Cloud computing is a new computing model. The resource monitoring tools are immature compared to traditional distributed computing and grid computing. In order to better monitor the virtual resource in cloud computing...Cloud computing is a new computing model. The resource monitoring tools are immature compared to traditional distributed computing and grid computing. In order to better monitor the virtual resource in cloud computing, a periodically and event-driven push （PEP） monitoring model is proposed. Taking advantage of the push and event-driven mechanism, the model can provide comparatively adequate information about usage and status of the resources. It can simplify the communication between Master and Work Nodes without missing the important issues happened during the push interval. Besides, we develop ＂mon＂ to make up for the deficiency of Libvirt in monitoring of virtual CPU and memory.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
Online Education (OE) system is an effective and efficient way to perform the education in all sectors of government and non-government educational organization. Low performance and minimum speed are major overhead in...Online Education (OE) system is an effective and efficient way to perform the education in all sectors of government and non-government educational organization. Low performance and minimum speed are major overhead in the current ongoing OE system due to the increase of users and some system issues. Base on the previous study and recent practical issues, a model is proposed to Enhancing the Performance of Online Education System (EPOES) to examine the bare metal virtualization, isolation and virtual machine templates. Bare metal virtualization has led the native execution, isolation isolated the running application and Virtual Machine Template has help to increase efficiency, avoiding the repetitive installation and operate the server in less time. The proposed model boosts the performance of the current OE system, and examines the benefits of the adaptation of cloud computing and virtualization which can be used to overcome the existing challenges and barriers of the current OE System.展开更多
The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems.This research presents an innovative hybrid framework t...The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems.This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network(QT-DNN)with Binary Bird Swarm Optimization(BBSO)to enhance resource allocation while preserving Quality of Service(QoS).In contrast to conventional approaches,the QT-DNN accurately predicts task-resource mappings using tensor-based task representation,significantly minimizing computing overhead.The BBSO allocates resources dynamically,optimizing energy efficiency and task distribution.Experimental results from extensive simulations indicate the efficacy of the suggested strategy;the proposed approach demonstrates the highest level of accuracy,reaching 98.1%.This surpasses the GA-SVM model,which achieves an accuracy of 96.3%,and the ART model,which achieves an accuracy of 95.4%.The proposed method performs better in terms of response time with 1.598 as compared to existing methods Energy-Focused Dynamic Task Scheduling(EFDTS)and Federated Energy-efficient Scheduler for Task Allocation in Large-scale environments(FESTAL)with 2.31 and 2.04,moreover,the proposed method performs better in terms of makespan with 12 as compared to Round Robin(RR)and Recurrent Attention-based Summarization Algorithm(RASA)with 20 and 14.The hybrid method establishes a new standard for sustainable and efficient administration of cloud computing resources by explicitly addressing scalability and real-time performance.展开更多
In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many ...In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.展开更多
Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves ...Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.展开更多
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
With the rapid development of social,science and technology ,we are always looking for the advanced and rapid manufacturing method and the management pattern.thus a new enterprise cooperation pattern-Virtual Enterpris...With the rapid development of social,science and technology ,we are always looking for the advanced and rapid manufacturing method and the management pattern.thus a new enterprise cooperation pattern-Virtual Enterprise arises at the historic moment. The cooperation is a process which advantages the temporary enterprise resources each other. Therefore, the virtual enterprise must encounter the problem that how to realize the virtual enterprises’ information resources sharing and improve the efficiency of enterprise cooperation. This paper uses the cloud computing’s advantage to solve the problem of virtual enterprise information resources sharing. Then enterprise is able to share the information of different regions,different computing environment and improve the efficiency of virtual enterprise cooperation.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
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
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
The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource pr...The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.展开更多
