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
To resolve the problem of quantitative analysis in hybrid cloud,a quantitative analysis method,which is based on the security entropy,is proposed.Firstly,according to the information theory,the security entropy is put...To resolve the problem of quantitative analysis in hybrid cloud,a quantitative analysis method,which is based on the security entropy,is proposed.Firstly,according to the information theory,the security entropy is put forward to calculate the uncertainty of the system' s determinations on the irregular access behaviors.Secondly,based on the security entropy,security theorems of hybrid cloud are defined.Finally,typical access control models are analyzed by the method,the method's practicability is validated,and security and applicability of these models are compared.Simulation results prove that the proposed method is suitable for the security quantitative analysis of the access control model and evaluation to access control capability in hybrid cloud.展开更多
Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately u...Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.展开更多
In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, lo...In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.展开更多
As technology improves,several modernization efforts are taken in the process of teaching and learning.An effective education system should maintain global connectivity,federate security and deliver self-access to its...As technology improves,several modernization efforts are taken in the process of teaching and learning.An effective education system should maintain global connectivity,federate security and deliver self-access to its services.The cloud computing services transform the current education system to an advanced one.There exist several tools and services to make teaching and learning more interesting.In the higher education system,the data flow and basic operations are almost the same.These systems need to access cloud-based applications and services for their operational advancement and flexibility.Architecting a suitable cloud-based education system will leverage all the benefits of the cloud to its stakeholders.At the same time,educational institutions want to keep their sensitive information more secure.For that,they need to maintain their on-premises data center along with the cloud infrastructure.This paper proposes an advanced,flexible and secure hybrid cloud architecture to satisfy the growing demands of an education system.By sharing the proposed cloud infrastructure among several higher educational institutions,there is a possibility to implement a common education system among organizations.Moreover,this research demonstrates how a cloud-based education architecture can utilize the advantages of the cloud resources offered by several providers in a hybrid cloud environment.In addition,a reference architecture using Amazon Web Service(AWS)is proposed to implement a common university education system.展开更多
In the article, an experiment is aimed at clarifying the transfer efficiency of the database in the cloud infrastructure. The system was added to the control unit, which has guided the database search in the local par...In the article, an experiment is aimed at clarifying the transfer efficiency of the database in the cloud infrastructure. The system was added to the control unit, which has guided the database search in the local part or in the cloud. It is shown that the time data acquisition remains unchanged as a result of modification. Suggestions have been made about the use of the theory of dynamic systems to hybrid cloud database. The present work is aimed at attracting the attention of specialists in the field of cloud database to the apparatus control theory. The experiment presented in this article allows the use of the description of the known methods for solving important practical problems.展开更多
In hybrid cloud computing, encrypted data access control can provide a fine-grained access method for organizations to enact policies closer to organizational policies. This paper presents an improved CP-ABE (cipherte...In hybrid cloud computing, encrypted data access control can provide a fine-grained access method for organizations to enact policies closer to organizational policies. This paper presents an improved CP-ABE (ciphertext-policy attribute-based encryption) scheme to construct an encrypted data access control solution that is suitable for mobile users in hybrid cloud system. In our improvement, we split the original decryption keys into a control key, a secret key and a set of transformation keys. The private cloud managed by the organization administrator takes charge of updating the transformation keys using the control key. It helps to handle the situation of flexible access management and attribute alteration. Meanwhile, the mobile user's single secret key remains unchanged as well as the ciphertext even if the data user's attribute has been revoked. In addition, we modify the access control list through adding the attributes with corresponding control key and transformation keys so as to manage user privileges depending upon the system version. Finally, the analysis shows that our scheme is secure, flexible and efficient to be applied in mobile hybrid cloud computing.展开更多
Hybrid cloud peer to peer （P2P） system is widely used for content distribution by utilizing user＇s capabilities to relieve the cloud bandwidth pressure. However, as demands for large-size files grow rapidly, it is ...Hybrid cloud peer to peer （P2P） system is widely used for content distribution by utilizing user＇s capabilities to relieve the cloud bandwidth pressure. However, as demands for large-size files grow rapidly, it is a challenge to support high speed downloading experience simultaneously in different swarms with limited cloud bandwidth resource in such system. Therefore, it requires an optimized cloud bandwidth allocation to improve overall downloading experience of users. In this paper, we propose a system performance model which characterizes the relationship between cloud uploading bandwidth and user download speed. Based on the model, we study the cloud uploading bandwidth allocation, with the goal of optimizing user＇s quality of experience （QoE） that mainly depends on downloading rate of desired contents. Furthermore, to decrease the computation complexity, we put forward a heuristic algorithm to approximate the optimized solution. Simulation results show that our heuristic algorithm can obtain higher user＇s QoE as compared with two typical bandwidth allocation algorithms.展开更多
