnitude.The dirty page rate decreases,themigration time and the downtime grow,and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VMtransfer is completed.All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.