The data and applications in cloud computing reside in cyberspace, that allowing to users access data through any connection device, when you need to transfer information over the cloud, you will lose control of it. T...The data and applications in cloud computing reside in cyberspace, that allowing to users access data through any connection device, when you need to transfer information over the cloud, you will lose control of it. There are multi types of security challenge must be understood and countermeasures. One of the major security challenges is resources of the cloud computing infrastructures are provided as services over the Internet, and entire data in the cloud computing are reside over network resources, that enables the data to be access through VMs. In this work, we describe security techniques for securing a VCCI, VMMs such as Encryption and Key Management (EKM), Access Control Mechanisms (ACMs), Virtual Trusted Platform Module (vTPM), Virtual Firewall (VF), and Trusted Virtual Domains (TVDs). In this paper we focus on security of virtual resources in Virtualized Cloud Computing Infrastructure (VCCI), Virtual Machine Monitor (VMM) by describing types of attacks on VCCI, and vulnerabilities of VMMs and we describe the techniques for securing a VCCI.展开更多
This paper sums up four security factors after analyzing co-residency threats caused by the special multitenant environment in the cloud.To secure the factors,a multiway dynamic trust chain transfer model was proposed...This paper sums up four security factors after analyzing co-residency threats caused by the special multitenant environment in the cloud.To secure the factors,a multiway dynamic trust chain transfer model was proposed on the basis of a measurement interactive virtual machine and current behavior to protect the integrity of the system.A trust chain construction module is designed in a virtual machine monitor.Through dynamic monitoring,it achieves the purpose of transferring integrity between virtual machine.A cloud system with a trust authentication function is implemented on the basis of the model,and its practicability is shown.展开更多
In the field of cloud computing, topics such as computing resource virtualization, differences between grid and cloud computing, relationship between high-performance computers and cloud computing centers, and cloud s...In the field of cloud computing, topics such as computing resource virtualization, differences between grid and cloud computing, relationship between high-performance computers and cloud computing centers, and cloud security and standards have attracted much research interest. This paper analyzes these topics and highlights that resource virtualization allows information services to be scalable, intensive, and specialized; grid computing involves using many computers for large-scale computing tasks, while cloud computing uses one platform for multiple services; high-performance computers may not be suitable for a cloud computing; security in cloud computing focuses on trust management between service suppliers and users; and based on the existing standards, standardization of cloud computing should focus on interoperability between services.展开更多
To overcome vendor lock-in obstacles in public cloud computing, the capability to define transferable cloud-based services is crucial but has not yet been solved satisfactorily. This is especially true for small and m...To overcome vendor lock-in obstacles in public cloud computing, the capability to define transferable cloud-based services is crucial but has not yet been solved satisfactorily. This is especially true for small and medium sized enterprises being typically not able to operate a vast staff of cloud service and IT experts. Actual state of the art cloud service design does not systematically deal with how to define, deploy and operate cross-platform capable cloud services. This is mainly due to inherent complexity of the field and differences in details between a plenty of existing public and private cloud infrastructures. One way to handle this complexity is to restrict cloud service design to a common subset of commodity features provided by existing public and private cloud infrastructures. Nevertheless these restrictions raise new service design questions and have to be answered in ongoing research in a pragmatic manner regarding the limited IT-operation capabilities of small and medium sized enterprises. By simplifying and harmonizing the use of cloud infrastructures using lightweight virtualization approaches, the transfer of cloud deployments between a variety of cloud service providers will become possible. This article will discuss several aspects like high availability, secure communication, elastic service design, transferability of services and formal descriptions of service deployments which have to be addressed and are investigated by our ongoing research.展开更多
Infrastructure as a Service(IaaS)provides logical separation between data,network,applications and machines from the physical constrains of real machines.IaaS is one of the basis of cloud virtualization.Recently,secur...Infrastructure as a Service(IaaS)provides logical separation between data,network,applications and machines from the physical constrains of real machines.IaaS is one of the basis of cloud virtualization.Recently,security issues are also gradually emerging with virtualization of cloud computing.Different security aspects of cloud virtualization will be explored in this research paper,security recognizing potential threats or attacks that exploit these vulnerabilities,and what security measures are used to alleviate such threats.In addition,a dis-cussion of general security requirements and the existing security schemes is also provided.As shown in this paper,different components of virtualization environ-ment are targets to various attacks that in turn leads to security issues compromis-ing the whole cloud infrastructure.In this paper an overview of various cloud security aspects is also provided.Different attack scenarios of virtualization envir-onments and security solutions to cater these attacks have been discussed in the paper.We then proceed to discuss API security concerns,data security,hijacking of user account and other security concerns.The aforementioned discussions can be used in the future to propose assessment criteria,which could be useful in ana-lyzing the efficiency of security solutions of virtualization environment in the face of various virtual environment attacks.展开更多