Robot grabbing has been successfully applied to a range of challenging environments but met the resource bottleneck. To answer this question, a hybrid cloud-based robot grabbing system is proposed, which supports cent...Robot grabbing has been successfully applied to a range of challenging environments but met the resource bottleneck. To answer this question, a hybrid cloud-based robot grabbing system is proposed, which supports centralized bin-picking management and deployment, large-scale storage, and communication technologies. The hybrid cloud combines the powerful computational capabilities through massive parallel computation and higher data storage facilities in the public cloud with data privacy in the private data center. The benchmark tasks against a public cloud based on robot grabbing method are evaluated, whose results indicate that the whole system reduces the data collection time and increases elastic resource scheduling and is adapted in the real industry.展开更多
This article addresses the challenges associatedwith three-dimensional(3D)design models in the power industry,such as substantial data volume and complex calculations.Traditional data exchange methods pose difficultie...This article addresses the challenges associatedwith three-dimensional(3D)design models in the power industry,such as substantial data volume and complex calculations.Traditional data exchange methods pose difficulties in coordinating design efforts among project participants,and current computing systems cannot meet the performance demands of 3D power design.To overcome these challenges,this study thoroughly evaluates both public and private clouds and ultimately selects a hybrid cloud infrastructure for a custom-built 3D design platform that addresses the specific needs of the power industry.Employing optimization algorithms for 3D design models,we reduce model size and implement automated synchronization of platform data through code.We also propose different levels of cloud services and identify the necessary functionalities to be fulfilled by each layer of the 3D design cloud platform.Nested virtualization technology is used to create cloud desktops with different hardware requirements.Ultimately,we successfully establish a comprehensive framework for a 3D design platform specifically tailored to power design enterprises.展开更多
As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid ...As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.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
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
Cloud computing can provide a great capacity for massive computing, storage as well as processing. The capacity comes from the cloud computing system itself, which can be likened to a virtualized resource pool that su...Cloud computing can provide a great capacity for massive computing, storage as well as processing. The capacity comes from the cloud computing system itself, which can be likened to a virtualized resource pool that supports virtualization applications as well as load migration. Based on the existing technologies, the paper proposes a resource virtualization model （RVM） utilizing a hybrid-graph structure. The hybrid-graph structure can formally represent the critical entities such as private clouds, nodes within the private clouds, and resource including its type and quantity. It also provides a clear description of the logical relationship and the dynamic expansion among them as well. Moreover, based on the RVM, a resource converging algorithm and a maintaining algorithm of the resource pool which can timely reflect the dynamic variation of the private cloud and resource are presented. The algorithms collect resources and put them into the private cloud resource pools and global resource pools, and enable a real-time maintenance for the dynamic variation of resource to ensure the continuity and reliability. Both of the algorithms use a queue structure to accomplish functions of resource converging. Finally, a simulation platform of cloud computing is designed to test the algorithms proposed in the paper. The results show the correctness and the reliability of the algorithms.展开更多
In the modern arena, the Information and Communication Technologies (ICTs) have been playing a vital role in every walks of our day to day life. In order to enhance the ICT capacity and align with the global technolog...In the modern arena, the Information and Communication Technologies (ICTs) have been playing a vital role in every walks of our day to day life. In order to enhance the ICT capacity and align with the global technology transformations, the developing countries have started introducing the computerization and automation processes at different levels of the governments. The several research studies revealed that the existing legacy of governance system and their services in current state have several issues and challenges in terms of timeliness, cost of services, delay in service delivery, time-bound availability of services (24/7/365), inefficiency services, ease of service and discomforts, poor service collaboration, absence of responsiveness, and limited security of sensitive information/documents. A significant question is still unanswered that how to bring the Citizens and Government bodies closer for alleviating the aforementioned issues and challenges of existing government system services. This research paper aims to investigate the issues and challenges in the current status of Governance and partial E-Governance systems which encompass the computerization or automation process. The research designs a cloud framework for effective delivery of citizen centric services in general and Ethiopia as a specific case study. After rigorous analysis of prior research efforts, along with primary survey and interview, it was clearly observed that cloud computing can be an alternative instrumental for significant transformation of governmental service delivery. The research paper used a mix of exploratory and constructive research design and methodology with qualitative & quantitative data analysis approach. Finally, a Cloud Based E-Governance (CBEG) Framework is designed for the delivery of Ethiopian Citizen Centric Services. The validation, evaluation and acceptance test of the framework proves that the revealed knowledge can provide a significant transformation towards the betterment of the E Governance Services Delivery Systems.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 Natural Science Foundation of China(No.60872041,61072066)Fundamental Research Funds for the Central Universities(JYI0000903001,JYI0000901034)
文摘To resolve the problem of quantitative analysis in hybrid cloud,a quantitative analysis method,which is based on the security entropy,is proposed.Firstly,according to the information theory,the security entropy is put forward to calculate the uncertainty of the system' s determinations on the irregular access behaviors.Secondly,based on the security entropy,security theorems of hybrid cloud are defined.Finally,typical access control models are analyzed by the method,the method's practicability is validated,and security and applicability of these models are compared.Simulation results prove that the proposed method is suitable for the security quantitative analysis of the access control model and evaluation to access control capability in hybrid cloud.
