Energy Efficient Virtual Machine Placement Research Articles

Energy-efficient virtual machine placement in heterogeneous cloud data centers: a clustering-enhanced multi-objective, multi-reward reinforcement learning approach

Energy-efficient virtual machine placement in heterogeneous cloud data centers: a clustering-enhanced multi-objective, multi-reward reinforcement learning approach

Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine placement has a significant impact on the energy efficiency of virtualized data centers. Among various methods to solve the virtual-machine placement problem, the genetic algorithm has been well accepted for its quality of solution. However, it is computationally demanding largely due to the complex form of fitness function, limiting its applications in data centers. To enhance the computational efficiency of the genetic algorithm while maintaining its quality of solution, a progressive-fidelity approach is developed in this paper for genetic-algorithm computation. It starts with a low-fidelity genetic algorithm with a simple fitness function. Then, for solution refinement, it switches to a medium-fidelity genetic algorithm with a more complicated fitness function. Finally, it progresses to the fine tuning of solution through a high-fidelity genetic algorithm with the energy consumption of data centers as fitness function. Heuristics are presented for the adaptive switching of genetic-algorithm computation from low fidelity to medium fidelity and finally to high fidelity. Experiments show that compared with the standard genetic algorithm of high fidelity, our progressive-fidelity approach of genetic algorithm computation is 50% faster for large-scale data centers while maintaining similar quality of solution in terms of the energy consumption of data centers.

A decentralized adaptation of model-free Q-learning for thermal-aware energy-efficient virtual machine placement in cloud data centers

The traditional method of saving energy in Virtual Machine Placement (VMP) is based on consolidating more virtual machines (VMs) in fewer servers and putting the rest in sleep mode, which may lead to the overheating of servers resulting in performance degradation and cooling cost. The lack of an accurate and computationally efficient model to describe the thermal condition of the data center environment makes it challenging to develop an effective and adaptive VMP mechanism. Although recently, data-driven approaches have acted successfully in model construction, the shortage of clean, adequate, and sufficient amounts of data put limits their generalizability. Moreover, any change in the data center configuration during operation, makes these models prone to error and forces them to repeat the learning process. Thus, researchers turn to applying model-free paradigms such as reinforcement learning. Due to the vast action-state space of real-world applications, scalability is one of the significant challenges in this area. In addition, the delayed feedback of environmental variables such as temperature give rise to exploration costs. In this paper, we present a decentralized implementation of reinforcement learning along with a novel state-action representation to perform the VMP in the data centers to optimize energy consumption and keep the host temperature as low as possible while satisfying Service Level Agreements (SLA). Our experimental results show more than 17% improvement in energy consumption and 12% in CPU temperature reduction compared to baseline algorithms. We also succeeded in accelerating optimal policy convergence after the occurrence of a configuration change.

Energy-efficient virtual machine placement algorithm based on power usage

Energy-efficient virtual machine placement algorithm based on power usage

Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers

Energy efficiency is a critical issue in the management and operation of cloud data centers, which form the backbone of cloud computing. Virtual machine (VM) placement has a significant impact on energy-efficiency improvement for virtualized data centers. Among various methods to solve the VM-placement problem, the genetic algorithm (GA) has been well accepted for the quality of its solution. However, GA is also computationally demanding, particularly in the computation of its fitness function. This limits its application in large-scale systems or specific scenarios where a fast VM-placement solution of good quality is required. Our analysis in this paper reveals that the execution time of the standard GA is mostly consumed in the computation of its fitness function. Therefore, this paper designs a data structure extended from a previous study to reduce the complexity of the fitness computation from quadratic to linear one with respect to the input size of the VM-placement problem. Incorporating with this data structure, an alternative fitness function is proposed to reduce the number of instructions significantly, further improving the execution-time performance of GA. Experimental studies show that our approach achieves 11 times acceleration of GA computation for energy-efficient VM placement in large-scale data centers with about 1500 physical machines in size.

