scholarly journals MFF: Performance Interference-Aware VM Placement Algorithm for Reducing Energy Consumption in Data Centers

2020 ◽  
Vol 3 (1) ◽  
pp. 1-10
Author(s):  
Derdus Mosoti ◽  
Vincent O. Omwenga ◽  
Patrick Ogao
Keyword(s):  
Energy Consumption ◽  
Data Centers ◽  
Vm Placement ◽  
Placement Algorithm ◽  
Reducing Energy

Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm

2021 ◽  
pp. 773-789
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Vm Placement ◽  
Energy Consumption Reduction ◽  
Consumption Reduction ◽  
The World ◽  
Placement Algorithm

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.

scholarly journals CPU-Memory Aware VM Consolidation for Cloud Data Centers

2020 ◽  
Vol 21 (2) ◽  
pp. 159-172
Author(s):  
Nithiya Baskaran ◽  
Eswari R
Keyword(s):  
Energy Consumption ◽  
Data Centers ◽  
Vm Placement ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Placement Algorithm ◽  
Violation Rate ◽  
Vm Consolidation ◽  
Memory Utilization ◽  
Better Than

The unbalanced usage of resources in cloud data centers cause an enormous amount of power consumption. The Virtual Machine (VM) consolidation shuts the underutilized hosts and makes the overloaded hosts as normally loaded hosts by selecting appropriate VMs from the hosts and migrates them to other hosts in such a way to reduce the energy consumption and to improve physical resource utilization. Efficient method is needed for VM selection and destination hosts selection (VM placement). In this paper, a CPU-Memory aware VM placement algorithm is proposed for selecting suitable destination host for migration. The VMs are selected using Fuzzy Soft Set (FSS) method VM selection algorithm. The proposed placement algorithm considers both CPU, Memory, and combination of CPU-Memory utilization of VMs on the source host. The proposed method is experimentally compared with several existing selection and placement algorithms and the results show that the proposed consolidation method performs better than existing algorithms in terms of energy efficiency, energy consumption, SLA violation rate, and number of VM migrations.

Energy and SLA Efficient Virtual Machine Placement in Cloud Environment Using Non-Dominated Sorting Genetic Algorithm

2019 ◽  
Vol 13 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Vm Placement ◽  
Energy Consumption Reduction ◽  
Sla Violation ◽  
Consumption Reduction ◽  
Placement Algorithm

To increase the availability of the resources and simultaneously to reduce the energy consumption of data centers by providing a good level of the service are one of the major challenges in the cloud environment. With the increasing data centers and their size around the world, the focus of the current research is to save the consumption of energy inside data centers. Thus, this article presents an energy-efficient VM placement algorithm for the mapping of virtual machines over physical machines. The idea of the mapping of virtual machines over physical machines is to lessen the count of physical machines used inside the data center. In the proposed algorithm, the problem of VM placement is formulated using a non-dominated sorting genetic algorithm based multi-objective optimization. The objectives are: optimization of the energy consumption, reduction of the level of SLA violation and the minimization of the migration count.

scholarly journals An Distributed Virtual Machine Placement Algorithm for Balanced Resource Utilization and Low Energy Consumption

2018 ◽  
Vol 173 ◽  
pp. 03092
Author(s):  
Bo Li ◽  
Yun Wang
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data ◽  
Low Energy Consumption ◽  
Cloud Data Centers ◽  
Placement Algorithm ◽  
Heterogeneous Cloud

Virtual machine placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Traditional load balancing or energy-aware VM placement approaches either allocate VMs to PMs in centralized manner or ignore PM’s cost-capacity ratio to implement energy-aware VM placement. We address these two issues by introducing a distributed VM placement approach. A auction-based VM placement algorithm is devised for help VM to find the most suitable server in large heterogeneous cloud data centers. Our algorithm is evaluated by simulation. Experimental results show two major improvements over the existing approaches for VM placement. First, our algorithm efficiently balances the utilization of multiple types of resource by minimizing the amount of physical servers used. Second, it reduces system cost compared with existing approaches in heterogeneous environment.

scholarly journals Virtual Machine Placement Algorithm for Both Energy-Awareness and SLA Violation Reduction in Cloud Data Centers

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zhou Zhou ◽  
Zhigang Hu ◽  
Keqin Li
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
High Energy ◽  
Energy Awareness ◽  
Cloud Data ◽  
Light Load ◽  
Sla Violation ◽  
Cloud Data Centers ◽  
Placement Algorithm

The problem of high energy consumption is becoming more and more serious due to the construction of large-scale cloud data centers. In order to reduce the energy consumption and SLA violation, a new virtual machine (VM) placement algorithm named ATEA (adaptive three-threshold energy-aware algorithm), which takes good use of the historical data from resource usage by VMs, is presented. In ATEA, according to the load handled, data center hosts are divided into four classes: hosts with little load, hosts with light load, hosts with moderate load, and hosts with heavy load. ATEA migrates VMs on heavily loaded or little-loaded hosts to lightly loaded hosts, while the VMs on lightly loaded and moderately loaded hosts remain unchanged. Then, on the basis of ATEA, two kinds of adaptive three-threshold algorithm and three kinds of VMs selection policies are proposed. Finally, we verify the effectiveness of the proposed algorithms by CloudSim toolkit utilizing real-world workload. The experimental results show that the proposed algorithms efficiently reduce energy consumption and SLA violation.

An energy-efficient VM placement method for cloud data centers using a hybrid genetic algorithm

2018 ◽  
Vol 20 (4) ◽  
pp. 430-445 ◽  
Author(s):  
Mohamed Amine Kaaouache ◽  
Sadok Bouamama
Keyword(s):  
Genetic Algorithm ◽  
Communication Network ◽  
Energy Consumption ◽  
Data Centers ◽  
Hybrid Genetic Algorithm ◽  
Vm Placement ◽  
Content Type ◽  
Cloud Data ◽  
Placement Method ◽  
Cloud Data Centers

Purpose This purpose of this paper is to propose a novel hybrid genetic algorithm based on a virtual machine (VM) placement method to improve energy efficiency in cloud data centers. How to place VMs on physical machines (PMs) to improve resource utilization and reduce energy consumption is one of the major concerns for cloud providers. Over the past few years, many approaches for VM placement (VMP) have been proposed; however, existing VM placement approaches only consider energy consumption by PMs, and do not consider the energy consumption of the communication network of a data center. Design/methodology/approach This paper attempts to solve the energy consumption problem using a VM placement method in cloud data centers. Our approach uses a repairing procedure based on a best-fit decreasing heuristic to resolve violations caused by infeasible solutions that exceed the capacity of the resources during the evolution process. Findings In addition, by reducing the energy consumption time with the proposed technique, the number of VM migrations was reduced compared with existing techniques. Moreover, the communication network caused less service level agreement violations (SLAV). Originality/value The proposed algorithm aims to minimize energy consumption in both PMs and communication networks of data centers. Our hybrid genetic algorithm is scalable because the computation time increases nearly linearly when the number of VMs increases.

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