Virtual Machine Placement Optimization Research Articles

Optimization of virtual machine placement based on constrained immune memory and immunodominance clone in IaaS cloud mode equipment training

In infrastructure as a service (IaaS) cloud mode equipment simulated training, to keep the resource utilization ratio in a rational high level, improve the training effect and reduce the system running cost, the problem of training virtual machine (TVM) placement needs to be resolved first. We make analysis to the problem and give the mathematical formulation to the problem. Then, we figure out the principle and target of the TVM placement. Based on above analysis, we propose a constrained immune memory and immunodominance clone (CIMIC) TVM placement optimization algorithm. By reverse optimization of the initial antibody population, the searching range is reduced. The common antibody population and the immunodominance antibody population evolve simultaneously, which realizes the simultaneous progressing of global searching and local searching of solutions. Further, local optimal is avoided by this means. Memory antibody makes ful use of the unfeasible solutions and the diversity of antibody population is maintained. The constraint information of the problem is utilized to improve the optimization effect. Experiment results show that the CIMIC algorithm improves the overall optimization effect of TVM placement, reduces the server number and improves the resource utilization and system stability.

Adaptive Management and Multi Objective Optimization of Virtual Machine Placement in Cloud Computing

Adaptive Management and Multi Objective Optimization of Virtual Machine Placement in Cloud Computing

Towards energy efficient cloud: an optimized ant colony model for virtual machine placement

A virtual machine placement optimization model based on optimized ant colony algorithm is proposed. The model is able to determine the physical machines suitable for hosting migrated virtual machines. Thus, it solves the problem of redundant power consumption resulting from idle resource waste of physical machines. First, based on the utilization parameters of the virtual machine, idle resources and energy consumption models are proposed. The models are dedicated to quantifying the features of virtual resource utilization and energy consumption of physical machines. Next, a multi-objective optimization strategy is derived for virtual machine placement in cloud environments. Finally, an optimal virtual machines placement scheme is determined based on feature metrics, multi-objective optimization, and the ant colony algorithm. Experimental results indicate that compared with the traditional genetic algorithms-based MGGA model, the convergence rate is increased by 16%, and the optimized highest average energy consumption is reduced by 18%. The model exhibits advantages in terms of algorithm efficiency and efficacy.

Provision of Data-Intensive Services Through Energy- and QoS-Aware Virtual Machine Placement in National Cloud Data Centers

Many data-intensive services (e.g., planet analysis, gene analysis, and so on) are becoming increasingly reliant on national cloud data centers (NCDCs) because of growing scientific collaboration among countries. In NCDCs, tens of thousands of virtual machines (VMs) are assigned to physical servers to provide data-intensive services with a quality-of-service (QoS) guarantee, and consume a massive amount of energy in the process. Although many VM placement schemes have been proposed to solve this problem of energy consumption, most of these assume that all the physical servers are homogeneous. However, the physical server configurations of NCDCs often differ significantly, which leads to varying energy consumption characteristics. In this paper, we explore an alternative VM placement approach to minimize energy consumption during the provision of data-intensive services with a global QoS guarantee in NCDCs. We use an improved particle swarm optimization algorithm to develop an optimal VM placement approach involving a tradeoff between energy consumption and global QoS guarantee for data-intensive services. Experimental results show that our approach significantly outperforms other approaches to energy optimization and global QoS guarantee in NCDCs.

Optimization of Virtual Machine Placement in Cloud Environment Using Genetic Algorithm

The current trend in the computing era is cloud computing, which helps in providing seamless service to the user, but optimizing the utilization of the available resources and an efficient placement of the virtual machines are not so significant in the existing phenomena. Virtual machines are software computers that act as the key feature in providing services to the existing physical machine. VM placement is the process of mapping the virtual machine requests or images to the physical machines, according to the availability of resources in these hosts. Hence in this study, we have studied on various methods in which the placement are being done and have proposed an idea which, treats the available pool of physical resources as each knapsacks, which are solved using genetic algorithm, to get an optimal placement. Starting with this aspect, we enhanced the solution by considering multiple and multidimensional parameters in the virtual machine request, so that the migration of the virtual machines will be reduced and hence power-saving.

Service-Oriented Virtual Machine Placement Optimization for Green Data Center

The first service-oriented virtual machine (VM) placement for green data center is designed in this work. Integer Linear Programming (ILP) is the problem design basis. The Tree algorithm is propose...

Orchestrating connectivity services to support elastic operations in datacenter federations

Datacenter federations are able to manage appropriately the green energy resources available in each datacenter (DC) thanks to their geographically distributed infrastructure, thus reducing energy expenditure. Scheduling algorithms can compute virtual machine migration, transferring huge amounts of raw data from one DC to another to minimize operational costs and ensuring a certain Quality of Experience. Because green energy availability greatly depends on weather conditions, in this work we present a statistical model to improve green solar energy availability estimation accuracy and we use it in a mixed integer linear programming formulation to compute optimal virtual machine placement. Optical connections can be used to provide connectivity services of enough capacity to support those migrations. In particular, elastic optical networks can provide connections with multi-granular bitrate, which can be adapted on demand. DC resource managers can request optical connections and control their capacity. However, that scheme involves the resource managers to implement algorithms and interfaces to deal with network specifics and complexity. To solve that issue, in this paper we propose coordinating transfer-based inter-DC connectivity services; inter-DC connectivity is requested in terms of volume of data and completion time. We analyze cost savings when each connectivity model is applied in a DC federation. For the sake of a compelling analysis, exhaustive simulation experiments are carried out considering realistic scenarios. Results show that the notification-based model can save up to 20 % of energy costs and more than 40 % of communication costs in the evaluated scenarios.

MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement

Cloud data centers are facing increasingly virtual machine (VM) placement problems, such as high energy consumption, imbalanced utilization of multidimension resource, and high resource wastage rate. In order to solve the virtual machine placement problems in large scale, three algorithms are proposed. Firstly, we propose a physical machine (PM) classification algorithm by analyzing pseudotime complexity and find out an important factor (the number of physical hosts) that affects the efficiency, which improves running efficiency through reduction number of physical hosts; secondly, we present a VM placement optimization model using multitarget heuristic algorithm and figure out the positive and negative vectors of three goals using matrix transformation so as to provide the mapping of VMs to hosts by comparing distance with positive and negative vectors such that the energy consumption is saved, resources wastage of occupied PM is lowered, multidimension resource utilization is optimized, and the running time is shortened. Finally, we consider the poor placement efficiency problem of large-scale virtual serial requests and design a concurrent VM classification algorithm using theK-means method. Simulation experiments validate the performance of the algorithm in four aspects, including placement efficiency, resources utilization balance rate, wastage rate, and energy consumption.