Energy-aware VM migration using dragonfly–crow optimization and support vector regression model in Cloud

2018 ◽  
Vol 09 (06) ◽  
pp. 1850050 ◽  
Author(s):  
Nitin S. More ◽  
Rajesh B. Ingle
Keyword(s):  
Energy Consumption ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Support Vector ◽  
Load Prediction ◽  
Energy Aware ◽  
Support Vector Regression Model ◽  
Migration Cost ◽  
And Migration

Nowadays, virtual machine migration (VMM) is a trending research since it helps in balancing the load of the Cloud effectively. Several VMM-based strategies defined in the literature have considered various metrics, such as load, energy, and migration cost for balancing the load of the model. This paper introduces a novel VMM strategy by considering the load of the Cloud network. Two important aspects of the proposed scheme are the load prediction through the support vector regression (SVR) and the optimal VM placement through the proposed dragonfly-based crow (D-Crow) optimization algorithm. The proposed D-Crow optimization algorithm is developed by incorporating crow search algorithm (CSA) into dragonfly algorithm (DA). Also, the proposed VMM strategy defines a load balancing model based on the energy consumption, load, and the migration cost to achieve the energy-aware VMM. The simulation of the proposed VMM strategy is done based on the metrics such as load, energy consumption, and the migration cost. From the results, it can be shown that the proposed VMM strategy surpassed other comparative models by achieving the minimum values of 7.3719%, 10.0368%, and 11.0639% for the load, energy consumption, and migration cost, respectively.

Optimizing the Topology and Energy-Aware VM Migration in Cloud Computing

2020 ◽  
Vol 11 (3) ◽  
pp. 42-65
Author(s):  
Nitin S. More ◽  
Rajesh B. Ingle
Keyword(s):  
Energy Consumption ◽  
Spanning Tree ◽  
Tree Topology ◽  
Superior Performance ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Energy Aware ◽  
Migration Cost ◽  
And Migration ◽  
The Cost

The advancements in virtual machine migration (VMM) have been trending due to its effective load balancing features in cloud infrastructure. Previously, data centers were used for handling VMs organized in racks. These racks are arranged in a spanning tree topology with a high bandwidth. Thus, the cost for moving the data between servers is highest when the racks are far from each other. This work addresses this issue and proposed VMM strategy based on self-adaptive D-Crow algorithm (S-DCrow) that incorporates adaptive constants in Dragonfly-based Crow (D-Crow) optimization algorithm based on the proposed topology model. The proposed S-DCrow describes a migrating model, which is based on topology, energy consumption, load, and migration cost. Here, the network is organized in a spanning tree topology and is adapted by proposed S-DCrow for optimal VMM. The performance of the proposed S-DCrow shows superior performance in terms of load, energy consumption, and migration cost with the values of 0.1417, 0.1009, and 0.1220, respectively.

scholarly journals Application of a Novel Fractional Order Grey Support Vector Regression Model to Forecast Wind Energy Consumption in China

2020 ◽  
pp. 58-69 ◽  
Author(s):  
Jiahao Cao ◽  
Liang Liu ◽  
Lizhi Yang ◽  
Shuchuan Xie
Keyword(s):  
Energy Consumption ◽  
Wind Energy ◽  
Prediction Model ◽  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Grey Prediction ◽  
Support Vector Regression Model ◽  
Related Data ◽  
New Energy

In order to achieve accurate prediction of new energy related data, a fractional grey support vector regression model based on nested cross-validation is proposed. In order to verify the superiority of the new model, China’s wind energy consumption data from 2001 to 2014 were selected, and a fractional grey prediction model, a support vector regression model and a fractional support vector regression combination model were established, and wind energy consumption in China was predicted from 2015 to 2018. Numerical experimental results show that the newly proposed combined prediction model has higher prediction accuracy.

scholarly journals Parameter Selection of Local Search Algorithm for Design Optimization of Automotive Rubber Bumper

2020 ◽  
Vol 10 (10) ◽  
pp. 3584
Author(s):  
Dávid Huri ◽  
Tamás Mankovits
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Support Vector Regression ◽  
Search Algorithm ◽  
Selection Process ◽  
Parameter Selection ◽  
Support Vector ◽  
Displacement Curve ◽  
Support Vector Regression Model ◽  
Selection Of

In rubber bumper design, the most important mechanical property of the product is the force–displacement curve under compression and its fulfillment requires an iterative design method. Design engineers can handle this task with the modification of the product shape, which can be solved with several optimization methods if the parameterization of the design process is determined. The numerical method is a good way to evaluate the working characteristics of the rubber product; furthermore, automation of the whole process is feasible with the use of Visual Basic for Application. An axisymmetric finite element model of a rubber bumper was built with the use of a calibrated two-term Mooney–Rivlin material model. A two-dimensional shape optimization problem was introduced where the objective function was determined as the difference between the initial and the optimum characteristics. Our goal was to integrate a surrogate model-based parameter selection of local search algorithms for the optimization process. As a metamodeling technique, cubic support vector regression was selected and seemed to be suitable to accurately predict the nonlinear objective function. The novel optimization procedure which applied the support vector regression model in the parameter selection process of the stochastic search algorithm proved to be an efficient method to find the global optimum of the investigated problem.

Stress evaluation in seven-wire strands based on singular value feature of ultrasonic guided waves

2021 ◽  
pp. 147592172110053
Author(s):  
Qian Ji ◽  
Li Jian-Bin ◽  
Liu Fan-Rui ◽  
Zhou Jian-Ting ◽  
Wang Xu
Keyword(s):  
Dispersion Curve ◽  
Support Vector Regression ◽  
Stress Level ◽  
Guided Waves ◽  
Guided Wave ◽  
Singular Value ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Various Boundary Conditions ◽  
Wave Methods

The seven-wire strands are the crucial components of prestressed structures, though their performance inevitably degrades with the passage of time. The ultrasonic guided wave methods have been intensely studied, owing to its tremendous potential for full-scale applications, among the existing nondestructive testing methods, for evaluating the stress status of strands. We have employed the theoretical and finite element methods to solve the dispersion curve of single wire and steel strands under various boundary conditions. Thereafter, the singular value decomposition was adopted to work with the simulated and experimental signals for extracting a feature vector that carries valuable stress status information. The effectiveness of the vector was verified by analyzing the relationship between the vector and the stress level. The vector was also used as an input to establish a support vector regression model. The accuracy of the model has been discussed for different sample sizes. The results show that the fundamental mode dispersion curve offset on the high-frequency part and cut-off frequency increases as the boundary constraints enhance. Simulated and experimental results have demonstrated the effectiveness and potential of the proposed support vector regression method for evaluating the stress level in the strands. This method performs well even at low stress levels and the reliability can be enhanced by adding more samples.

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