A Study on Transfer Functions of Binary Particle Swarm Optimization for Energy-Efficient VM Placement

Cloud computing has emerged as the backbone of the IT industry for infrastructural support. In cloud computing, resources are virtualized in form of Virtual Machines which eventually is mapped to physical infrastructure. Energy-efficient virtual machine placement is an important problem in cloud computing and has attracted the attention of researchers in recent. As virtual machine placement is an NP-hard problem, meta-heuristics have often been applied vastly for this. In one of our earlier works, Modified Binary Particle Swarm Optimization algorithm has been applied for the VM placement. It was observed therein that the transfer function, which plays an important role to obtain an optima, does not completely avoid the problem of local optima. Therefore, in this work, we have studied the behavior of eight different transfer functions towards this property. For this, energy-efficient VM placement problem is modelled as multi-objective optimization problem and Binary Particle Swarm Optimization is applied. The study is done by simulation and their statistical analysis.

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

Energy efficiency is a critical issue in data centre management, which is the foundation for cloud computing. The VM placement has a considerable impact on a data centre's energy efficiency and resource utilisation. The assignment of VMs to PMs is an NP-hard problem without an easy way to find an optimal solution, particularly in large-scale data centres. In this study, the VM placement problem is formulated as a constrained optimisation problem. The Genetic Algorithm (GA) is a suitable method for solving this problem in terms of the quality of the solution. However, GA is time-consuming to obtain an optimal solution in the large scale optimisation problem. Therefore, this paper focuses on accelerated GA for energy-efficient VM placement. As the most time-consuming element of the GA is the calculation of its fitness function, this paper simplifies this calculation through a new fitness function in GA. Simulation results of small-, medium-, and large-scale data centres demonstrate that our accelerated GA is faster than the standard GA and gives better quality of solution than the First Fit Decreasing (FFD) algorithm, respectively. The findings of our GA with the new fitness function reveal an 8% energy saving for our GA compared to FFD and a 66% reduction in our GA execution time compared to the standard GA with standard energy formula as a fitness function. The number of generations in our GA is reduced by about 50% in comparison with the standard GA. Moreover, we started with 3000 PMs in the large-scale dataset, and only 1086 PMs were actually used after running our GA. Therefore, we may switch off far more PMs for energy savings from our GA results than those from the standard GA.

Energy-Efficient Scientific Workflow Scheduling Algorithm in Cloud Environment

Scheduling extensive scientific applications that are deadline-aware (usually referred to as workflow) is a difficult task. This research provides a virtual machine (VM) placement and scheduling approach for effectively scheduling process tasks in the cloud environment while maintaining dependency and deadline constraints. The suggested model’s aim is to reduce the application’s energy consumption and total execution time while taking into account dependency and deadline limitations. To select the VM for the tasks and dynamically deploy/undeploy the VM on the hosts based on the jobs’ requirements, an energy-efficient VM placement (EVMP) algorithm is presented. Demonstrate that the proposed approach outperforms the existing PESVMC (power-efficient scheduling and VM consolidation) algorithm.

A thermal-aware energy-efficient virtual machine placement algorithm based on fuzzy controlled binary gravitational search algorithm (FC-BGSA)

A thermal-aware energy-efficient virtual machine placement algorithm based on fuzzy controlled binary gravitational search algorithm (FC-BGSA)

An effective energy-efficient virtual machine placement using clonal selection algorithm

An effective energy-efficient virtual machine placement using clonal selection algorithm

Energy-Efficient Virtual Machine Placement in Data Centers Via Accelerated Genetic Algorithm with Improved Fitness Computation

Energy-Efficient Virtual Machine Placement in Data Centers Via Accelerated Genetic Algorithm with Improved Fitness Computation

Energy Efficient Virtual Machine Placement With an Improved Ant Colony Optimization Over Data Center Networks

Virtualization has achieved widespread adoption in various fields. As one of the most significant technologies in network virtualization, an effective virtual machine placement can facilitate to improve resource utilization in data center networks and cut down the enterprises' operation cost. In this paper, we proposed an energy efficient virtual machine placement scheme, which pursued to reduce communication cost and power consumption over traffic-aware data center networks. To solve such an optimization problem, an improved ant colony optimization with adaptive parameter setting was presented to balance its fast convergence and robust search capability. Compared with existing algorithms, simulation results demonstrated that our scheme achieved improvements in power consumption and communication cost, and had a significant reduction in run time under different traffic patterns and configurations.

