Open AccessResearch on Hodoop Job Scheduling Algorithms Based on Dynamic Fusion
Author(s) -
Lihong Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1314/1/012183
Subject(s) - ant colony optimization algorithms , population based incremental learning , computer science , genetic algorithm , meta optimization , algorithm , ant colony , selection (genetic algorithm) , mathematical optimization , dinic's algorithm , scheduling (production processes) , metaheuristic , artificial intelligence , mathematics , machine learning , graph , theoretical computer science , dijkstra's algorithm , shortest path problem
Adaptive genetic algorithm and improved ant colony algorithm are combined to solve Hadoop job scheduling problem. Firstly, the global search ability of the adaptive genetic algorithm is used to generate the list of resources allocated by the task. When the search speed of the genetic algorithm gradually decreases, the optimal integration time of the adaptive genetic algorithm and the ant colony algorithm is determined dynamically. The initial pheromone distribution of the ant colony algorithm is generated from the optimal solution solved by the adaptive genetic algorithm. Improve the target node selection strategy of ant colony algorithm, consider the success rate of completing tasks of nodes, and accelerate the speed of ant colony algorithm to solve the optimal solution. Simulation results show that compared with genetic algorithm and ant colony algorithm, hybrid genetic algorithm takes less time, and the more tasks, the more obvious advantages.