This paper puts forward sharing teaching resources based on cloud computing solutions, through the use of architecture means virtualization technology based on KVM on the server side, the infrastructure layer manage t...This paper puts forward sharing teaching resources based on cloud computing solutions, through the use of architecture means virtualization technology based on KVM on the server side, the infrastructure layer manage the underlying physical hardware equipment. In the realization of the infrastructure layer using Libvirt virtualization management suite that provides a common API development Web, through the RDP protocol, and finally access to the remote virtual desktop browser by the graphical user interface （GUI） and traditional Web B/S architecture, to simulate and access to low-level resources and sharing of teaching resources, teaching resources can be achieved education informatization in the process of teaching.展开更多
Economic Management Professional Academic Education are increasingly becoming personalization,intelligence and application.Colleges and universities should actively use cloud computing and big data.Also Internet of Th...Economic Management Professional Academic Education are increasingly becoming personalization,intelligence and application.Colleges and universities should actively use cloud computing and big data.Also Internet of Things and other advanced information technologies to build an economics and management ERP virtual simulation experiment teaching platform.Cloud computing and big data,virtual simulation experiment teaching resources with"resource library+project library+enterprise management simulation sandbox training"as the core can build an online and offline collaborative and practical experiment teaching platform.It is expected to achieve the ideal effect of integration of three spaces.Such as physics and resources and social digital teaching.Moreover,it can also benefit human-computer collaboration and interactive teaching and inquiry learning.展开更多
Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the i...Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.展开更多
基金Project supported by the Shanghai Leading Academic Discipline Project(Grant No.J50103)the Ph D Programs Foundation of Ministry of Education of China(Grant No.200802800007)+1 种基金the Key Laboratory of Computer System and Architecture(Institute of Computing Technology,Chinese Academy of Sciences)the Innovation Project of Shanghai Municipal Education Commission(Grant No.11YZ09)
文摘Cloud computing is a new computing model. The resource monitoring tools are immature compared to traditional distributed computing and grid computing. In order to better monitor the virtual resource in cloud computing, a periodically and event-driven push （PEP） monitoring model is proposed. Taking advantage of the push and event-driven mechanism, the model can provide comparatively adequate information about usage and status of the resources. It can simplify the communication between Master and Work Nodes without missing the important issues happened during the push interval. Besides, we develop ＂mon＂ to make up for the deficiency of Libvirt in monitoring of virtual CPU and memory.
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Online Education (OE) system is an effective and efficient way to perform the education in all sectors of government and non-government educational organization. Low performance and minimum speed are major overhead in the current ongoing OE system due to the increase of users and some system issues. Base on the previous study and recent practical issues, a model is proposed to Enhancing the Performance of Online Education System (EPOES) to examine the bare metal virtualization, isolation and virtual machine templates. Bare metal virtualization has led the native execution, isolation isolated the running application and Virtual Machine Template has help to increase efficiency, avoiding the repetitive installation and operate the server in less time. The proposed model boosts the performance of the current OE system, and examines the benefits of the adaptation of cloud computing and virtualization which can be used to overcome the existing challenges and barriers of the current OE System.
文摘The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems.This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network(QT-DNN)with Binary Bird Swarm Optimization(BBSO)to enhance resource allocation while preserving Quality of Service(QoS).In contrast to conventional approaches,the QT-DNN accurately predicts task-resource mappings using tensor-based task representation,significantly minimizing computing overhead.The BBSO allocates resources dynamically,optimizing energy efficiency and task distribution.Experimental results from extensive simulations indicate the efficacy of the suggested strategy;the proposed approach demonstrates the highest level of accuracy,reaching 98.1%.This surpasses the GA-SVM model,which achieves an accuracy of 96.3%,and the ART model,which achieves an accuracy of 95.4%.The proposed method performs better in terms of response time with 1.598 as compared to existing methods Energy-Focused Dynamic Task Scheduling(EFDTS)and Federated Energy-efficient Scheduler for Task Allocation in Large-scale environments(FESTAL)with 2.31 and 2.04,moreover,the proposed method performs better in terms of makespan with 12 as compared to Round Robin(RR)and Recurrent Attention-based Summarization Algorithm(RASA)with 20 and 14.The hybrid method establishes a new standard for sustainable and efficient administration of cloud computing resources by explicitly addressing scalability and real-time performance.
文摘In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.
基金funded by Multimedia University(Ref:MMU/RMC/PostDoc/NEW/2024/9804).
