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 resou...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.展开更多
Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resour...Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.展开更多
Cloud computing makes it possible for users to share computing power.The framework of multiple data centers gains a greater popularity in modern cloud computing.Due to the uncertainty of the requests from users,the lo...Cloud computing makes it possible for users to share computing power.The framework of multiple data centers gains a greater popularity in modern cloud computing.Due to the uncertainty of the requests from users,the loads of center processing unit(CPU)of different data centers differ.High 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 consumption.Therefore,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 centers.The simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.展开更多
文摘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.
文摘Purpose-Current industrial scenario is largely dependent on cloud computing paradigms.On-demand services provided by cloud data centre are paid as per use.Hence,it is very important to make use of the allocated resources to the maximum.The resource utilization is highly dependent on the allocation of resources to the incoming request.The allocation of requests is done with respect to the physical machines present in the datacenter.While allocating the tasks to these physical machines,it needs to be allocated in such a way that no physical machine is underutilized or over loaded.To make sure of this,optimal load balancing is very important.Design/methodology/approach-The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks.The major focus of the proposed work is to optimize the load balancing in a datacenter.When optimization happens,none of the physical machine is neither overloaded nor under-utilized,hence resulting in efficient utilization of the resources.Findings-The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load(RR)ant colony optimization(ACO),artificial bee colony(ABC)with respect to the selected parameters response time,virtual machine migrations,host shut down and energy consumption.All the four parameters gave a positive result when the algorithm is simulated.Originality/value-The contribution of this paper is towards the domain of cloud load balancing.The paper is proposing a novel approach to optimize the cloud load balancing process.The results obtained show that response time,virtual machine migrations,host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study.The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.
基金supported by the Primary Research and Development Plan (Social Development) of Jiangsu Province (BE2017743)
文摘Cloud computing makes it possible for users to share computing power.The framework of multiple data centers gains a greater popularity in modern cloud computing.Due to the uncertainty of the requests from users,the loads of center processing unit(CPU)of different data centers differ.High 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 consumption.Therefore,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 centers.The simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.
