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.

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.

Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment

2017 ◽  
Vol 13 (1) ◽  
pp. 45-57 ◽  
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Vm Placement ◽  
Consolidation Process ◽  
Physical Machine ◽  
Overall Performance ◽  
Vm Consolidation ◽  
New Algorithms

In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.

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.

scholarly journals Power Management as a Service (Pmaas) – An Energy Saving Service by ―Auto-Fit VM Placement‖ Algorithm in Cloud Computing for Maximum Resource Utilization at Minimum Energy Consumption Without Live-Migrating the Virtual Machines by using the Concept of Virtual-Servers

2019 ◽  
Vol 9 (2) ◽  
pp. 1437-1446
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Power Management ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Memory Storage ◽  
Vm Placement ◽  
Placement Algorithm

Now a day Energy Consumption is one of the most promising fields amongst several computing services of cloud computing. A maximum amount of Power resources are absorbed by the data centre because of huge amount of data processing which is increased abnormally. So it’s the time to think about the energy consumption in cloud environment. Existing Energy Consumption systems are limited in terms of virtualization because improper virtualization leads to loads imbalance and excessive power consumption and inefficiency in terms of computational power. Billing[1,2 ] is another exciting feature that is closely related to energy consumption, because higher or lesser billing depends on energy consumption somehow-as we know that cloud providers allow cloud users to access resources as pay-per-use, so these resources need to be optimally selected to process the user request to maximize user satisfaction in the distributed virtualized environment. There may be an inequity between the actual power consumption by the users and the provided billing records by the providers, So any false accusation that may claimed by each other to get illegal compensations. To avoid such accusation, we propose a work to consolidate the VMs using the Power Management as a Service (PMaaS) model in such a way, to reduce power consumption by maximum resource utilization without live-migration of the virtual machines by using the concept of Virtual Servers. The proposed PMaaS model uses a new “Auto-fit VM placement algorithm”, which computes tasks resource demands, models a Virtual Machine that fits those demands, and places the Virtual Machines on a Virtual server made by the collective resources (CPU, Memory, Storage and Bandwidth) from the respective schedulers directly connected to the actual physical servers and that has the minimum remaining resources which is large enough to accommodate such a Virtual Machine.

scholarly journals Task Based Virtual Machine Placement in Cloud

2020 ◽  
Vol 9 (5) ◽  
pp. 1994-1998
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Test System ◽  
Vm Placement ◽  
Cloud Architecture ◽  
Use Of Resources ◽  
Placement Algorithm

Cloud computing offers many advantages by optimizing various parameters to meet the complex requirements .Some of the problems of cloud computing are utilization of resources and less energy consumption. More research and resources heterogeneity complicates the consolidation problem inside cloud architecture. VM placement refers to an ideal mapping of a task to virtual machines (VM) and virtual machines to physical machines (PM). The task-based VM placement algorithm is introduced in this research work. Here tasks are divided in accordance with their requirements, and then search for appropriate VM, again searching for appropriate PM, where selected VM could be sent. The algorithm decreases the use of resources by devaluation of the number of dynamic PMs while further decreases the rate of dismissal of make span and assignment. CloudSim test System is used to evaluate our algorithm in this research work. The outcomes of this implementation show the effectiveness of some current algorithms such as Round robin and Shortest Job First (SJF) algorithms.

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.

scholarly journals VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

2019 ◽  
Vol 8 (2) ◽  
pp. 3444-3449
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Environment ◽  
Total Energy Consumption ◽  
Vm Migration ◽  
Allocation Policy ◽  
Sla Violation ◽  
Violation Rate

Cloud computing, a metered based technology provides the services using virtualized technology over the internet. In the cloud environment, to improve the performance (such as utilization of the resources, energy minimization) extreme number of virtual machines (VMs) can be installed on the servers as per their resource capacity. In this way, servers can be overloaded. Overloaded servers consume more energythan normal status servers. VM migration (VMM) is an efficient technique to become a server in a normal state. VMM technique is used to consolidate the resources to increase resource utilization (RU) and reduceenergy usage. In the VMM technique, selection of VM such as which VM is migrated from one server to another server and allocation of VM on servers is an important aspect. Appropriate VM selection declines the numeral of VMMs and increasesenergy efficiency. Appropriate VM allocation declines the server to become overloaded. In this paper, the VM selection and allocation strategy is presented. CloudSim toolkit is used to verify the strength of proposed VM selection and allocation algorithm. Proposed VM Selection algorithm (MaMT) performs better than existing MiMT algorithm in terms of total energy consumption, number of hosts shut down, number of VMM, and average Service Level Agreement (SLA) violation rate. MaMT algorithm with resource aware provisioning (RAP) and MiMT+RAP algorithm combines both VM selection and allocation policies. RAP algorithm used both energy and RU parameters while allocating VM to the server.MaMTreduces the energy consumption up to 7.25% and reduces the SLA violation rate up-to 2.6% in comparison to MiMT algorithm. When VM selection and allocation policies combines together than more system performance is improved. MaMT+RAPreduces the energy consumption up to6.76% and reduces the SLA violation rate up-to 0.22% in comparison to MaMT algorithm.MiMT+RAPreduces the energy consumption up to15.23% and reduces the SLA violation rate up-to 0.95% in comparison to MiMT algorithm.

New Deadline-Aware Energy-Consumption Optimization Model and Genetic Algorithm Under Cloud Computing

2016 ◽  
Vol 30 (03) ◽  
pp. 1659006 ◽  
Author(s):  
Hai Zhu ◽  
Hongfeng Wang
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Optimization Model ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Time Constraints ◽  
Primary Concern ◽  
Total Energy Consumption ◽  
Energy Consumption Optimization

With large-scale data centers widely deployed around the world, their huge energy consumption becomes a primary concern. Effective resource allocation and scheduling is one of the key to solve this problem. However, existing studies on this topic are relatively rare. In this paper, a new deadline-aware energy-consumption optimization model is designed, which optimizes both the idle and execution energy consumption of servers. To save the idle energy consumption, we propose a new virtual machine deployment algorithm for mapping virtual machines to a constrained packing problem with multidimensional variables. In the proposed genetic algorithm, in order to improve the diversity of the population, we select some of the individuals which do not satisfy time constraints but have low energy consumption into the next generation. To save the execution energy consumption, we adopt the technique of dynamic voltage and frequency scaling. Finally, experimental results show that compared with the existing algorithms, the proposed one greatly reduces the total energy consumption of data centers under the time constraints of tasks.

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