scholarly journals Research on Resource Scheduling of Cloud Computing Based on Improved Genetic Algorithm

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Juanzhi Zhang ◽  
Fuli Xiong ◽  
Zhongxing Duan
Keyword(s):  
Genetic Algorithm ◽  
Data Center ◽  
Completion Time ◽  
Resource Scheduling ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Cloud Data Center ◽  
Two Stage ◽  
Cloud Data ◽  
Conventional Genetic Algorithm

In order to solve the problem that the resource scheduling time of cloud data center is too long, this paper analyzes the two-stage resource scheduling mechanism of cloud data center. Aiming at the minimum task completion time, a mathematical model of resource scheduling in cloud data center is established. The two-stage resource scheduling optimization simulation is realized by using the conventional genetic algorithm. On the technology of the conventional genetic algorithm, an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm. The experimental results show that the improved genetic algorithm can significantly reduce the total completion time of the task, and has good convergence and global optimization ability.

Task Scheduling Mechanism Based on Multi-QoS Genetic Algorithm in Cloud Data Center

2013 ◽  
Vol 846-847 ◽  
pp. 1468-1471
Author(s):  
De Wen Wang ◽  
Yang Liu
Keyword(s):  
Genetic Algorithm ◽  
Electric Power ◽  
Task Scheduling ◽  
Data Center ◽  
Evaluation Model ◽  
Operating Efficiency ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Computing Platform ◽  
Qos Evaluation

A multi-QoS evaluation model for electric power users is defined, combined with the characteristics of data center in electric power corporation, based on the research of cloud computing platform of data center in electric power corporation and task scheduling strategies of cloud data center. And a genetic algorithm based on multi-QoS, which fitness functions are QoS utility value and completion time, is put forward. Tests in Cloudsim platform and the result shows that the genetic algorithm based on multi-QoS can satisfy the requirements of multi-QoS of electric power users and improve the operating efficiency of data center in electric power corporation.

Blower Resource Scheduling Based on Elite Genetic Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 591-595 ◽  
Author(s):  
Guang Nian Yang ◽  
Wei Qi ◽  
Jun Zhou
Keyword(s):  
Genetic Algorithm ◽  
High Efficiency ◽  
Scheduling Algorithm ◽  
Sewage Treatment ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Task Completion ◽  
Toxic Substances ◽  
Improved Genetic Algorithm ◽  
Grid Resource

Now, our sewage treatment industry mainly depends on the blower of aeration act as metabolic, absorbed in the toxic substances. Blower resources management is the key issue of sewage treatment. Traditional resource scheduling algorithm exist some defects, for example it can not well meet the quality requirements and can not get the optimal solution. This article gives a new resource scheduling method based on improved genetic algorithm. It achieves grid resource scheduling by using real number encoding and activities point crossover. Experiments show that genetic algorithm can reduce executing time and task completion time, and further improve the scalability of resource scheduling model. This algorithm has stability and high efficiency in grid environment.

An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization

2016 ◽  
Vol 8 (2) ◽  
pp. 43-57 ◽  
Author(s):  
Li Mao ◽  
De Yu Qi ◽  
Wei Wei Lin ◽  
Bo Liu ◽  
Ye Da Li
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Resource Scheduling ◽  
Constraint Satisfaction Problems ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource

With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

Virtual Machine Migrating Algorithm Based on Genetic Algorithm in Cloud Data Center

2014 ◽  
Vol 513-517 ◽  
pp. 2031-2034
Author(s):  
Hui Zhang ◽  
Yong Liu
Keyword(s):  
Genetic Algorithm ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Data Center ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Cloud Data

Virtual machine migration is an effective method to improve the resource utilization of cloud data center. The common migration methods use heuristic algorithms to allocation virtual machines, the solution results is easy to fall into local optimal solution. Therefore, an algorithm called Migrating algorithm based on Genetic Algorithm (MGA) is introduced in this paper, which roots from genetic evolution theory to achieve global optimal search in the map of virtual machines to target nodes, and improves the objective function of Genetic Algorithm by setting the resource utilization of virtual machine and target node as an input factor into the calculation process. There is a contrast between MGA, Single Threshold (ST) and Double Threshold (DT) through simulation experiments, the results show that the MGA can effectively reduce migrations times and the number of host machine used.

P-Aware: a proportional multi-resource scheduling strategy in cloud data center

2016 ◽  
Vol 19 (3) ◽  
pp. 1089-1103 ◽  
Author(s):  
Hang Zhou ◽  
Qing Li ◽  
Weiqin Tong ◽  
Samina Kausar ◽  
Hai Zhu
Keyword(s):  
Data Center ◽  
Resource Scheduling ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Scheduling Strategy
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