performances of different schedulers in yarn and their effects on hadoop haolap

This study investigates the effects of the yarn on the haolap hadoop and tries to assess how different types of schedulers of the yarn can be used to achieve the optimum result to improve the performance of the haolap hadoop. in the first step, haolap hadoop v.1.0 is compared with haolap hadoop v.3.2.1 augmented with yarn, and all three major schedulers namely fifo, fair, and capacity are separately used and their effects on execution time are evaluated. accordingly, three collections of data called c_1, c_2, and c_3 containing 〖10〗^5, 〖10〗^6, and 〖10〗^7 items respectively from medical clinic records are selected and evaluated under three operations a_1, a_2, and a_3. in the second step performance of the schedulers is compared and their cons and pros are assessed. finally, some suggestions to achieve the optimum results to augment haolap hadoop with yarn are presented based on this study’s results.