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
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
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
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
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
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
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
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
Due to its decentralized,tamper-proof,and trust-free characteristics,blockchain is used in the Internet of Things(IoT)to guarantee the reliability of data.However,some technical flaws in blockchain itself prevent the ...Due to its decentralized,tamper-proof,and trust-free characteristics,blockchain is used in the Internet of Things(IoT)to guarantee the reliability of data.However,some technical flaws in blockchain itself prevent the development of these applications,such as the issue with linearly growing storage capacity of blockchain systems.On the other hand,there is a lack of storage resources for sensor devices in IoT,and numerous sensor devices will generate massive data at ultra-high speed,which makes the storage problem of the IoT enabled by blockchain more prominent.There are various solutions to reduce the storage burden by modifying the blockchain’s storage policy,but most of them do not consider the willingness of peers.In attempt to make the blockchain more compatible with the IoT,this paper proposes a storage optimization scheme that revisits the system data storage problem from amore practically oriented standpoint.Peers will only store transactional data that they are directly involved in.In addition,a transaction verification model is developed to enable peers to undertake transaction verification with the aid of cloud computing,and an incentive mechanism is premised on the storage optimization scheme to assure data integrity.The results of the simulation experiments demonstrate the proposed scheme’s advantage in terms of storage and throughput.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
Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few years.Among the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attac...Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few years.Among the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest performance.In the CC environment,Distributed Denial of Service(DDoS)attacks are widespread.The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial losses.Although various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic features.Therefore,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack traffic.Besides,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed IDS.The results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,respectively.As a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.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
Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the constr...Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms.With the rapid development and application of cloud computing and big data technology,knowledge management is faced with many problems,such as how to combine with the new generation of information technology,how to achieve integration with organizational business processes,and so on.To solve such problems,this paper proposes a reciprocal collaborative knowledge management model(RCKMmodel)based on cloud computing technology,reciprocity theory,and collaboration technology.RCKM model includes project group management and cloud computing technology,which can realize management,finance,communication,and quality assurance of multiple projects and solve the problem of business integration with knowledge management.This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data.The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model.The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business,and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.展开更多
With the development of cloud computing technology,more and more data owners upload their local data to the public cloud server for storage and calculation.While this can save customers’operating costs,it also poses ...With the development of cloud computing technology,more and more data owners upload their local data to the public cloud server for storage and calculation.While this can save customers’operating costs,it also poses privacy and security challenges.Such challenges can be solved using secure multi-party computation(SMPC),but this still exposes more security issues.In cloud computing using SMPC,clients need to process their data and submit the processed data to the cloud server,which then performs the calculation and returns the results to each client.Each client and server must be honest.If there is cooperation or dishonest behavior between clients,some clients may profit from it or even disclose the private data of other clients.This paper proposes the SMPC based on a Partially-Homomorphic Encryption(PHE)scheme in which an addition homomorphic encryption algorithm with a lower computational cost is used to ensure data comparability and Zero-Knowledge Proof(ZKP)is used to limit the client’s malicious behavior.In addition,the introduction of Oblivious Transfer(OT)technology also ensures that the semi-honest cloud server knows nothing about private data,so that the cloud server of this scheme can calculate the correct data in the case of malicious participant models and safely return the calculation results to each client.Finally,the security analysis shows that the scheme not only ensures the privacy of participants,but also ensures the fairness of the comparison protocol data.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
Flash Crowd attacks are a form of Distributed Denial of Service(DDoS)attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing(CC).Botnets are often...Flash Crowd attacks are a form of Distributed Denial of Service(DDoS)attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing(CC).Botnets are often used by attackers to perform a wide range of DDoS attacks.With advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to detect.When it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue.This is mainly because it can imitate typical user behavior,leading to a substantial influx of requests that can overwhelm the server by consuming either its network bandwidth or resources.Therefore,identifying these types of attacks on web servers has become crucial,particularly in the CC.In this article,an efficient intrusion detection method is proposed based on White Shark Optimizer and ensemble classifier(Convolutional Neural Network(CNN)and LighGBM).Experiments were conducted using a CICIDS 2017 dataset to evaluate the performance of the proposed method in real-life situations.The proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 measure.Flash crowd attacks are challenging to detect,but the proposed IDS has proven its effectiveness in identifying such attacks in CC and holds potential for future improvement.展开更多
