Power and Performance Based Genetic Ant Colony Algorithm for Virtual Machine Placement

2020 ◽  
Vol 17 (1) ◽  
pp. 32-36
Author(s):  
Sushmitha ◽  
G. M. Karthik ◽  
M. Sayeekumar
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Ant Colony Algorithm ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
The Internet ◽  
Computing Services ◽  
Consumption Model ◽  
And Performance

Cloud Computing is the provisioning of computing services over the Internet. A Virtual Machine (VM) creation request has to be processed in any one data center of the physical machines. Virtual Machine Placement refers to choosing appropriate host for the VM. One of the major concerns in datacenter management is reducing the power consumption and performance filth of virtual machines. For solving the problem, GACO algorithm is proposed which uses PpW, IPR and LDR as heuristic information for ACO algorithm and for selection in Genetic algorithm. It also uses a non-linear power consumption model for quantifying power. The performance evaluation shows the efficiency of the algorithm.

Data Center Enhancement by Server Resources Utilization

2021 ◽  
Vol 39 (1B) ◽  
pp. 203-208
Author(s):  
Haider A. Ghanem ◽  
Rana F. Ghani ◽  
Maha J. Abbas
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
The Internet ◽  
Memory Resources

Data centers are the main nerve of the Internet because of its hosting, storage, cloud computing and other services. All these services require a lot of work and resources, such as energy and cooling. The main problem is how to improve the work of data centers through increased resource utilization by using virtual host simulations and exploiting all server resources. In this paper, we have considered memory resources, where Virtual machines were distributed to hosts after comparing the virtual machines with the host from where the memory and putting the virtual machine on the appropriate host, this will reduce the host machines in the data centers and this will improve the performance of the data centers, in terms of power consumption and the number of servers used and cost.

Virtual Machine Placement in IaaS Cloud

2017 ◽  
pp. 130-158
Author(s):  
Prateek Khandelwal ◽  
Gaurav Somani
Keyword(s):  
Customer Satisfaction ◽  
Virtual Machine ◽  
Data Center ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Attractive Alternative ◽  
Operational Cost ◽  
Trade Offs ◽  
Crucial Component

A crucial component of providing services over virtual machines to users is how the provider places those virtual machines on physical servers. While one strategy can offer an increased performance for the virtual machine, and hence customer satisfaction, another can offer increased savings for the cloud operator. Both have their trade-offs. Also, with increasing costs of electricity, and given the fact that the major component of the operational cost of a data center is that of powering it, green strategies also offer an attractive alternative. In this chapter, the authors will look into what kind of different placement strategies have been developed, and the kind of advantages they purport to offer.

scholarly journals Optimal Virtual Machine Placement Based on Grey Wolf Optimization

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 283 ◽  
Author(s):  
Ammar Al-Moalmi ◽  
Juan Luo ◽  
Ahmad Salah ◽  
Kenli Li
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Combinatorial Problems ◽  
Virtual Machine Placement ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Substantial Impact ◽  
Computational Intelligence Technique ◽  
Memory Resources

Virtual machine placement (VMP) optimization is a crucial task in the field of cloud computing. VMP optimization has a substantial impact on the energy efficiency of data centers, as it reduces the number of active physical servers, thereby reducing the power consumption. In this paper, a computational intelligence technique is applied to address the problem of VMP optimization. The problem is formulated as a minimization problem in which the objective is to reduce the number of active hosts and the power consumption. Based on the promising performance of the grey wolf optimization (GWO) technique for combinatorial problems, GWO-VMP is proposed. We propose transforming the VMP optimization problem into binary and discrete problems via two algorithms. The proposed method effectively minimizes the number of active servers that are used to host the virtual machines (VMs). We evaluated the proposed method on various VM sizes in the CloudSIM environment of homogeneous and heterogeneous servers. The experimental results demonstrate the efficiency of the proposed method in reducing energy consumption and the more efficient use of CPU and memory resources.

