Cloud computing task scheduling based on dynamically adaptive ant colony algorithm

2013 ◽  
Vol 33 (11) ◽  
pp. 3160-3162 ◽  
Author(s):  
Fang WANG ◽  
Mei'an LI ◽  
Weijun DAUN
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Ant Colony

Review of Task Scheduling Algorithm Research in Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 3236-3239 ◽  
Author(s):  
Mei Geng Huang ◽  
Zhi Qi Ou
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Slow Convergence

The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

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