基金supported by National Natural Science Foundation of China(Grant No.61806138)the Central Government Guides Local Science and Technology Development Funds(Grant No.YDZJSX2021A038)+2 种基金Key RD Program of Shanxi Province(International Cooperation)under Grant No.201903D421048Outstanding Innovation Project for Graduate Students of Taiyuan University of Science and Technology(Project No.XCX211004)China University Industry-University-Research Collaborative Innovation Fund(Future Network Innovation Research and Application Project)(Grant 2021FNA04014).
文摘Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.
文摘In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.
基金supported by the Deanship of Scientific Research,Prince Sattam Bin Abdulaziz University,KSA,Project Grant No.2019/02/10478,Almotiry O.N and Sha M,www.psau.edu.sa.
文摘As technology improves,several modernization efforts are taken in the process of teaching and learning.An effective education system should maintain global connectivity,federate security and deliver self-access to its services.The cloud computing services transform the current education system to an advanced one.There exist several tools and services to make teaching and learning more interesting.In the higher education system,the data flow and basic operations are almost the same.These systems need to access cloud-based applications and services for their operational advancement and flexibility.Architecting a suitable cloud-based education system will leverage all the benefits of the cloud to its stakeholders.At the same time,educational institutions want to keep their sensitive information more secure.For that,they need to maintain their on-premises data center along with the cloud infrastructure.This paper proposes an advanced,flexible and secure hybrid cloud architecture to satisfy the growing demands of an education system.By sharing the proposed cloud infrastructure among several higher educational institutions,there is a possibility to implement a common education system among organizations.Moreover,this research demonstrates how a cloud-based education architecture can utilize the advantages of the cloud resources offered by several providers in a hybrid cloud environment.In addition,a reference architecture using Amazon Web Service(AWS)is proposed to implement a common university education system.
文摘In the article, an experiment is aimed at clarifying the transfer efficiency of the database in the cloud infrastructure. The system was added to the control unit, which has guided the database search in the local part or in the cloud. It is shown that the time data acquisition remains unchanged as a result of modification. Suggestions have been made about the use of the theory of dynamic systems to hybrid cloud database. The present work is aimed at attracting the attention of specialists in the field of cloud database to the apparatus control theory. The experiment presented in this article allows the use of the description of the known methods for solving important practical problems.
文摘In hybrid cloud computing, encrypted data access control can provide a fine-grained access method for organizations to enact policies closer to organizational policies. This paper presents an improved CP-ABE (ciphertext-policy attribute-based encryption) scheme to construct an encrypted data access control solution that is suitable for mobile users in hybrid cloud system. In our improvement, we split the original decryption keys into a control key, a secret key and a set of transformation keys. The private cloud managed by the organization administrator takes charge of updating the transformation keys using the control key. It helps to handle the situation of flexible access management and attribute alteration. Meanwhile, the mobile user's single secret key remains unchanged as well as the ciphertext even if the data user's attribute has been revoked. In addition, we modify the access control list through adding the attributes with corresponding control key and transformation keys so as to manage user privileges depending upon the system version. Finally, the analysis shows that our scheme is secure, flexible and efficient to be applied in mobile hybrid cloud computing.
基金supported by the National Natural Science Foundation of China (61271199, 61301082)the Fundamental Research Funds for the Central Universities (W14JB00500)
文摘Hybrid cloud peer to peer （P2P） system is widely used for content distribution by utilizing user＇s capabilities to relieve the cloud bandwidth pressure. However, as demands for large-size files grow rapidly, it is a challenge to support high speed downloading experience simultaneously in different swarms with limited cloud bandwidth resource in such system. Therefore, it requires an optimized cloud bandwidth allocation to improve overall downloading experience of users. In this paper, we propose a system performance model which characterizes the relationship between cloud uploading bandwidth and user download speed. Based on the model, we study the cloud uploading bandwidth allocation, with the goal of optimizing user＇s quality of experience （QoE） that mainly depends on downloading rate of desired contents. Furthermore, to decrease the computation complexity, we put forward a heuristic algorithm to approximate the optimized solution. Simulation results show that our heuristic algorithm can obtain higher user＇s QoE as compared with two typical bandwidth allocation algorithms.