An Energy-efficient Virtual Machine Placement Algorithm based Service Level Agreement in Cloud Computing Environments

The cloud computing has given services to the users throughout the world during recent years. The cloud computing services have been founded according to ‘As-Pay-You-Go’ model and some leading enterprises give these services. The giving these cloud-computing services has been developed every day and these requirements necessitate for more infrastructures and Internet providers (IPs). The nodes of data centers consume a lot of energy in cloud structure and disseminate noticeable amount of carbon dioxide into the environment. We define a framework and structure for cloud environment of efficient energy in the present paper. We examine the present problems and challenges based on this structure and then present and model management algorithms and source allocation in cloud computing environment in order to manage energy in addition to considering Service Level Agreement. The proposed algorithm has been implemented by cloudsim simulator where the obtained results from simulation of real-time data indicate that the proposed method is superior to previous techniques in terms of energy consumption and observance of Service Level Agreement. Similarly, number of live migration of virtual machines and quantity of transferred data has been improved.

An energy-efficient virtual machine placement and route scheduling scheme in data center networks

The increasing requirements of big data analytics and complex scientific computing impose significant burdens on cloud data centers. As a result, not only the computation but also the communication expenses in data centers are greatly increased. Previous work on green computing in data centers mainly focused on the energy consumption of the servers rather than the communication. However, for those emerging applications with big data-flows transmission, more energy consumption could be consumed by communication links, switching and aggregation elements. To this end, based on data-flows’ transmission characteristics, we proposes a novel Job-Aware Virtual Machine Placement and Route Scheduling (JAVPRS) scheme to reduce the energy consumption of data center networks (DCN) while still meeting as many network QoS (Quality of Service) requirements as possible. Our proposed scheme focuses on not just migrating large data flows, but also integrating small data flows to improve the utilization rate of the communication links. With more idle switches turned off, DCN’s energy consumption will thus be reduced. Besides the data flows’ migration and integration, the Traffic Engineering (TE) technique is also applied to decrease the transmission delay and increase the network throughput. To evaluate the performance of our proposed scheme, a number of simulation studies are performed. Compared to the selected benchmarks, the simulation results showed that JAVPRS can achieve 22.28%–35.72% energy saving while reducing communication delay by 5.8%–6.8% and improving network throughput by 13.3%.

Optimized Energy Efficient Virtual Machine Placement Algorithm and Techniques for Cloud Data Centers

Cloud computing is an internet based computing technology that provide on demand computing for end users. Normally, data centers allocation for application on statically based. But today so many data centers have a problem how to reduce energy consumption? Due to increase use of cloud services and infrastructure by various cloud providers, uses of energy day by day increase that's why energy consumption increase lots. Large numbers of data centers that consume lots of energy which increase the level of co2. Hence there is need for green computing and for energy efficient management of cloud data center resources besides meeting the QoS constraints. Within a data center, maximum energy is consumed by cooling systems and ICT Infrastructure, particularly, the servers. So it is of utmost importance to optimize energy utilization in datac enter servers. In order to achieve this, we can leverage the power of virtualization which is intrinsic in Cloud Computing. Virtualization opens the doors for VM consolidation by allowing dynamic migration of virtual machines across physical machines. In this study, the problem of VM consolidation has been formulated as a mathematical optimization problem.

A Critical Analysis of Energy Efficient Virtual Machine Placement Techniques and its Optimization in a Cloud Computing Environment

Infrastructure-as-a-service cloud computing providers with on-demand infrastructures have become a promising alternative to the rising cost of ownership for computing infrastructures in many enterprises. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology that allows cloud-scale datacenters to improve resource utilization and energy efficiency. This paper provides a critical analysis on the basis of literature review of the state-of-the-art research on energy efficient dynamic allocation of virtual machines to hosts in a datacenter as per variable workload demands of different application running on the virtual machines and literature review suggests that further optimization of the virtual machine placement can be done using live migration. So, this paper proposes a technique for optimizing virtual machine placement by live migration using dynamic threshold values ensuring a deadlock free resource allocation focusing on multidimensional resources. The goal is to improve the overall utilization of computing resources thus reducing the energy consumption of datacenter.