文摘Networking,storage,and hardware are just a few of the virtual computing resources that the infrastruc-ture service model offers,depending on what the client needs.One essential aspect of cloud computing that improves resource allocation techniques is host load prediction.This difficulty means that hardware resource allocation in cloud computing still results in hosting initialization issues,which add several minutes to response times.To solve this issue and accurately predict cloud capacity,cloud data centers use prediction algorithms.This permits dynamic cloud scalability while maintaining superior service quality.For host prediction,we therefore present a hybrid convolutional neural network long with short-term memory model in this work.First,the suggested hybrid model is input is subjected to the vector auto regression technique.The data in many variables that,prior to analysis,has been filtered to eliminate linear interdependencies.After that,the persisting data are processed and sent into the convolutional neural network layer,which gathers intricate details about the utilization of each virtual machine and central processing unit.The next step involves the use of extended short-term memory,which is suitable for representing the temporal information of irregular trends in time series components.The key to the entire process is that we used the most appropriate activation function for this type of model a scaled polynomial constant unit.Cloud systems require accurate prediction due to the increasing degrees of unpredictability in data centers.Because of this,two actual load traces were used in this study’s assessment of the performance.An example of the load trace is in the typical dispersed system.In comparison to CNN,VAR-GRU,VAR-MLP,ARIMA-LSTM,and other models,the experiment results demonstrate that our suggested approach offers state-of-the-art performance with higher accuracy in both datasets.
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With the rapid development of social,science and technology ,we are always looking for the advanced and rapid manufacturing method and the management pattern.thus a new enterprise cooperation pattern-Virtual Enterprise arises at the historic moment. The cooperation is a process which advantages the temporary enterprise resources each other. Therefore, the virtual enterprise must encounter the problem that how to realize the virtual enterprises’ information resources sharing and improve the efficiency of enterprise cooperation. This paper uses the cloud computing’s advantage to solve the problem of virtual enterprise information resources sharing. Then enterprise is able to share the information of different regions,different computing environment and improve the efficiency of virtual enterprise cooperation.
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.
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.
文摘The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.
文摘The data and applications in cloud computing reside in cyberspace, that allowing to users access data through any connection device, when you need to transfer information over the cloud, you will lose control of it. There are multi types of security challenge must be understood and countermeasures. One of the major security challenges is resources of the cloud computing infrastructures are provided as services over the Internet, and entire data in the cloud computing are reside over network resources, that enables the data to be access through VMs. In this work, we describe security techniques for securing a VCCI, VMMs such as Encryption and Key Management (EKM), Access Control Mechanisms (ACMs), Virtual Trusted Platform Module (vTPM), Virtual Firewall (VF), and Trusted Virtual Domains (TVDs). In this paper we focus on security of virtual resources in Virtualized Cloud Computing Infrastructure (VCCI), Virtual Machine Monitor (VMM) by describing types of attacks on VCCI, and vulnerabilities of VMMs and we describe the techniques for securing a VCCI.
基金supported by The National Natural Science Foundation for Young Scientists of China under Grant No.61303263the Jiangsu Provincial Research Foundation for Basic Research(Natural Science Foundation)under Grant No.BK20150201+4 种基金the Scientific Research Key Project of Beijing Municipal Commission of Education under Grant No.KZ201210015015Project Supported by the National Natural Science Foundation of China(Grant No.61370140)the Scientific Research Common Program of the Beijing Municipal Commission of Education(Grant No.KMKM201410015006)The National Science Foundation of China under Grant Nos.61232016 and U1405254and the PAPD fund
文摘This paper sums up four security factors after analyzing co-residency threats caused by the special multitenant environment in the cloud.To secure the factors,a multiway dynamic trust chain transfer model was proposed on the basis of a measurement interactive virtual machine and current behavior to protect the integrity of the system.A trust chain construction module is designed in a virtual machine monitor.Through dynamic monitoring,it achieves the purpose of transferring integrity between virtual machine.A cloud system with a trust authentication function is implemented on the basis of the model,and its practicability is shown.
文摘In the field of cloud computing, topics such as computing resource virtualization, differences between grid and cloud computing, relationship between high-performance computers and cloud computing centers, and cloud security and standards have attracted much research interest. This paper analyzes these topics and highlights that resource virtualization allows information services to be scalable, intensive, and specialized; grid computing involves using many computers for large-scale computing tasks, while cloud computing uses one platform for multiple services; high-performance computers may not be suitable for a cloud computing; security in cloud computing focuses on trust management between service suppliers and users; and based on the existing standards, standardization of cloud computing should focus on interoperability between services.
