Multi-Objective Resources Allocation Using Improved Genetic Algorithm at Cloud Data Center

2016 ◽  
Author(s):  
Neeraj Kumar Sharma ◽  
Ram Mohana Reddy Guddeti
Keyword(s):  
Genetic Algorithm ◽  
Data Center ◽  
Resources Allocation ◽  
Improved Genetic Algorithm ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Multi Objective

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.

A Fuzzy Logic Approach for Resources Allocation in Cloud Data Center

2015 ◽  
Author(s):  
Davide Adami ◽  
Andrea Gabbrielli ◽  
Stefano Giordano ◽  
Michele Pagano ◽  
Giuseppe Portaluri
Keyword(s):  
Fuzzy Logic ◽  
Data Center ◽  
Resources Allocation ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Fuzzy Logic Approach ◽  
Logic Approach

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.

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