HybSMRP: a hybrid scheduling algorithm in Hadoop MapReduce framework

Due to the advent of new technologies, devices, and communication tools such as social networking sites, the amount of data produced by mankind is growing rapidly every year. Big data is a collection of large datasets that cannot be processed using traditional computing techniques. MapReduce has been introduced to solve large-data computational problems. It is specifically designed to run on commodity hardware, and it depends on dividing and conquering principles. Nowadays, the focus of researchers has shifted towards Hadoop MapReduce. One of the most outstanding characteristics of MapReduce is data locality-aware scheduling. Data locality-aware scheduler is a further efficient solution to optimize one or a set of performance metrics such as data locality, energy consumption and job completion time. Similar to all situations, time and scheduling are the most important aspects of the MapReduce framework. Therefore, many scheduling algorithms have been proposed in the past decades. The main ideas of these algorithms are increasing data locality rate and decreasing the response and completion time. In this paper, a new hybrid scheduling algorithm has been proposed, which uses dynamic priority and localization ID techniques and focuses on increasing data locality rate and decreasing completion time. The proposed algorithm was evaluated and compared with Hadoop default schedulers (FIFO, Fair), by running concurrent workloads consisting of Wordcount and Terasort benchmarks. The experimental results show that the proposed algorithm is faster than FIFO and Fair scheduling, achieves higher data locality rate and avoids wasting resources.