Reservation Resource Technique for Virtual Machine Placement in Cloud Data Centre
نویسنده
چکیده
Migrations of Virtual Machine directly influence on energy consumption and QoS, to avoid migration of virtual machine when a host is overloaded a good placement technique need to be applied. Virtual Machine Placement is vital in cloud computing to utilize the resources in an efficient manner. Migration of a VM instance when a host is overloaded is familiar in cloud computing. VM selection policy finds a suitable VM to migrate from overloaded host and place to an under loaded host or turn on a new host. While migration there is small downtime of the service, even thou down time is small there is a huge change in energy consumption. Energy consumption in data centre has lead to emission of carbon dioxide to the environment. Frequent VM migration may cause the services to high latency in the network and may disturb the network environment. These works focus to reduce the VM migration, improve SLA and energy consumption. Therefore, a reservation method known as RTBBE (RTBBE (Reservation Technique Bin BECK Entropy) proposed in the study that is by allocating and assigning double upper threshold with entropy method with new overload detection PR (Polynomial Regression) and a VM selection policy MUR (Minimum Utilization Rank) had proposed in this study. The result shows that the proposed technique reduces the energy consumption, SLA and VM migration. Experimental shows that the proposed method reduce the energy up to 21.30 kWh when the overload detection PR combines with MUR, SLA of 0.00029% with IQR with MUR and 775 VM were migrated with LRR and MC.
منابع مشابه
Communication-Aware Traffic Stream Optimization for Virtual Machine Placement in Cloud Datacenters with VL2 Topology
By pervasiveness of cloud computing, a colossal amount of applications from gigantic organizations increasingly tend to rely on cloud services. These demands caused a great number of applications in form of couple of virtual machines (VMs) requests to be executed on data centers’ servers. Some of applications are as big as not possible to be processed upon a single VM. Also, there exists severa...
متن کاملBasip a Virtual Machine Placement Technique to Reduce Energy Consumption in Cloud Data Centre
Infrastructure as a Service (IaaS) in cloud computing provides Infrastructure as a service for the demand of user from small instance to large instance in pay per use model. The services include like computer resource, networking and data storage. An API (Application Programming Interface) is used to access the infrastructure and a dashboard to control the server and to create and manage differ...
متن کاملVM Consolidation by using Selection and Placement of VMs in Cloud Datacenters
The Cloud Computing model leverages virtualization of computing resources allowing customers to provision resources on-demand on a pay-as-you-go basis. During recent years, the power consumption of datacenters in cloud environment attracted researchers. Optimization of energy consumption can be performed by different methods including virtual machine (VM) consolidation. This technique can reduc...
متن کاملA Genetic Based Resource Management Algorithm Considering Energy Efficiency in Cloud Computing Systems
Cloud computing is a result of the continuing progress made in the areas of hardware, technologies related to the Internet, distributed computing and automated management. The Increasing demand has led to an increase in services resulting in the establishment of large-scale computing and data centers, in addition to high operating costs and huge amounts of electrical power consumption. Insuffic...
متن کاملEfficient Virtual Machine Placement with Energy Saving in Cloud Data Center
Cloud data centers provide computing infrastructure as a service to their customers on pay per use basis. In virtualized data centers CPU, RAM, storage and bandwidth are allotted to a Virtual Machine (VM) from pool of shared resources. An autonomic consolidation of VMs on appropriate Physical Machine (PM) by achieving performance and saving cost is the key challenge for virtualized data centers...
متن کامل