A cross study on apache hadoop and yarn schedulers - Deshai, N.; V.D.S.Sekhar, B.; Venkataramana, S.; V.S.S.S.Chakravarthy, V.; S.R.Chowdary, P. (International Journal of Engineering and Technology, 2018)
Todays, digital world facing more challenges from processing data with general technologies on different real-time oriented applications. The specific reason that a fast-growing scale on the size of the datasets during continuously generated from heterogeneous applications in various industries and fields, while such rapidly expanding data size to handling, processing, computing and store efficiently with Existing techniques are extremely critical and difficult. Nowadays, computer world focuses the innovative direction for massive information process and storage about digital world activities is called big data (BD) because at present digital transaction world datasets have double in every second. So many fields, industries and applications are turned to big data methods and platforms. Core Hadoop open source community has most famous and advanced technologies which Assist efficiently process, organize also store huge length of datasets through popular components are Hadoop Distributed File System which is quickly stored peta and zetta-byte information and efficiently processing that petabyte and zetta-byte information by Map-Reduce (MR), but working with that hadoop1 version some restrictions on resource allotment, scalability and support only few applications. Therefore, we describe an efficient comparison with new MR is yet another Resource Negotiator to avoid Hadoop v1 efficient resource allotments issues. Because advance resource allotments are leading function for efficiently, process the jobs. Also, study default schedulers with advanced schedulers in their issues on basic Hadoop v1 MR and YARN.YARN presents advanced schedulers like fair and capacity schedulers are leads high utilization onresources,excellent sharing and more scalability.Â
Keywords: Big Data; Hadoop; HDFS; Map Reduce; Schedulers; Yarn.