Cauchy Particle Swarm Optimization (CPSO) based Migrations of tasks in a virtual machine

2021 ◽  
Author(s):  
Vadivel R ◽  
SUDALAIMUTHU T
Keyword(s):  
Power Consumption ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Principal Component ◽  
Attribute Selection ◽  
Particle Swarm Algorithm ◽  
Method Performance ◽  
The Past ◽  
Virtual Resources ◽  
Migration Technique

Abstract Cloud computing technology helps to resolve the problem in storage management by providing virtual resources to the end users. But, the overloading of virtual machines results in degradation of performances as well as it increases in the energy consumption of the virtual machines. Several techniques were used to determine the workloads of the cloud and then apply the migration algorithm for efficient utilization of resources. But, the process depends on the past outputs and only few step ahead predictions. Most of the techniques allocate the resources based on all the attributes. This results in higher processing time for the allocation. Hence, in this, an attribute based resource allocation is proposed to allocate and utilize the resources effectively based on the user demands. The modified Principal component analysis and relief is used for the attribute selection. Then, the selected attribute is processed with the hybrid Cauchy particle swarm algorithm for the allocation of resources. The proposed method is tested google cluster dataset and its performance is evaluated in terms of migration count and power consumption. The proposed method performance is compared with the automated migration technique (ALM) and forecast based migration technique (CF-LA). The proposed method outperforms both the existing technique by reducing the power consumption and the migration count between the virtual machines. Hence, the proposed MPCA and relief basedCPSO is best for allocating the resources dynamically in the cloud.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

Particle Swarm Algorithm Parameters Analysis for Scheduling Virtual Machines in Cloud Computing

2020 ◽  
Author(s):  
Wellington Francisco de Silva ◽  
Roberta Spolon ◽  
Renata Spolon Lobato ◽  
Aleardo Manacero Junior ◽  
Marcos Antonio Cavenaghi Humber
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Parameters Analysis ◽  
Swarm Algorithm

scholarly journals Enhancement of dynamic load balancing using Particle Swarm Algorithm in Cloud Environment

2016 ◽  
Vol 15 (10) ◽  
pp. 7164-7168
Author(s):  
Ginni Bansal ◽  
Amanpreet Kaur
Keyword(s):  
Load Balancing ◽  
Dynamic Load ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Dynamic Load Balancing ◽  
Particle Swarm Algorithm ◽  
Shared Resources ◽  
Multiple Resources ◽  
Swarm Algorithm ◽  
Load Balancer

Dynamic load balancing with decentralized load balancer using PSO technique: Cloud consists of multiple resources and various clients request to the cloud for allocation of shared resources. Each request will be allotted to the virtual machines. In different situation different machines get different load. So to balance the load amongst different virtual machines decentralized load balancer is enhanced using particle swarm algorithm. The main objective is reducing the energy and increasing the throughput in comparison to centralized and simple decentralized load balancer using particle swarm optimization.

scholarly journals Hybrid Load Balancing Approach based on the Integration of QoS and Power Consumption in Cloud Computing

2021 ◽  
Vol 10 (2) ◽  
pp. 1079-1090
Keyword(s):  
Load Balancing ◽  
Power Consumption ◽  
Task Scheduling ◽  
Hybrid Algorithm ◽  
Virtual Machines ◽  
Average Response ◽  
Average Response Time ◽  
The Past ◽  
Load Balancing Algorithm ◽  
And Task

Over the past few years, there has been keen research interest in load balancing and task scheduling in the cloud as the extensive amount of data that is stored in the server leads to significantly increased load. This can be resolved by using a hybrid algorithm in which the honeybee behavior algorithm’s advantages are integrated with fuzzy logic to conduct task scheduling and as well as balancing in the cloud. The design of this hybrid algorithm aims to enhance prior approaches. It is developed as per ABC and merges the important QoS factors along with power consumption so that the power that virtual machines (VMs) consume on the host can be precisely assessed, thereby ensuring efficient load balancing algorithm. The present study aims to evaluate the VMs’ power consumption by taking into account crucial QoS factors for selecting which host and virtual machine will be best suited for receiving the task. CloudSim was used to simulate the ILBA_HB algorithm. In terms of makespan, average response time, and degree of imbalance, the performance of the ILBA HB algorithm is compared to that of the LBA HB and HBB-LB algorithms. According to the results, the proposed algorithm outperformed LBA_HB and HBB-LB.

