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

2015 ◽  
Vol 4 (2) ◽  
pp. 107-118
Author(s):  
Amin Rahimi ◽  
Leili Mohammad Khanli ◽  
Saeid Pashazadeh
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Energy Efficient ◽  
Virtual Machine Placement ◽  
Environmental Damage ◽  
Vm Placement ◽  
Increase Energy Efficiency ◽  
Placement Algorithm ◽  
Best Fit

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.

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.

A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing

2017 ◽  
Vol 8 (2) ◽  
pp. 20-36
Author(s):  
Yu Cai
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Virtual Machines ◽  
Live Migration ◽  
Vm Placement ◽  
Server Cluster ◽  
Simulated Environment ◽  
Resource Requirements ◽  
Placement Algorithm

Energy efficient virtual machines (VM) management and distribution on cloud platforms is an important research subject. Mapping VMs into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It should also take into accounts of VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. In this article, the authors propose an energy efficient statistical live VM placement scheme in a heterogeneous server cluster. Their scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption and is ready to be extended with more considerations on user demands. The authors conducted extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of the proposed scheme.

scholarly journals Balanced and Energy Efficient Multi Core-Aware Virtual Machine Placement Algorithm Using Multi-Dimensional Resource Space Partition Model

2021 ◽  
Author(s):  
Nagadevi ◽  
Kasmir Raja
Keyword(s):  
Resource Allocation ◽  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Energy Efficient ◽  
Virtual Machine Placement ◽  
Partition Model ◽  
Space Partition ◽  
Resource Space ◽  
Placement Algorithm

Optimal resource management is required in a data center to allocate the resources to users in a balanced manner. Balanced resource allocation is one of the key challenges in the data center. The multi-dimensional resources of a data center must be allocated in a balanced manner in all the dimensions of physical machines. The unbalanced resource allocation leads to unused residual resource fragments. The unused residual resource fragments leads to resource wastage. If the multi-dimensional data center resources are allocated in a balanced manner, the resource wastage does not occur. Also, the balanced allocation improves the power consumption. The balanced resource allocation reduces the resource wastage as well as reduces the power consumption. In this paper, we have designed a Balanced Energy Efficient Multi-Core Aware Virtual Machine Placement algorithm (MCA-BEE-VMP) using multi-dimensional resource space partition model to balance the resources like CPU and memory and also to reduce the power consumption. We used Google Cloud Jobs (GoCJ) dataset for the simulation. In our simulation of MCA-BEE-VMP using Cloud Sim simulation tool we have achieved balanced CPU and memory resources allocation in two dimensions of a physical machine. The resource wastage and power consumption is improved and the simulation results were analyzed.

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