The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n...The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.展开更多
Because of its on-demand servicing and scalability features in cloud computing,security and confidentiality have converted to key concerns.Maintaining transaction information on thirdparty servers carries significant ...Because of its on-demand servicing and scalability features in cloud computing,security and confidentiality have converted to key concerns.Maintaining transaction information on thirdparty servers carries significant dangers so that malicious individuals trying for illegal access to information data security architecture.This research proposes a security-aware information transfer in the cloud-based on the blowfish algorithm(BFA)to address the issue.The user is verified initially with the identification and separate the imported data using pattern matching technique.Further,BFA is utilised to encrypt and save the data in cloud.This can safeguard the data and streamline the proof so that client cannot retrieve the information without identification which makes the environment secure.The suggested approach’s performance is evaluated using several metrics,including encryption time,decryption time,memory utilisation,and runtime.Compared to the existing methodology,the investigational findings clearly show that the method takes the least time to data encryption.展开更多
文摘The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.
文摘Because of its on-demand servicing and scalability features in cloud computing,security and confidentiality have converted to key concerns.Maintaining transaction information on thirdparty servers carries significant dangers so that malicious individuals trying for illegal access to information data security architecture.This research proposes a security-aware information transfer in the cloud-based on the blowfish algorithm(BFA)to address the issue.The user is verified initially with the identification and separate the imported data using pattern matching technique.Further,BFA is utilised to encrypt and save the data in cloud.This can safeguard the data and streamline the proof so that client cannot retrieve the information without identification which makes the environment secure.The suggested approach’s performance is evaluated using several metrics,including encryption time,decryption time,memory utilisation,and runtime.Compared to the existing methodology,the investigational findings clearly show that the method takes the least time to data encryption.
