Configuration optimization method of Hadoop system performance based on genetic simulated annealing algorithm
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The configuration optimization method of Hadoop system performance based on genetic simulated annealing algorithm is studied. In view of the performance of Hadoop on open source cloud computing platform, an optimization method is proposed. Based on the genetic simulated annealing algorithm, each configuration scheme is used as a chromosome for multiple selection, crossover and mutation. Combined with the principle of simulated annealing, the survival of the new chromosome and the number of iterations of the whole algorithm are controlled, and the optimal scheme of the system configuration is found. The experimental results show that the method can effectively improve the operation efficiency of the operation. In addition, the overall effect of the group is very good at the end of the iteration. When the job types in the system are similar, according to the characteristics that the whole simulated annealing algorithm is approaching the optimal solution, a real-time optimization configuration model is proposed on the basic of genetic simulated annealing algorithm.
KeywordsGenetic simulated annealing algorithm Hadoop System performance Configuration optimization
This research was financially supported by Chinese Natural Science Foundations (61363016, 61063004), Key Project of Inner Mongolia Advanced Science Research (NJZZ14100), Inner Mongolia Colleges and Universities Education Department Science Research (NJZC059), Natural Science Foundation of Inner Mongolia Autonomous Region of China (No. 2015MS0605, No. 2015MS0626 and No. 2015MS0627) and Ministry of Education Scientific research foundation for Study abroad personel  1685.
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