In the age of online workload explosion,cloud users are increasing exponentialy.Therefore,large scale data centers are required in cloud environment that leads to high energy consumption.Hence,optimal resource utiliza...In the age of online workload explosion,cloud users are increasing exponentialy.Therefore,large scale data centers are required in cloud environment that leads to high energy consumption.Hence,optimal resource utilization is essential to improve energy efficiency of cloud data center.Although,most of the existing literature focuses on virtual machine(VM)consolidation for increasing energy efficiency at the cost of service level agreement degradation.In order to improve the existing approaches,load aware three-gear THReshold(LATHR)as well as modified best fit decreasing(MBFD)algorithm is proposed for minimizing total energy consumption while improving the quality of service in terms of SLA.It offers promising results under dynamic workload and variable number of VMs(1-290)allocated on individual host.The outcomes of the proposed work are measured in terms of SLA,energy consumption,instruction energy ratio(IER)and the number of migrations against the varied numbers of VMs.From experimental results it has been concluded that the proposed technique reduced the SLA violations(55%,26%and 39%)and energy consumption(17%,12%and 6%)as compared to median absolute deviation(MAD),inter quartile range(IQR)and double threshold(THR)overload detection policies,respectively.展开更多
Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/rem...Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/remote sensing(GIS/RS) researchers rent more public clouds or establish more private clouds.However,a large proportion of these clouds are found to be underutilized,since users do not deal with big data every day.The low usage of cloud resources violates the original intention of cloud computing,which is to save resources by improving usage.In this work,a low-cost cloud computing solution was proposed for geo-information processing,especially for temporary processing tasks.The proposed solution adopted a hosted architecture and can be realized based on ordinary computers in a common GIS/RS laboratory.The usefulness and effectiveness of the proposed solution was demonstrated by using big data simplification as a case study.Compared to commercial public clouds and dedicated private clouds,the proposed solution is more low-cost and resource-saving,and is more suitable for GIS/RS applications.展开更多
Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time o...Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.展开更多
In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the...In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.展开更多
In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the r...In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.展开更多
Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A...Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
Private clouds and public clouds are turning mutually into the open integrated cloud computing environment,which can aggregate and utilize WAN and LAN networks computing,storage,information and other hardware and soft...Private clouds and public clouds are turning mutually into the open integrated cloud computing environment,which can aggregate and utilize WAN and LAN networks computing,storage,information and other hardware and software resources sufficiently,but also bring a series of security,reliability and credibility problems.To solve these problems,a novel secure-agent-based trustworthy virtual private cloud model named SATVPC was proposed for the integrated and open cloud computing environment.Through the introduction of secure-agent technology,SATVPC provides an independent,safe and trustworthy computing virtual private platform for multi-tenant systems.In order to meet the needs of the credibility of SATVPC and mandate the trust relationship between each task execution agent and task executor node suitable for their security policies,a new dynamic composite credibility evaluation mechanism was presented,including the credit index computing algorithm and the credibility differentiation strategy.The experimental system shows that SATVPC and the credibility evaluation mechanism can ensure the security of open computing environments with feasibility.Experimental results and performance analysis also show that the credit indexes computing algorithm can evaluate the credibilities of task execution agents and task executor nodes quantitatively,correctly and operationally.展开更多
How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data cente...How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.展开更多
Due to the increasing number of cloud applications,the amount of data in the cloud shows signs of growing faster than ever before.The nature of cloud computing requires cloud data processing systems that can handle hu...Due to the increasing number of cloud applications,the amount of data in the cloud shows signs of growing faster than ever before.The nature of cloud computing requires cloud data processing systems that can handle huge volumes of data and have high performance.However,most cloud storage systems currently adopt a hash-like approach to retrieving data that only supports simple keyword-based enquiries,but lacks various forms of information search.Therefore,a scalable and efficient indexing scheme is clearly required.In this paper,we present a skip list-based cloud index,called SLC-index,which is a novel,scalable skip list-based indexing for cloud data processing.The SLC-index offers a two-layered architecture for extending indexing scope and facilitating better throughput.Dynamic load-balancing for the SLC-index is achieved by online migration of index nodes between servers.Furthermore,it is a flexible system due to its dynamic addition and removal of servers.The SLC-index is efficient for both point and range queries.Experimental results show the efficiency of the SLC-index and its usefulness as an alternative approach for cloud-suitable data structures.展开更多
Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service(QoS).This paper analyzes the features of...Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service(QoS).This paper analyzes the features of cloud server load and the advantages and disadvantages of typical server load prediction algorithms, integrates the cloud model(CM) and the Markov chain(MC) together to realize a new CM-MC algorithm, and then proposes a new server load prediction algorithm based on CM-MC for cloud systems. The algorithm utilizes the historical data sample training method of the cloud model, and utilizes the Markov prediction theory to obtain the membership degree vector, based on which the weighted sum of the predicted values is used for the cloud model. The experiments show that the proposed prediction algorithm has higher prediction accuracy than other typical server load prediction algorithms, especially if the data has significant volatility. The proposed server load prediction algorithm based on CM-MC is suitable for cloud systems, and can help to reduce the energy consumption of cloud data centers.展开更多