Energy Efficient Virtual Machine Placement Approach for Balanced Resource Utilization in Cloud Environment

Cloud is a business model that provides on demand computing resources to the clients on rental basis. It is an utility model, so clients need to pay only for that amount of resources which is actually used. Cloud provider want to gain more and more profit that can be possible only by effective resource utilization. For this purpose virtualization technology is used, which allows sharing of physical resources. In virtualization hypervisor provide requested resources to the client in the form of virtual machine. Virtualization increases the resource utilization by creating the number of VM in a single host, but wrong VM placement can increase resource leakage. Resource leakage can be defined as an amount of waste resources that can’t be used. Resources in the cloud are in multi dimensions i.e. CPU, RAM, bandwidth etc. So resource leakage is one of the major problems in the cloud environment. Balanced resource utilization in the cloud is more challenging task as compared to the traditional distributed computing paradigms, due to dynamic behavior of the VM resource requirement. Number of virtual machine placement algorithms has been proposed that run under cloud computing environment. Main goal of these algorithms are either to save energy by shutting down some severs or maximizing the resources utilization, but few of them considered both. This paper proposes a VM placement approach that increases the balanced resources utilization and reduced power consumed by the data centers. For the balanced resources utilization six clusters CMB, CBM, MBC, MCB, BMC and BCM are used, where C, M and B stands for CPU, memory and bandwidth respectively. Initially some host assigned to each cluster according to the remaining capacity. CBM cluster keep the host where remaining CPU > remaining bandwidth > remaining RAM. Host in the CBM cluster can run only that VM, which require more CPU and minimum RAM. This approach is motivated by the concept that VM is assigned to PM such that, resource requirement of the VM are complement with the resource utilization of the PM. CloudSim simulator is used to implement this approach. Experimental result shows that this approach increased the resource utilization, reduced the resource leakage and reduced the energy consumption by minimizing the number of active server.

Energy efficient virtual machine placement algorithm with balanced resource utilization based on priority of resources

The increasing energy consumption has become a major concern in cloud computing due to its cost and environmental damage. Virtual Machine placement algorithms have been proven to be very effective in increasing energy efficiency and thus reducing the costs. In this paper we have introduced a new priority routing VM placement algorithm and have compared it with PABFD (power-aware best fit decreasing) on CoMon dataset using CloudSim for simulation. Our experiments show the superiority of our new method with regards to energy consumption and level of SLA violations measures and prove that priority routing VM placement algorithm can be effectively utilized to increase energy efficiency in the clouds.

A Hybrid Genetic Algorithm for the Energy-Efficient Virtual Machine Placement Problem in Data Centers

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation technology. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches consider the energy consumption by physical machines only, but do not consider the energy consumption in communication network, in a data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement. In our preliminary research, we have proposed a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both physical machines and the communication network in a data center. Aiming at improving the performance and efficiency of the genetic algorithm, this paper presents a hybrid genetic algorithm for the energy-efficient virtual machine placement problem. Experimental results show that the hybrid genetic algorithm significantly outperforms the original genetic algorithm, and that the hybrid genetic algorithm is scalable.

Power consumption estimation of CPU and peripheral components in virtual machines

Energy consumption of IT increased continuously during the last decades. Numerous works have been accomplished for improving energy efficiency of hardware whereas software energy efficiency has been ignored for a long time. This contribution presents a novel approach for estimating energy consumption of computer systems in dependency of software-caused workloads in different execution environments. The system is the basis for automatic optimization of software execution in an energy-efficient way by finding the best-suiting host computer (and best-suiting peripheral devices). Thus, it opens novel ways to further improve energy-efficiency of IT systems by migrating software-caused load to an energy-efficient target. Exemplary, the approach is tested in a virtualized data center environment, where virtual machines are the applications. The presented approach is a vehicle for automatically computing an energy-efficient virtual machine placement. The paper presents a new algorithm for estimating virtual machine power consumption, which consists of CPU power consumption estimation as well as power usage estimation of peripheral components like hard disk drive and network interface controller. The accuracy of the presented approach is proved by means of measurements.