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A Service Level Agreement Aware Online Algorithm for virtual machine migration

Computers, Materials & Continua 2023年第1期74卷 279-291页

作者： Iftikhar Ahmad Ambreen Shahnaz Muhammad Asfand-e-Yar Wajeeha Khalil Yasmin Bano Department of Computer Science and Information Technology University of Engineering and TechnologyPeshawar25120Pakistan Department of Computer Science Shaheed Benazir Bhutto Women UniversityPeshawar25000Pakistan Center of Excellence in Artificial Intelligence Department of Computer ScienceBahria UniversityIslamabadPakistan

The demand for cloud computing has increased manifold in the recent *** specifically,on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing *** cloud service provider fulfills different user requirements using virtualization-where a single physical machine can host multiple *** virtualmachine potentially represents a different user environment such as operating system,programming environment,and ***,these cloud services use a large amount of electrical energy and produce greenhouse *** reduce the electricity cost and greenhouse gases,energy efficient algorithms must be *** specific area where energy efficient algorithms are required is virtual machine *** virtualmachine consolidation,the objective is to utilize the minimumpossible number of hosts to accommodate the required virtual machines,keeping in mind the service level agreement *** research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized *** online algorithm is analyzed using a competitive analysis *** addition,an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark *** proposed online algorithm consumed 25%less energy and performed 43%fewer migrations than the benchmark algorithms.

关键词： Cloud computing green computing online algorithms virtual machine migration

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Layered virtual machine migration algorithm for network resource balancing in cloud computing

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Frontiers of Computer Science 2018年第1期12卷 75-85页

作者： Xiong FU Juzhou CHEN Song DENG Junchang WANG Lin ZHANG School of Computer and Technology Nanjing University of Posts and Telecommunications Nanjing 210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing 210023 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 China

Due to the increasing sizes of cloud data centers, the number of virtual machines （VMs） and applications rises quickly. The rapid growth of large scale Internet services re- suits in unbalanced load of network resource. The bandwidth utilization rate of some physical hosts is too high, and this causes network congestion. This paper presents a layered VM migration algorithm （LVMM）. At first, the algorithm will divide the cloud data center into several regions according to the bandwidth utilization rate of the hosts. Then we bal- ance the load of network resource of each region by VM migrations, and ultimately achieve the load balance of net- work resource in the cloud data center. Through simulation experiments in different environments, it is proved that the LVMM algorithm can effectively balance the load of network resource in cloud computing.

关键词： virtual machine migration cloud computing,layered theory load balancing

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A Resource Management Algorithm for virtual machine migration in Vehicular Cloud Computing

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Computers, Materials & Continua 2021年第5期67卷 2647-2663页

作者： Sohan Kumar Pande Sanjaya Kumar Panda Satyabrata Das Kshira Sagar Sahoo Ashish Kr.Luhach N.Z.Jhanjhi Roobaea Alroobaea Sivakumar Sivanesan Veer Surendra Sai University of Technology Burla Burla768018India National Institute of Technology Warangal Warangal506004India VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad500090India The Papua New Guinea University of Technology Lae-41PapuaNew Guinea School of Computer Science and Engineering Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

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 *** studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are *** a circumstance,the energy consumption of overloaded vehicles is drastically *** the other hand,underloaded vehicles are also drawing considerable energy in the underutilized ***,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC *** proper and efcient utilization of the vehicle’s resources can reduce energy consumption *** of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded *** the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the *** 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 ***-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM *** ensures that any vehicles’resource utilization should not exceed the threshold before or after the ***-VMM also tries to avoid unnecessary VM migrations between the ***-VMM is extensively simulated and tested using nine *** 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

关键词： Resource management virtual machine migration vehicular cloud computing resource utilization source vehicle destination vehicle

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Improved Metaheuristic Based Failure Prediction with migration Optimization in Cloud Environment

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Computer Systems Science & Engineering 2023年第5期45卷 1641-1654页

作者： K.Karthikeyan Liyakathunisa Eman Aljohani Thavavel Vaiyapuri Department of Computer Applications Anna UniversityRegional CampusMaduraiMaduraiIndia Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Cloud data centers consume high volume of energy for processing and switching the servers among different *** machine(VM)migration enhances the performance of cloud servers in terms of energy efficiency,internal failures and *** the other end,energy utilization can be minimized by decreasing the number of active,underutilized sources which conversely reduces the dependability of the *** VM migration process,the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the *** this view,the current study develops an Improved Metaheuristic Based Failure Prediction with virtual machine migration Optimization(IMFP-VMMO)model in cloud *** major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure *** accomplish this,IMFPVMMO model employs Gradient Boosting Decision Tree(GBDT)classification model at initial stage for effectual prediction of VM *** the same time,VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm(QO-AFSA)which in turn reduces the energy *** performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed *** comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.

关键词： Cloud computing energy efficiency virtual machine migration failure prediction energy optimization metaheuristics

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VM migration algorithm for the balance of energy resource across data centers in cloud computing

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The Journal of China Universities of Posts and Telecommunications 2019年第5期26卷 22-32页

作者： Song Da Fu Xiong Zhou Jingjing Wang Junchang Zhang Lin Deng Song Qiao Lei School of Computer Science and Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China Beijing Institute of Control Engineering Beijing 100000China

Cloud computing makes it possible for users to share computing *** framework of multiple data centers gains a greater popularity in modern cloud *** to the uncertainty of the requests from users,the loads of center processing unit(CPU)of different data centers *** CPU utilization rate of a data center affects the service provided for users,while low CPU utilization rate of a data center causes high energy ***,it is important to balance the CPU resource across data centers in modern cloud computing framework.A virtual machine(VM)migration algorithm was proposed to balance the CPU resource across data *** simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.

关键词： cloud computing load balancing across data centers virtual machine migration

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Energy-aware VM migration using dragonfly–crow optimization and support vector regression model in Cloud

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International Journal of Modeling, Simulation, and Scientific Computing 2018年第6期9卷 19-42页

作者： Nitin S.More Rajesh B.Ingle Department of Computer Engineering Pune Institute of Computer Technology(PICT)SPPU Pune 411043MaharashtraIndia

Nowadays,virtual machine migration(VMM)is a trending research since it helps in balancing the load of the Cloud *** VMM-based strategies defined in the literature have considered various metrics,such as load,energy,and migration cost for balancing the load of the *** paper introduces a novel VMM strategy by considering the load of the Cloud *** important aspects of the proposed scheme are the load prediction through the support vector regression(SVR)and the optimal VM placement through the proposed dragonfly-based crow(D-Crow)optimization *** proposed D-Crow optimization algorithm is developed by incorporating crow search algorithm(CSA)into dragonfly algorithm(DA).Also,the proposed VMM strategy defines a load balancing model based on the energy consumption,load,and the migration cost to achieve the energy-aware *** simulation of the proposed VMM strategy is done based on the metrics such as load,energy consumption,and the migration *** the results,it can be shown that the proposed VMM strategy surpassed other comparative models by achieving the minimum values of 7.3719%,10.0368%,and 11.0639%for the load,energy consumption,and migration cost,respectively.

关键词： virtual machine migration load balancing load prediction optimal VM placement energy awareness.

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Data Center Network Architecture

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ZTE Communications 2013年第1期11卷 54-61页

作者： Yantao Sun Jing Cheng Konggui Shi Qiang Liu School of Computer and Information Technology Beijing Jiaotong University

1 Introduction The history of data centers can be traced back to the 1960s. Early data centers were deployed on main- frames that were time-shared by users via remote terminals. The boom in data centers came duringthe... 详细信息

关键词： data center network network architecture network topology virtual machine migration

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