scholarly journals Energy Management Techniques for Cloud Based Environment

2020 ◽  
pp. 287-294
Author(s):  
Neenu Juneja ◽  
Chamkaur Singh ◽  
Krishan Tuli
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Virtual Machines ◽  
Electricity Consumption ◽  
Efficient Energy ◽  
Consumption Cost ◽  
Massive Scale ◽  
Virtual Resources ◽  
Computer Resources

Cloud Computing has the facility to transform a large part of information technology into services in which computer resources are virtualized and made available as a utility service. From here comes the importance of scheduling virtual resources to get the maximum utilization of physical resources. The growth in server’s power consumption is increased continuously; and many researchers proposed, if this pattern repeats continuously, then the power consumption cost of a server over its lifespan would be higher than its hardware prices. The power consumption troubles more for clusters, grids, and clouds, which encompass numerous thousand heterogeneous servers. Continuous efforts have been done to reduce the electricity consumption of these massive-scale infrastructures. To identify the challenges and required future enhancements in the field of efficient energy consumption in Cloud Computing, it is necessary to synthesize and categorize the research and development done so far. In this paper, the authors prepare taxonomy of huge energy consumption problems and its related solutions. The authors cover all aspects of energy consumption by Cloud Datacenters and analyze many more research papers to find out the better solution for efficient energy consumption. Keywords: Cloud computing, Collocated virtual machines, Live migration, Load balancing, Resource scheduling

scholarly journals An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm

2014 ◽  
Vol 14 (1) ◽  
pp. 25-39 ◽  
Author(s):  
Cao Jianfang ◽  
Chen Junjie ◽  
Zhao Qingshan
Keyword(s):  
Completion Time ◽  
Feasible Solution ◽  
Cloud Model ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Workflow Scheduling ◽  
Particle Swarm Algorithm ◽  
Discrete Particle ◽  
Cost Constraints ◽  
Virtual Resources

Abstract In order to solve the problems of security threats on workflow scheduling in cloud computing environments, the security of tasks and virtual machine resources are quantified using a cloud model, and the users’ satisfaction degree with the security of tasks assigned to the virtual resources is measured through the similarity of the security cloud. On this basis, combined with security, completion time and cost constraints, an optimized cloud workflow scheduling algorithm is proposed using a discrete particle swarm. The particle in the particle swarm indicates a different cloud workflow scheduling scheme. The particle changes its velocity and position using the evolution equation of the standard particle swarm algorithm, which ensures that it is a feasible solution through the feasible solution adjustment strategies. The simulation experiment results show that the algorithm has better comprehensive performance with respect to the security utility, completion time, cost and load balance compared to other similar algorithms.

scholarly journals Risk Prediction and Response Strategies in Corporate Financial Management Based on Optimized BP Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Meijia Zhai
Keyword(s):  
Neural Network ◽  
Risk Prediction ◽  
Bp Neural Network ◽  
Financial Management ◽  
Risk Evaluation ◽  
Financial Risk ◽  
Particle Swarm ◽  
Principal Component ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization

This paper mainly analyzes the theories related to the financial risk of the company and combines the principles of principal component analysis, particle swarm optimization algorithm, and artificial neural network to derive the financial risk index system of the company. To improve the accuracy of financial risk prediction, principal component analysis and particle swarm algorithm are applied to optimize the BP neural network model, the input data of the prediction model is improved, and the optimal initial weights and thresholds are given to the BP neural network by using particle swarm algorithm search, whereby the financial risk prediction model of particle swarm optimization BP neural network is constructed. The empirical results show that the model constructed by BP neural network not only has a high accuracy rate for static financial risk evaluation but also has a better prediction effect. After training and testing, the BP neural network-based enterprise financial risk evaluation model can accurately determine the existing financial situation of enterprise financial management and has a good prediction effect. Our research method is a fusion of the processing of the two methods, which belongs to the first integration of results.

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