Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositori...Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositories results in increased overload due to the adoption of cloud services.Moreover,the migration of applications on the cloud with optimized resource allocation is a herculean task even though it is employed for minimizing the dilemma of allocating resources.In this paper,a Fire Hawk Optimization enabled Deep Learning Scheme(FHOEDLS)is proposed for minimizing the overload and optimizing the resource allocation on the hybrid cloud container architecture for migrating interoperability based applications This FHOEDLS achieves the load prediction through the utilization of deep CNN-GRU-AM model for attaining resource allocation and better migration of applications.It specifically adopted the Fire Hawk Optimization Algorithm(FHOA)for optimizing the parameters that influence the factors that aid in better interoperable application migration with improved resource allocation and minimized overhead.It considered the factors of resource capacity,transmission cost,demand,and predicted load into account during the formulation of the objective function utilized for resource allocation and application migration.The cloud simulation of this FHOEDLS is achieved using a container,Virtual Machine(VM),and Physical Machine(PM).The results of this proposed FHOEDLS confirmed a better resource capability of 0.418 and a minimized load of 0.0061.展开更多
A cloud computing based optimal driving method is proposed and its feasibility is validated through a real-world scenario simulation.Based on principles of vehicle dynamics,the driving optimization problem has been fo...A cloud computing based optimal driving method is proposed and its feasibility is validated through a real-world scenario simulation.Based on principles of vehicle dynamics,the driving optimization problem has been formulated into an optimal control problem constrained by traffic rules,directed at achieving lower equivalent fuel consumption and shorter travel time.In order to conveniently specify the constraints and facilitate the application of the dynamic programming(DP)algorithm,the driving optimization problem is transformed into spatial domain and discretized properly.Considering the heavy computational costs of the DP algorithm,a cloud computing based platform structure is proposed to solve the optimal driving problem in real-time.A case study is simulated based on a real-world traffic scenario in Matlab.Simulation results demonstrate that the cloud computing framework is promising toward realizing the real-time energy management for hybrid electric vehicles.展开更多
文摘Virtualization is an indispensable part of the cloud for the objective of deploying different virtual servers over the same physical layer.However,the increase in the number of applications executing on the repositories results in increased overload due to the adoption of cloud services.Moreover,the migration of applications on the cloud with optimized resource allocation is a herculean task even though it is employed for minimizing the dilemma of allocating resources.In this paper,a Fire Hawk Optimization enabled Deep Learning Scheme(FHOEDLS)is proposed for minimizing the overload and optimizing the resource allocation on the hybrid cloud container architecture for migrating interoperability based applications This FHOEDLS achieves the load prediction through the utilization of deep CNN-GRU-AM model for attaining resource allocation and better migration of applications.It specifically adopted the Fire Hawk Optimization Algorithm(FHOA)for optimizing the parameters that influence the factors that aid in better interoperable application migration with improved resource allocation and minimized overhead.It considered the factors of resource capacity,transmission cost,demand,and predicted load into account during the formulation of the objective function utilized for resource allocation and application migration.The cloud simulation of this FHOEDLS is achieved using a container,Virtual Machine(VM),and Physical Machine(PM).The results of this proposed FHOEDLS confirmed a better resource capability of 0.418 and a minimized load of 0.0061.
基金Supported by the National Nature Science Foundation of China(5177503951861135301)
文摘A cloud computing based optimal driving method is proposed and its feasibility is validated through a real-world scenario simulation.Based on principles of vehicle dynamics,the driving optimization problem has been formulated into an optimal control problem constrained by traffic rules,directed at achieving lower equivalent fuel consumption and shorter travel time.In order to conveniently specify the constraints and facilitate the application of the dynamic programming(DP)algorithm,the driving optimization problem is transformed into spatial domain and discretized properly.Considering the heavy computational costs of the DP algorithm,a cloud computing based platform structure is proposed to solve the optimal driving problem in real-time.A case study is simulated based on a real-world traffic scenario in Matlab.Simulation results demonstrate that the cloud computing framework is promising toward realizing the real-time energy management for hybrid electric vehicles.