文摘To overcome vendor lock-in obstacles in public cloud computing, the capability to define transferable cloud-based services is crucial but has not yet been solved satisfactorily. This is especially true for small and medium sized enterprises being typically not able to operate a vast staff of cloud service and IT experts. Actual state of the art cloud service design does not systematically deal with how to define, deploy and operate cross-platform capable cloud services. This is mainly due to inherent complexity of the field and differences in details between a plenty of existing public and private cloud infrastructures. One way to handle this complexity is to restrict cloud service design to a common subset of commodity features provided by existing public and private cloud infrastructures. Nevertheless these restrictions raise new service design questions and have to be answered in ongoing research in a pragmatic manner regarding the limited IT-operation capabilities of small and medium sized enterprises. By simplifying and harmonizing the use of cloud infrastructures using lightweight virtualization approaches, the transfer of cloud deployments between a variety of cloud service providers will become possible. This article will discuss several aspects like high availability, secure communication, elastic service design, transferability of services and formal descriptions of service deployments which have to be addressed and are investigated by our ongoing research.
文摘Infrastructure as a Service(IaaS)provides logical separation between data,network,applications and machines from the physical constrains of real machines.IaaS is one of the basis of cloud virtualization.Recently,security issues are also gradually emerging with virtualization of cloud computing.Different security aspects of cloud virtualization will be explored in this research paper,security recognizing potential threats or attacks that exploit these vulnerabilities,and what security measures are used to alleviate such threats.In addition,a dis-cussion of general security requirements and the existing security schemes is also provided.As shown in this paper,different components of virtualization environ-ment are targets to various attacks that in turn leads to security issues compromis-ing the whole cloud infrastructure.In this paper an overview of various cloud security aspects is also provided.Different attack scenarios of virtualization envir-onments and security solutions to cater these attacks have been discussed in the paper.We then proceed to discuss API security concerns,data security,hijacking of user account and other security concerns.The aforementioned discussions can be used in the future to propose assessment criteria,which could be useful in ana-lyzing the efficiency of security solutions of virtualization environment in the face of various virtual environment attacks.
文摘This paper puts forward sharing teaching resources based on cloud computing solutions, through the use of architecture means virtualization technology based on KVM on the server side, the infrastructure layer manage the underlying physical hardware equipment. In the realization of the infrastructure layer using Libvirt virtualization management suite that provides a common API development Web, through the RDP protocol, and finally access to the remote virtual desktop browser by the graphical user interface （GUI） and traditional Web B/S architecture, to simulate and access to low-level resources and sharing of teaching resources, teaching resources can be achieved education informatization in the process of teaching.
基金the 2020 University Innovation and Entrepreneurship Project of Guangdong University of Foreign Studies.
文摘Economic Management Professional Academic Education are increasingly becoming personalization,intelligence and application.Colleges and universities should actively use cloud computing and big data.Also Internet of Things and other advanced information technologies to build an economics and management ERP virtual simulation experiment teaching platform.Cloud computing and big data,virtual simulation experiment teaching resources with"resource library+project library+enterprise management simulation sandbox training"as the core can build an online and offline collaborative and practical experiment teaching platform.It is expected to achieve the ideal effect of integration of three spaces.Such as physics and resources and social digital teaching.Moreover,it can also benefit human-computer collaboration and interactive teaching and inquiry learning.
基金the Ocean Public Welfare Scientific Research Project of State Oceanic Administration of China under contract No.201105033
文摘Based on the features of marine environmental data and processing requirements, a cloud computing archi- tecture of marine environment information is proposed, which provides a new cloud technology framework for the integration and sharing of marine environmental information resources. A physical layer, software platform layer and an application layer are illustrated systematically, at the same time, a corresponding solu- tions for many difficult technical problems such as parallel query processing of multi-dimensional, spatio- temporal information, data slice storage, software service flow customization, analysis, reorganization and so on. A prototype system is developed and many different data-size experiments and a comparative analy- sis are done based on it. The experiment results show that the cloud platform based on this framework can achieve high performance and scalability when dealing with large-scale marine data.