In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several ...In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.展开更多
Cloud computing(CC)is developing as a powerful and flexible computational structure for providing ubiquitous service to users.It receives interrelated software and hardware resources in an integrated manner distinct f...Cloud computing(CC)is developing as a powerful and flexible computational structure for providing ubiquitous service to users.It receives interrelated software and hardware resources in an integrated manner distinct from the classical computational environment.The variation of software and hardware resources were combined and composed as a resource pool.The software no more resided in the single hardware environment,it can be executed on the schedule of resource pools to optimize resource consumption.Optimizing energy consumption in CC environments is the question that allows utilizing several energy conservation approaches for effective resource allocation.This study introduces a Battle Royale Optimization-based Resource Scheduling Scheme for Cloud Computing Environment(BRORSS-CCE)technique.The presented BRORSS-CCE technique majorly schedules the available resources for maximum utilization and effectual makespan.In the BRORSS-CCE technique,the BRO is a population-based algorithm where all the individuals are denoted by a soldier/player who likes to go towards the optimal place and ultimate survival.The BRORSS-CCE technique can be employed to balance the load,distribute resources based on demand and assure services to all requests.The experimental validation of the BRORSS-CCE technique is tested under distinct aspects.The experimental outcomes indicated the enhancements of the BRORSS-CCE technique over other models.展开更多
The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the diff...The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such problems.In this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task sequence.Firstly,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic strategies.Secondly,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities.Besides,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process.Compared with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.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 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We would also thank the support from the National Natural Science Foundation of China(Nos.62172182,62202118,61962009)the Top Technology Talent Project from Guizhou Education Department(Qian jiao ji[2022]073)The Opening Foundation of Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province(Grant No.ZNKZN2021-07).
文摘Due to its decentralized,tamper-proof,and trust-free characteristics,blockchain is used in the Internet of Things(IoT)to guarantee the reliability of data.However,some technical flaws in blockchain itself prevent the development of these applications,such as the issue with linearly growing storage capacity of blockchain systems.On the other hand,there is a lack of storage resources for sensor devices in IoT,and numerous sensor devices will generate massive data at ultra-high speed,which makes the storage problem of the IoT enabled by blockchain more prominent.There are various solutions to reduce the storage burden by modifying the blockchain’s storage policy,but most of them do not consider the willingness of peers.In attempt to make the blockchain more compatible with the IoT,this paper proposes a storage optimization scheme that revisits the system data storage problem from amore practically oriented standpoint.Peers will only store transactional data that they are directly involved in.In addition,a transaction verification model is developed to enable peers to undertake transaction verification with the aid of cloud computing,and an incentive mechanism is premised on the storage optimization scheme to assure data integrity.The results of the simulation experiments demonstrate the proposed scheme’s advantage in terms of storage and throughput.
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.
基金The authors gratefully acknowledge the approval and the support of this research study by the Grant No.SCIA-2022-11-1545the Deanship of Scientific Research at Northern Border University,Arar,K.S.A.
文摘Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few years.Among the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest performance.In the CC environment,Distributed Denial of Service(DDoS)attacks are widespread.The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial losses.Although various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic features.Therefore,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack traffic.Besides,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed IDS.The results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,respectively.As a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.
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.
基金supported by the research project of the Jiangsu water conservancy science and technology project (Contract Number:2021067).
文摘Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms.With the rapid development and application of cloud computing and big data technology,knowledge management is faced with many problems,such as how to combine with the new generation of information technology,how to achieve integration with organizational business processes,and so on.To solve such problems,this paper proposes a reciprocal collaborative knowledge management model(RCKMmodel)based on cloud computing technology,reciprocity theory,and collaboration technology.RCKM model includes project group management and cloud computing technology,which can realize management,finance,communication,and quality assurance of multiple projects and solve the problem of business integration with knowledge management.This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data.The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model.The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business,and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.
