MapReduce scheduling algorithms in Hadoop : a systematic study

• Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

Nyckelord

Distributed systems

Resource allocation

Scheduling algorithms

Hadoop

MapReduce

Fair scheduling

Data- och informationsvetenskap

Computer and Information Sciences Computer Science

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)