Grid Resource Scheduling Strategy Based on Elite DNA Genetic Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 1594-1598
Author(s):  
Sheng Jun Xue ◽  
Wei Qi
Keyword(s):  
Genetic Algorithm ◽  
High Efficiency ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Optimal Solution ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Grid Resource ◽  
Grid Environment ◽  
Grid Resource Scheduling

Traditional resource scheduling algorithm, in grid environment, 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.

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.

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.

Research on Resource Scheduling Algorithm Integrating Task Weight and Multi-QoS Constraint

2011 ◽  
Vol 52-54 ◽  
pp. 1125-1130
Author(s):  
Jing Chen ◽  
Ming Xin Liu
Keyword(s):  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Grid Resource ◽  
Resource Information ◽  
Grid Resource Scheduling ◽  
Utilization Ratio ◽  
Simulation Results ◽  
And Task

To improve the utilization ratio of resources and the complete number of tasks, a kind of a new grid resource scheduling algorithm TWMQC (based on Task Weight and Multi-QoS Constraint) integrating multi-QoS constraint with task weight was proposed. The accomplished process of grid resource scheduling algorithm was transformed multi attribute constraints of resource and task, according to the parametric resource information and task information, classified different task weight sets based on the priority of tasks. Multi-QoS constraints of deadline of gridlets, bandwidth and CPU were defined, and the correlative algorithms were simulated by the GridSim toolkits. The simulation results show that algorithm TWMQC, which integrating multi-QoS constraint and tasks weight is superior in solving such kind of issues by comparing and analyzing the result data.

Algorithm Research Based on Distributed Water Multi-Level Resources Scheduling

2014 ◽  
Vol 513-517 ◽  
pp. 2565-2568
Author(s):  
Yi Fan Yuan ◽  
Jiu Li Wang
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Resource Scheduling ◽  
Improved Genetic Algorithm ◽  
Scheduling Policy ◽  
Coding Scheme ◽  
Level Sequence ◽  
Multi Level ◽  
And Storage

With the rapid development of hydropower in China, there are a lot of reservoirs under constructions are put into operation. Therefore, resource scheduling of distributed water conservancy project has become a key focus in current researches. Based on distributed water multi-level resources, the paper put forward to apply the improved genetic algorithm to reservoir resource scheduling. In this way, water level sequence can be the basic genetic algorithm coding scheme, and storage status of reservoir can be stored with the array. Then the genetic algorithm coding can be operated based on the corresponding array index of each reservoir. The paper tries to prove the feasibility of this scheduling policy with some examples, simplifying the process of scheduling algorithm and providing guiding basis for water resource scheduling.

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