文摘Robot grabbing has been successfully applied to a range of challenging environments but met the resource bottleneck. To answer this question, a hybrid cloud-based robot grabbing system is proposed, which supports centralized bin-picking management and deployment, large-scale storage, and communication technologies. The hybrid cloud combines the powerful computational capabilities through massive parallel computation and higher data storage facilities in the public cloud with data privacy in the private data center. The benchmark tasks against a public cloud based on robot grabbing method are evaluated, whose results indicate that the whole system reduces the data collection time and increases elastic resource scheduling and is adapted in the real industry.
基金supported by the Innovation Project of China Energy EngineeringGroup Co.,Ltd.-Electrical 3DDesign Software for Substations(Converter Stations).
文摘This article addresses the challenges associatedwith three-dimensional(3D)design models in the power industry,such as substantial data volume and complex calculations.Traditional data exchange methods pose difficulties in coordinating design efforts among project participants,and current computing systems cannot meet the performance demands of 3D power design.To overcome these challenges,this study thoroughly evaluates both public and private clouds and ultimately selects a hybrid cloud infrastructure for a custom-built 3D design platform that addresses the specific needs of the power industry.Employing optimization algorithms for 3D design models,we reduce model size and implement automated synchronization of platform data through code.We also propose different levels of cloud services and identify the necessary functionalities to be fulfilled by each layer of the 3D design cloud platform.Nested virtualization technology is used to create cloud desktops with different hardware requirements.Ultimately,we successfully establish a comprehensive framework for a 3D design platform specifically tailored to power design enterprises.
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education(20114307120032)the National Natural Science Foundation of China(71201167)
文摘As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.
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.
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supported by National Natural Science Foundation of China(No.61101139)Natural Science Foundation of Fujian Province(Nos.2012J01244 and 2012J01243)Hunan Provincial Project of Science and Technology(No.2013FJ3090)
文摘Cloud computing can provide a great capacity for massive computing, storage as well as processing. The capacity comes from the cloud computing system itself, which can be likened to a virtualized resource pool that supports virtualization applications as well as load migration. Based on the existing technologies, the paper proposes a resource virtualization model （RVM） utilizing a hybrid-graph structure. The hybrid-graph structure can formally represent the critical entities such as private clouds, nodes within the private clouds, and resource including its type and quantity. It also provides a clear description of the logical relationship and the dynamic expansion among them as well. Moreover, based on the RVM, a resource converging algorithm and a maintaining algorithm of the resource pool which can timely reflect the dynamic variation of the private cloud and resource are presented. The algorithms collect resources and put them into the private cloud resource pools and global resource pools, and enable a real-time maintenance for the dynamic variation of resource to ensure the continuity and reliability. Both of the algorithms use a queue structure to accomplish functions of resource converging. Finally, a simulation platform of cloud computing is designed to test the algorithms proposed in the paper. The results show the correctness and the reliability of the algorithms.
文摘In the modern arena, the Information and Communication Technologies (ICTs) have been playing a vital role in every walks of our day to day life. In order to enhance the ICT capacity and align with the global technology transformations, the developing countries have started introducing the computerization and automation processes at different levels of the governments. The several research studies revealed that the existing legacy of governance system and their services in current state have several issues and challenges in terms of timeliness, cost of services, delay in service delivery, time-bound availability of services (24/7/365), inefficiency services, ease of service and discomforts, poor service collaboration, absence of responsiveness, and limited security of sensitive information/documents. A significant question is still unanswered that how to bring the Citizens and Government bodies closer for alleviating the aforementioned issues and challenges of existing government system services. This research paper aims to investigate the issues and challenges in the current status of Governance and partial E-Governance systems which encompass the computerization or automation process. The research designs a cloud framework for effective delivery of citizen centric services in general and Ethiopia as a specific case study. After rigorous analysis of prior research efforts, along with primary survey and interview, it was clearly observed that cloud computing can be an alternative instrumental for significant transformation of governmental service delivery. The research paper used a mix of exploratory and constructive research design and methodology with qualitative & quantitative data analysis approach. Finally, a Cloud Based E-Governance (CBEG) Framework is designed for the delivery of Ethiopian Citizen Centric Services. The validation, evaluation and acceptance test of the framework proves that the revealed knowledge can provide a significant transformation towards the betterment of the E Governance Services Delivery Systems.