The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.Cloud computing is concerned as a powerful solution...The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.Cloud computing is concerned as a powerful solution to handle complex large-scale control missions by using sufficient computing resources.However,the computing ability enables more complex devices and more data to be involved and most of the data have not been fully utilized.Meanwhile,it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms.Therefore,motivated by the above reasons,we propose a data-driven predictive cloud control system.To achieve the proposed system,a practical data-driven predictive cloud control testbed is established and together a cloud-edge communication scheme is developed.Finally,the simulations and experiments demonstrate the effectiveness of the proposed system.展开更多
In order to improve resource utilization, it is necessary to integrate storage and data, and the emergence of cloud computing makes it possible. This paper analyzed the study of virtualization and cloud computing, pro...In order to improve resource utilization, it is necessary to integrate storage and data, and the emergence of cloud computing makes it possible. This paper analyzed the study of virtualization and cloud computing, proposed a new scheme based on virtualization, and established a shared storage platform, which made a good complement and perfected the centralized storage platform.展开更多
文摘In the age of online workload explosion,cloud users are increasing exponentialy.Therefore,large scale data centers are required in cloud environment that leads to high energy consumption.Hence,optimal resource utilization is essential to improve energy efficiency of cloud data center.Although,most of the existing literature focuses on virtual machine(VM)consolidation for increasing energy efficiency at the cost of service level agreement degradation.In order to improve the existing approaches,load aware three-gear THReshold(LATHR)as well as modified best fit decreasing(MBFD)algorithm is proposed for minimizing total energy consumption while improving the quality of service in terms of SLA.It offers promising results under dynamic workload and variable number of VMs(1-290)allocated on individual host.The outcomes of the proposed work are measured in terms of SLA,energy consumption,instruction energy ratio(IER)and the number of migrations against the varied numbers of VMs.From experimental results it has been concluded that the proposed technique reduced the SLA violations(55%,26%and 39%)and energy consumption(17%,12%and 6%)as compared to median absolute deviation(MAD),inter quartile range(IQR)and double threshold(THR)overload detection policies,respectively.
基金Project(41401434)supported by the National Natural Science Foundation of China
文摘Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/remote sensing(GIS/RS) researchers rent more public clouds or establish more private clouds.However,a large proportion of these clouds are found to be underutilized,since users do not deal with big data every day.The low usage of cloud resources violates the original intention of cloud computing,which is to save resources by improving usage.In this work,a low-cost cloud computing solution was proposed for geo-information processing,especially for temporary processing tasks.The proposed solution adopted a hosted architecture and can be realized based on ordinary computers in a common GIS/RS laboratory.The usefulness and effectiveness of the proposed solution was demonstrated by using big data simplification as a case study.Compared to commercial public clouds and dedicated private clouds,the proposed solution is more low-cost and resource-saving,and is more suitable for GIS/RS applications.
基金supported by the National Natural Science Foundation of China(6120235461272422)the Scientific and Technological Support Project(Industry)of Jiangsu Province(BE2011189)
文摘Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.
基金Project(61272148) supported by the National Natural Science Foundation of ChinaProject(20120162110061) supported by the Doctoral Programs of Ministry of Education of China+1 种基金Project(CX2014B066) supported by the Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject(2014zzts044) supported by the Fundamental Research Funds for the Central Universities,China
文摘In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.
基金supported by the National Natural Science Foundation of China(6147219261202004)+1 种基金the Special Fund for Fast Sharing of Science Paper in Net Era by CSTD(2013116)the Natural Science Fund of Higher Education of Jiangsu Province(14KJB520014)
文摘In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.
基金supported by the National Natural Science Foundation of China(61173017)the National High Technology Research and Development Program(863 Program)(2014AA01A701)
文摘Mobile cloud computing（MCC） combines mobile Internet and cloud computing to improve the performance of mobile applications. However, MCC faces the problem of energy efficiency because of randomly varying channels. A scheduling algorithm is proposed by introducing the Lyapunov optimization, which can dynamically choose users to transmit data based on queue backlog and channel statistics. The Lyapunov analysis shows that the proposed scheduling algorithm can make a tradeoff between queue backlog and energy consumption in the channel-aware mobile cloud computing system. The simulation results verify the effectiveness of the proposed algorithm.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
基金Projects(61202004,61272084)supported by the National Natural Science Foundation of ChinaProjects(2011M500095,2012T50514)supported by the China Postdoctoral Science Foundation+2 种基金Projects(BK2011754,BK2009426)supported by the Natural Science Foundation of Jiangsu Province,ChinaProject(12KJB520007)supported by the Natural Science Fund of Higher Education of Jiangsu Province,ChinaProject(yx002001)supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,China
文摘Private clouds and public clouds are turning mutually into the open integrated cloud computing environment,which can aggregate and utilize WAN and LAN networks computing,storage,information and other hardware and software resources sufficiently,but also bring a series of security,reliability and credibility problems.To solve these problems,a novel secure-agent-based trustworthy virtual private cloud model named SATVPC was proposed for the integrated and open cloud computing environment.Through the introduction of secure-agent technology,SATVPC provides an independent,safe and trustworthy computing virtual private platform for multi-tenant systems.In order to meet the needs of the credibility of SATVPC and mandate the trust relationship between each task execution agent and task executor node suitable for their security policies,a new dynamic composite credibility evaluation mechanism was presented,including the credit index computing algorithm and the credibility differentiation strategy.The experimental system shows that SATVPC and the credibility evaluation mechanism can ensure the security of open computing environments with feasibility.Experimental results and performance analysis also show that the credit indexes computing algorithm can evaluate the credibilities of task execution agents and task executor nodes quantitatively,correctly and operationally.
