Energy-Efficient Virtual Machines Placement

Procedia Computer Science, 2016

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.

Communications in Computer and Information Science, 2016

Cloud providers offer computing services based on user demands using the Infrastructure as a Service (IaaS) service model. In a cloud data center, it is possible that multiple Virtual Machines (VMs) run on a Physical Machine (PM) using virtualization technology. Virtual Machine Placement (VMP) problem is the mapping of virtual machines across multiple physical ones. This process plays a vital role in defining energy consumption and resource usage efficiency in the cloud data center infrastructure. However, providing an efficient solution is not trivial due to difficulties such as machine heterogeneity, multi-dimensional resources, and large scale cloud data centers. In this paper, we propose an efficient heuristic algorithm that focuses on power consumption and resource wastage optimization to solve the aforementioned problem. The proposed algorithm, called MinPR, minimizes the total power consumption by reducing the number of active physical machines and prioritizing the power-efficient ones. Also, it reduces resource wastage by maximizing and balancing resource utilization among physical machines. To achieve these goals, we propose a new Resource Usage Factor model that manages virtual machine placement on physical machines using reward and penalty mechanisms. Simulations based on cloud user-customized VMs and Amazon EC2 Instances workloads illustrate that the proposed algorithm outperforms existing approaches. In particular, the proposed algorithm reduces total energy consumption by up to 15% for cloud user-customized VMs and by up to 10% for Amazon EC2 Instances.

International Journal of Cloud-Computing and Super-Computing, 2014

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. This paper presents a self-organizing and multi-objective approach for autonomic consolidation of VMs. The proposed approach does the initial placement of VMs in appropriate PM of cloud data centre which addresses different issues altogether such as maximum resource requirement during setup of VMs, future demand of free resources at peak load, improving the performance and energy saving by keeping idle PMs at offline state. The performance of the proposed algorithm is evaluated by simulating a data center with randomly generated resource capacities of PMs and resource requirement of VMs. Experiment results of proposed technique are also compared with standard algorithms of VM consolidation such as first-first, next-fit and random-selection on two dimensions of resources-CPU and RAM.

Neural Computing and Applications

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. ...

— Cloud computing is one of the most emerging technology of the world. Due to rapid increase in the number of data centers to provide the services to the users the power consumed and operational costs of them is going on increasing day by day. Due to high power consumption and continuous working the amount of CO2 emission is going on increasing which is adding up to the greenhouse effect. So there is a need to create an energy efficient system where the power consumption is less. Virtualization can be used to reduce the power consumption by data center. VM placement deals with the optimal choice of physical machine where it should be placed. Moreover the main aim for making it energy efficient is to transfer the load of fewer servers and switch off the idle servers. So in this paper a summary of various VM placement algorithms are discussed and energy conservation parameters are also discussed.

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.

2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security, 2014 IEEE 11th Intl Conf on Embedded Software and Syst (HPCC,CSS,ICESS), 2014

Employing cloud computing to acquire the benefit of cloud by optimizing various parameters that meet changing demands is a challenging task. The optimal mapping of tasks to virtual machines (VMs) and VMs to physical machines (PMs) (known as VM placement) problem are necessary for advancing energy consumption and resource utilization. High heterogeneity of tasks as well as resources, great dynamism and virtualization make the consolidation issue more complicated in the cloud computing system. In this paper, a complete mapping (i.e., task VM and VM to PM) algorithm is proposed. The tasks are classified according to their resource requirement and then searching for the appropriate VM and again searching for the appropriate PM where the selected VM can be deployed. The proposed algorithm reduces the energy consumption by depreciating the number of active PMs, while also minimizes the makespan and task rejection rate. We have evaluated our proposed approach in CloudSim simulator, and the results demonstrate the effectiveness of the proposed algorithm over some existing standard algorithms.

Proceedings of the Companion Conference on Genetic and Evolutionary Computation

Green cloud computing, an emerging computing paradigm in IT industry, is about providing efficient utilization of cloud's resources with minimal impact on the environment. The virtual machine placement has a considerable impact on a data center's energy and resource utilisation. The assignment of Virtual Machines (VMs) to Physical Machines (PMs) is an NP-hard problem to be solved using approximate algorithms at a reasonable computing time. In this paper, an energy-aware based algorithm is proposed for virtual machine placement. Our aim is to reduce the energy consumption and CO2 emission under a set of constraints. The performance and the efficiency of the proposed approach is tested on a cloud computing simulation toolkit.

2021

Cloud computing can be defined as an evolving Computing technology using the Internet and essential remote infrastructure to retain on-demand and pay-as-you-go assets. The number of data centers across the world has increased through wide adaptation to cloud principles, leading to large amounts of data center power consumption that affects the climate and economic aspects. So many virtual machines (VMs) could be installed on one Physical Machine(PM) via virtualization. The Cloud workload is held by these VMs and executed. Effective PM allocation of VMs will lead to better use of resources and energy savings. In this paper, our goal is to provide an improved Policy on energy-efficient VM placement to minimize energy consumption in the cloud environment, placing the VMs in the addressed bin backing mechanism and retaining the Service Level Agreement (SLA) between the provider and the cloud customer. Significant reduction in power consumption could even be made if convective are implemented at software level. Energy-aware scheduling processes produces excellent performance by implementing bin-packing energy efficiency mechanisms. An improved algorithm has been enhanced for the two First Fit Decreasing FFB and Best Fit Decreasing BFD algorithms, which are considered the best among bin packing algorithms. This algorithm adopts server power as the basis for arranging servers in the database, unlike the BFD, FFD algorithms that arrange servers according to CPU. The proposed algorithm has been practically tried using Matlab 2020 programing language for samples of servers and virtual machines, whose specifications were chosen randomly, and the results showed great efficiency of the proposed algorithm in reducing energy consumption.

Computer Science and Information Systems

Dynamic Virtual Machine (VM) consolidation is a successful approach to improve the energy efficiency and the resource utilization in cloud environments. Consequently, optimizing the online energy-performance tradeoff directly influences quality of service. In this study, algorithms named as CPU Priority based Best-Fit Decreasing (CPBFD) and Dynamic CPU Priority based Best-Fit Decreasing (DCPBFD) are proposed for VM placement. A number of VM placement algorithms are implemented and compared with the proposed algorithms. The algorithms are evaluated through simulations with real-world workload traces and it is shown that the proposed algorithms outperform the known algorithms. The simulation results clearly show that CPBFD and DCPBFD provide the least service level agreement violations, least VM migrations, and efficient energy consumption.