基金supported by the National Natural Science Foundation of China under Grant No.(62202118.61962009)And in part by Natural Science Foundation of Shandong Province(ZR2021MF086)+1 种基金And in part by Top Technology Talent Project from Guizhou Education Department(Qian jiao ji[2022]073)And in part by Foundation of Guangxi Key Laboratory of Cryptography and Information Security(GCIS202118).
文摘With the development of cloud computing technology,more and more data owners upload their local data to the public cloud server for storage and calculation.While this can save customers’operating costs,it also poses privacy and security challenges.Such challenges can be solved using secure multi-party computation(SMPC),but this still exposes more security issues.In cloud computing using SMPC,clients need to process their data and submit the processed data to the cloud server,which then performs the calculation and returns the results to each client.Each client and server must be honest.If there is cooperation or dishonest behavior between clients,some clients may profit from it or even disclose the private data of other clients.This paper proposes the SMPC based on a Partially-Homomorphic Encryption(PHE)scheme in which an addition homomorphic encryption algorithm with a lower computational cost is used to ensure data comparability and Zero-Knowledge Proof(ZKP)is used to limit the client’s malicious behavior.In addition,the introduction of Oblivious Transfer(OT)technology also ensures that the semi-honest cloud server knows nothing about private data,so that the cloud server of this scheme can calculate the correct data in the case of malicious participant models and safely return the calculation results to each client.Finally,the security analysis shows that the scheme not only ensures the privacy of participants,but also ensures the fairness of the comparison protocol data.
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The authors gratefully acknowledge the approval and the support of this research study by grant no.SCIA-2022-11-1551 from the Deanship of Scientific Research at Northern Border University,Arar,K.S.A.
文摘Flash Crowd attacks are a form of Distributed Denial of Service(DDoS)attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing(CC).Botnets are often used by attackers to perform a wide range of DDoS attacks.With advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to detect.When it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue.This is mainly because it can imitate typical user behavior,leading to a substantial influx of requests that can overwhelm the server by consuming either its network bandwidth or resources.Therefore,identifying these types of attacks on web servers has become crucial,particularly in the CC.In this article,an efficient intrusion detection method is proposed based on White Shark Optimizer and ensemble classifier(Convolutional Neural Network(CNN)and LighGBM).Experiments were conducted using a CICIDS 2017 dataset to evaluate the performance of the proposed method in real-life situations.The proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 measure.Flash crowd attacks are challenging to detect,but the proposed IDS has proven its effectiveness in identifying such attacks in CC and holds potential for future improvement.
文摘In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works.
文摘Cloud computing(CC)is developing as a powerful and flexible computational structure for providing ubiquitous service to users.It receives interrelated software and hardware resources in an integrated manner distinct from the classical computational environment.The variation of software and hardware resources were combined and composed as a resource pool.The software no more resided in the single hardware environment,it can be executed on the schedule of resource pools to optimize resource consumption.Optimizing energy consumption in CC environments is the question that allows utilizing several energy conservation approaches for effective resource allocation.This study introduces a Battle Royale Optimization-based Resource Scheduling Scheme for Cloud Computing Environment(BRORSS-CCE)technique.The presented BRORSS-CCE technique majorly schedules the available resources for maximum utilization and effectual makespan.In the BRORSS-CCE technique,the BRO is a population-based algorithm where all the individuals are denoted by a soldier/player who likes to go towards the optimal place and ultimate survival.The BRORSS-CCE technique can be employed to balance the load,distribute resources based on demand and assure services to all requests.The experimental validation of the BRORSS-CCE technique is tested under distinct aspects.The experimental outcomes indicated the enhancements of the BRORSS-CCE technique over other models.
基金supported in part by the National Key R&D Program of China under Grant 2017YFB1302400the Jinan“20 New Colleges and Universities”Funded Scientific Research Leader Studio under Grant 2021GXRC079+2 种基金the Major Agricultural Applied Technological Innovation Projects of Shandong Province underGrant SD2019NJ014the Shandong Natural Science Foundation under Grant ZR2019MF064the Beijing Advanced Innovation Center for Intelligent Robots and Systems under Grant 2019IRS19.
文摘The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process.However,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such problems.In this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task sequence.Firstly,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic strategies.Secondly,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities.Besides,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process.Compared with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.