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.

scholarly journals Optimized Virtual Machine Placement with Traffic-Aware Balancing in Data Center Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Chen ◽  
Xiaofeng Gao ◽  
Guihai Chen
Keyword(s):  
Virtual Machine ◽  
Heuristic Algorithm ◽  
Data Center ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Np Hard ◽  
Data Center Networks ◽  
Placement Problem ◽  
Placement Algorithm ◽  
Communication Locality

Virtualization has been an efficient method to fully utilize computing resources such as servers. The way of placing virtual machines (VMs) among a large pool of servers greatly affects the performance of data center networks (DCNs). As network resources have become a main bottleneck of the performance of DCNs, we concentrate on VM placement with Traffic-Aware Balancing to evenly utilize the links in DCNs. In this paper, we first proposed a Virtual Machine Placement Problem with Traffic-Aware Balancing (VMPPTB) and then proved it to be NP-hard and designed a Longest Processing Time Based Placement algorithm (LPTBP algorithm) to solve it. To take advantage of the communication locality, we proposed Locality-Aware Virtual Machine Placement Problem with Traffic-Aware Balancing (LVMPPTB), which is a multiobjective optimization problem of simultaneously minimizing the maximum number of VM partitions of requests and minimizing the maximum bandwidth occupancy on uplinks of Top of Rack (ToR) switches. We also proved it to be NP-hard and designed a heuristic algorithm (Least-Load First Based Placement algorithm, LLBP algorithm) to solve it. Through extensive simulations, the proposed heuristic algorithm is proven to significantly balance the bandwidth occupancy on uplinks of ToR switches, while keeping the number of VM partitions of each request small enough.

Power Consumption-Aware Virtual Machine Placement in Cloud Data Center

2017 ◽  
Vol 1 (4) ◽  
pp. 541-550 ◽  
Author(s):  
Giuseppe Portaluri ◽  
Davide Adami ◽  
Andrea Gabbrielli ◽  
Stefano Giordano ◽  
Michele Pagano
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Virtual Machine Placement ◽  
Cloud Data Center ◽  
Cloud Data

Dynamic Virtual Machine Placement in Cloud Computing

2017 ◽  
pp. 136-167 ◽  
Author(s):  
Arnab Kumar Paul ◽  
Bibhudatta Sahoo
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Optimal Placement ◽  
On Demand ◽  
Energy Consumption Model ◽  
Consumption Model

The aim of cloud computing is to enable users to access resources on demand. The number of users is continuously increasing. In order to fulfil their needs, we need more number of physical machines and data centers. The increase in the number of physical machines is directly proportional to the consumption of energy. This gives us one of the major challenges; minimization of energy consumption. One of the most effective ways to minimize the consumption of energy is the optimal virtual machine placement on physical machines. This chapter focuses on finding the solution to the problem of dynamic virtual machine placement for the optimized consumption of energy. An energy consumption model is built which takes into account the states of physical machines and live migration of virtual machines. On top of this, the cloud computing model is built. Unlike centralized approaches towards virtual machine placement which result in many unreachable solutions, a decentralized approach is used in this chapter which provides a list of virtual machine migrations for their optimal placement.

Virtual Machine Placement Strategy for Cloud Data Center

2015 ◽  
pp. 261-287
Author(s):  
Sourav Kanti Addya ◽  
Bibhudutta Sahoo ◽  
Ashok Kumar Turuk
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Bin Packing ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Placement Problem ◽  
Cloud Data ◽  
Minimum Power Consumption

The data center is the physical infrastructure layer in cloud architecture. To run a large data center requires a huge amount of power. A proper strategy can minimize the number of servers used. Minimization of active servers caused minimization of power consumption. But the maximum number of virtual machine placement will be a monetary benefit for cloud service providers. To earn maximum revenue, the CSP is to maximize resource utilization. VM placement is one of the major issues to achieve minimum power consumption as well as to earn maximum revenue by CSP. In this research chapter, we have formulated an optimization problem for initial VM placement in the data center. An iterative heuristic using simulated annealing has been used for VM placement problem. The proposed heuristic has been analysis to be scalable and the coding scheme shows that the proposed technique is outperforming traditional FFD on bin packing technique.

Virtual Machine Placement Strategy for Cloud Data Center

2016 ◽  
pp. 783-808
Author(s):  
Sourav Kanti Addya ◽  
Bibhudatta Sahoo ◽  
Ashok Kumar Turuk
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Bin Packing ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Placement Problem ◽  
Cloud Data ◽  
Minimum Power Consumption

The data center is the physical infrastructure layer in cloud architecture. To run a large data center requires a huge amount of power. A proper strategy can minimize the number of servers used. Minimization of active servers caused minimization of power consumption. But the maximum number of virtual machine placement will be a monetary benefit for cloud service providers. To earn maximum revenue, the CSP is to maximize resource utilization. VM placement is one of the major issues to achieve minimum power consumption as well as to earn maximum revenue by CSP. In this research chapter, we have formulated an optimization problem for initial VM placement in the data center. An iterative heuristic using simulated annealing has been used for VM placement problem. The proposed heuristic has been analysis to be scalable and the coding scheme shows that the proposed technique is outperforming traditional FFD on bin packing technique.

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