Energy Efficient Virtual Machine Allocation Research Articles

Energy and QoS aware resource allocation for heterogeneous sustainable cloud datacenters

As the demand on Internet services such as cloud and mobile cloud services drastically increases recently, the energy consumption consumed by the cloud datacenters has become a burning topic. The deployment of renewable energy generators such as PhotoVoltaic (PV) and wind farms is an attractive candidate to reduce the carbon footprint and, achieve the sustainable cloud datacenters. However, current studies have focused on geographical load balancing of Virtual Machine (VM) requests to reduce the cost of brown energy usage, while most of them have ignored the heterogeneity of power consumption of each cloud datacenter and the incurred performance degradation by VM co-location. In this paper, we propose Evolutionary Energy Efficient Virtual Machine Allocation (EEE-VMA), a Genetic Algorithm (GA) based metaheuristic which supports a power heterogeneity aware VM request allocation of multiple sustainable cloud datacenters. This approach provides a novel metric called powerMark which diagnoses the power efficiency of each cloud datacenter in order to reduce the energy consumption of cloud datacenters more efficiently. Furthermore, performance degradation caused by VM co-location and bandwidth cost between cloud service users and cloud datacenters are considered to avoid the deterioration of Quality-of-Service (QoS) required by cloud service users by using our proposed cost model. Extensive experiments including real-world traces based simulation and the implementation of cloud testbed with a power measuring device are conducted to demonstrate the energy efficiency and performance assurance of the proposed EEE-VMA approach compared to the existing VM request allocation strategies.

Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center

Presently, massive energy consumption in cloud data center tends to be an escalating threat to the environment. To reduce energy consumption in cloud data center, an energy efficient virtual machine allocation algorithm is proposed in this paper based on a proposed energy efficient multiresource allocation model and the particle swarm optimization (PSO) method. In this algorithm, the fitness function of PSO is defined as the total Euclidean distance to determine the optimal point between resource utilization and energy consumption. This algorithm can avoid falling into local optima which is common in traditional heuristic algorithms. Compared to traditional heuristic algorithms MBFD and MBFH, our algorithm shows significantly energy savings in cloud data center and also makes the utilization of system resources reasonable at the same time.

Energy Efficient Virtual Machine Allocation in Cloud

Cloud data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. When virtualized resources of cloud data centers are allocated, the energy consumption is a problem to be considered necessarily. In this paper, three energy efficient virtual machine allocation algorithms are carried on experimental analysis, including MAX, DVFS and dynamic deployment algorithm (DDA). We have evaluated these algorithms by conducting a performance evaluation study using the CloudSim toolkit. The experimental results show that the dynamic virtual machine allocation algorithm like as DDA is more energy-efficient and the virtual machine migration is an efficient saving energy method.

Performance bounded energy efficient virtual machine allocation in the global cloud

Reducing energy consumption is a critical step in lowering data center operating costs for various institutions. As such, with the growing popularity of cloud computing, it is necessary to examine various methods by which energy consumption in cloud environments can be reduced. We analyze the effects of global virtual machine allocation on energy consumption, using a variety of real-world policies and a realistic testing scenario. We found that by using an allocation policy designed to minimize energy, total energy consumption could be reduced by up to 14%, and total monetary energy costs could be reduced by up to 26%. Further, we have begun performance qualification of our energy cost driven allocation policies through network capability tests. Our results indicate that performance and IaaS provider implementation costs have a significant influence on selection of optimal virtual machine allocation policies.