Statistical analysis of an improved tuning method for optimizing performances of Hadoop applications
Prabhdeep Singh, M. S. Guru Prasad, Harsh Taneja, Aditya Pai H, Sharonchrista · Journal of Information and Optimization Sciences · 2022
Hadoop is one among upcoming and most promising technologies for handling immense volume of data, generated from a variety of sources. It supports parallel processing among multiple jobs, and when such jobs are being executed on Hadoop, it is of paramount importance to know if Hadoop cluster resources are being completely utilized or not, which in turn tells us about efficiency in resource utilization for the purpose of effective completion of jobs. It could happen that Hadoop cluster resources are not being completely utilized, and because of which there arises a need for performance tuning. Fortunately, Hadoop framework equips us with several parameters to do the same and fine-tune aforesaid jobs. Performance tuning comprises of four main components: CPU utilization, Memory consumption, Disk I/O and Network traffic. In the proposed work, authors have presented several important relative parameters in relation to aforesaid components, along with how to fine-tune these parameters with appropriate values so as to optimize Hadoop execution.