scholarly journals Clustering composite SaaS components in Cloud computing using a Grouping Genetic Algorithm

2012 ◽  
Author(s):  
Zeratul Izzah Mohd Yusoh ◽  
Maolin Tang
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Grouping Genetic Algorithm ◽  
Composite Saas

An Ant Colony Optimization for the Composite SaaS Placement Problem in the Cloud

2011 ◽  
Vol 130-134 ◽  
pp. 3062-3067 ◽  
Author(s):  
Zhi Wei Ni ◽  
Xue Feng Pan ◽  
Zhang Jun Wu
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Software As A Service ◽  
Ant Colony ◽  
Placement Problem ◽  
Main Medium ◽  
Composite Saas ◽  
Cloud Network

Cloud computing has become a main medium for Software as a Service (SaaS) hosting as it can provide the scalability a SaaS requires. In the composite SaaS placement process， it should consider the factors about the size of the cloud network, SaaS interactions between its components and SaaS interactions with its data components. A previous research has tackled this problem using a genetic algorithm (GA) approach. This paper proposes an ant colony optimization (ACO) approach. The ACO for the SaaS placement problem has been implemented and evaluated.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment

2019 ◽  
Vol 23 (2) ◽  
pp. 797-836 ◽  
Author(s):  
Seyedeh Yasaman Rashida ◽  
Masoud Sabaei ◽  
Mohammad Mehdi Ebadzadeh ◽  
Amir Masoud Rahmani
Keyword(s):  
Genetic Algorithm ◽  
Cloud Environment ◽  
Vm Placement ◽  
Grouping Genetic Algorithm ◽  
Cost Efficient ◽  
Multi Cloud

Optimal machine placement based on improved genetic algorithm in cloud computing

2021 ◽  
Author(s):  
Jiawei Lu ◽  
Wei Zhao ◽  
Haotian Zhu ◽  
Jie Li ◽  
Zhenbo Cheng ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Improved Genetic Algorithm ◽  
Optimal Machine
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