Analisis Perbandingan Kinerja Algoritme Fair Share dan Capacity Scheduling pada Pengiriman Job Hadoop Multi-Node Cluster - Herdian Zend Komara; Heru Nurwarsito (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Hadoop is a distributed system use to process large amounts of data whith is stored in several groups of compute. Hadoop has three main connected component namely hadoop distributed file system, mapreduce and Yet Another Resource Negotiator it also called YARN. YARN is used as a resource which regulases cluster processing and hadoop scheduling. There are several scheduling algorithm on Hadoop including capacity scheduling and for share scheduling. Capacity scheduling algoritm is an algorithm that can make schedule in YARN by executing job first. Each scheduling has priority for resource and cluster while slot in scheduling idle, so the scheduling can be use directly even it has not priority. Fair share scheduling algorithm is an algorithme that runs at each hadoop cluster so that cluster on each job are equal. This research was hold to optimize performance of fair share algorithme and to compaire performance of these algorithme wit capacity scheduling using parameters job fail rate, responce time and throughput. Based on the results of testing that has been do. In the fair share algorithm the difference in the average value of failrate parameters is 0.623% better than the capacity scheduling algorithm, the responce time parameter is 5.44 minute better than the capacity scheduling algorithm and the throughput is 0.596 Job/minute better than the capacity scheduling algorithm so the average value the fair share algorithm is better than the capacity scheduling value.