基金supported by the National Natural Science Foundation of China(6120200461272084)+9 种基金the National Key Basic Research Program of China(973 Program)(2011CB302903)the Specialized Research Fund for the Doctoral Program of Higher Education(2009322312000120113223110003)the China Postdoctoral Science Foundation Funded Project(2011M5000952012T50514)the Natural Science Foundation of Jiangsu Province(BK2011754BK2009426)the Jiangsu Postdoctoral Science Foundation Funded Project(1102103C)the Natural Science Fund of Higher Education of Jiangsu Province(12KJB520007)the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(yx002001)
文摘How to effectively reduce the energy consumption of large-scale data centers is a key issue in cloud computing. This paper presents a novel low-power task scheduling algorithm （L3SA） for large-scale cloud data centers. The winner tree is introduced to make the data nodes as the leaf nodes of the tree and the final winner on the purpose of reducing energy consumption is selected. The complexity of large-scale cloud data centers is fully consider, and the task comparson coefficient is defined to make task scheduling strategy more reasonable. Experiments and performance analysis show that the proposed algorithm can effectively improve the node utilization, and reduce the overall power consumption of the cloud data center.
基金Projects(61363021,61540061,61663047)supported by the National Natural Science Foundation of ChinaProject(2017SE206)supported by the Open Foundation of Key Laboratory in Software Engineering of Yunnan Province,China
文摘Due to the increasing number of cloud applications,the amount of data in the cloud shows signs of growing faster than ever before.The nature of cloud computing requires cloud data processing systems that can handle huge volumes of data and have high performance.However,most cloud storage systems currently adopt a hash-like approach to retrieving data that only supports simple keyword-based enquiries,but lacks various forms of information search.Therefore,a scalable and efficient indexing scheme is clearly required.In this paper,we present a skip list-based cloud index,called SLC-index,which is a novel,scalable skip list-based indexing for cloud data processing.The SLC-index offers a two-layered architecture for extending indexing scope and facilitating better throughput.Dynamic load-balancing for the SLC-index is achieved by online migration of index nodes between servers.Furthermore,it is a flexible system due to its dynamic addition and removal of servers.The SLC-index is efficient for both point and range queries.Experimental results show the efficiency of the SLC-index and its usefulness as an alternative approach for cloud-suitable data structures.
基金supported by the National Natural Science Foundation of China(61472192 61772286)+3 种基金the National Key Research and Development Program of China(2018YFB1003700)the Scientific and Technological Support Project(Society)of Jiangsu Province(BE2016776)the "333" Project of Jiangsu Province(BRA2017228 BRA2017401)
文摘Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service(QoS).This paper analyzes the features of cloud server load and the advantages and disadvantages of typical server load prediction algorithms, integrates the cloud model(CM) and the Markov chain(MC) together to realize a new CM-MC algorithm, and then proposes a new server load prediction algorithm based on CM-MC for cloud systems. The algorithm utilizes the historical data sample training method of the cloud model, and utilizes the Markov prediction theory to obtain the membership degree vector, based on which the weighted sum of the predicted values is used for the cloud model. The experiments show that the proposed prediction algorithm has higher prediction accuracy than other typical server load prediction algorithms, especially if the data has significant volatility. The proposed server load prediction algorithm based on CM-MC is suitable for cloud systems, and can help to reduce the energy consumption of cloud data centers.
基金supported by the National Natural Science Foundation of China(61836001,62122014,62173036,62102022)。
文摘The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.Cloud computing is concerned as a powerful solution to handle complex large-scale control missions by using sufficient computing resources.However,the computing ability enables more complex devices and more data to be involved and most of the data have not been fully utilized.Meanwhile,it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms.Therefore,motivated by the above reasons,we propose a data-driven predictive cloud control system.To achieve the proposed system,a practical data-driven predictive cloud control testbed is established and together a cloud-edge communication scheme is developed.Finally,the simulations and experiments demonstrate the effectiveness of the proposed system.
基金Supported by the National Natural Science Foundation of Heilongjiang Province (G201206)
文摘In order to improve resource utilization, it is necessary to integrate storage and data, and the emergence of cloud computing makes it possible. This paper analyzed the study of virtualization and cloud computing, proposed a new scheme based on virtualization, and established a shared storage platform, which made a good complement and perfected the centralized storage platform.
