A Virtual Machine Placement Strategy Based on Virtual Machine Selection and Integration

Cloud data centers face the largest energy consumption.In order to save energy consumption in cloud data centers,cloud service providers adopt a virtual machine migration strategy.In this paper,we propose an efficient virtual machine placement strategy(VMP-SI)based on virtual machine selection and integration.Our proposed VMP-SI strategy divides the migration process into three phases:physical host state detection,virtual machine selection and virtual machine placement.The local regression robust(LRR)algorithm and minimum migration time(MMT)policy are individual used in the first and section phase,respectively.Then we design a virtual machine migration strategy that integrates the process of virtual machine selection and placement,which can ensure a satisfactory utilization efficiency of the hardware resources of the active physical host.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.

Virtual machine placement optimizing to improve network performance in cloud data centers

With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine （VM） is becoming a major issue for cloud providers. The existing virtual machine placement （VMP） solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization （MLU）. On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.

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.

Chaotic Simulator for Bilevel Optimization of Virtual Machine Placements in Cloud Computing

The drastic increase in engineering system complexity has spurred the development of highly efficient optimization techniques.Many real-world optimization problems have been identified as bilevel/multilevel as well as multiobjective.The primary aim of this work is to present a framework to tackle the bilevel virtual machine(VM)placement problem in cloud systems.This is done using the coupled map lattice(CML)approach in conjunction with the Stackelberg game theory and weighted-sum frameworks.The VM placement problem was modified from the original multiobjective(MO)problem to an MO bilevel formulation to make it more realistic albeit more complicated.Additionally comparative analysis on the performance of the CML approach was carried out against the particle swarm optimization method.A new bilevel metric called the cascaded hypervolume indicator is introduced and applied to measure the dominance of the solutions produced by both methods.Detailed analysis on the computational results is presented.

A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems

Virtualization is the most important technology in the unified resource layer of cloud computing systems.Static placement and dynamic management are two types of Virtual Machine(VM)management methods.VM dynamic management is based on the structure of the initial VM placement,and this initial structure will affect the efficiency of VM dynamic management.When a VM fails,cloud applications deployed on the faulty VM will crash if fault tolerance is not considered.In this study,a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors,including the service-level agreement violation rate,resource remaining rate,power consumption rate,failure rate,and fault tolerance cost.Then,a heuristic ant colony algorithm is proposed to solve the model.The service-providing VMs are placed by the ant colony algorithms,and the redundant VMs are placed by the conventional heuristic algorithms.The experimental results obtained from the simulation,real cluster,and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.

Deadline based scheduling for data-intensive applications in clouds

Cloud computing emerges as a new computing pattern that can provide elastic services for any users around the world. It provides good chances to solve large scale scientific problems with fewer efforts. Application deployment remains an important issue in clouds. Appropriate scheduling mechanisms can shorten the total completion time of an application and therefore improve the quality of service（QoS） for cloud users. Unlike current scheduling algorithms which mostly focus on single task allocation, we propose a deadline based scheduling approach for data-intensive applications in clouds. It does not simply consider the total completion time of an application as the sum of all its subtasks＇ completion time. Not only the computation capacity of virtual machine（VM） is considered, but also the communication delay and data access latencies are taken into account. Simulations show that our proposed approach has a decided advantage over the two other algorithms.