The Power Consumption Model of a Server to Perform Data Access Application Processes in Virtual Machine Environments

2020 ◽  
pp. 184-192 ◽  
Author(s):  
Tomoya Enokido ◽  
Makoto Takizawa
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Access ◽  
Consumption Model

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.

scholarly journals CoPoMo: a context‐aware power consumption model for LTE user equipment

2013 ◽  
Vol 24 (6) ◽  
pp. 615-632 ◽  
Author(s):  
Bjoern Dusza ◽  
Christoph Ide ◽  
Liang Cheng ◽  
Christian Wietfeld
Keyword(s):  
Power Consumption ◽  
User Equipment ◽  
Context Aware ◽  
Consumption Model

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 Energy Modeling and Experimental Validation of Four-Wheel Mecanum Mobile Robots for Energy-Optimal Motion Control

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1372
Author(s):  
Liang Zhang ◽  
Jongwon Kim ◽  
Jie Sun
Keyword(s):  
Power Consumption ◽  
Mobile Robots ◽  
Motion Control ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Control Method ◽  
Energy Modeling ◽  
Optimal Motion ◽  
Consumption Model ◽  
Mecanum Wheel

Four-wheel Mecanum mobile robots (FWMRs) are widely used in transportation because of their omnidirectional mobility. However, the FWMR trades off energy efficiency for flexibility. To efficiently predict the energy consumption of the robot movement processes, this paper proposes a power consumption model for the omnidirectional movement of an FWMR. A power consumption model is of great significance for reducing the power consumption, motion control, and path planning of robots. However, FWMRs are highly maneuverable, meaning their control is complicated and their energy modeling is extremely complex. The speed, distance, path, and power consumption of the robot can vary greatly depending on the control method. This energy model was mathematically implemented in MATLAB and validated by our laboratory’s Mecanum wheel robot. The prediction accuracy of the model was over 95% through simulation and experimental verification.

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