Open AccessA Brief Review on scheduling algorithms of MapReduce Optimization Techniques
Author(s) -
R. Lavanya,
Jeevanshu Malhotra,
Rajeshwari Swaminathan
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1362/1/012001
Subject(s) - computer science , scalability , scheduling (production processes) , distributed computing , fair share scheduling , two level scheduling , dynamic priority scheduling , locality , big data , data mining , database , mathematical optimization , operating system , mathematics , schedule , linguistics , philosophy
With the increase in size and complexity if modern datasets, the world is faced with new challenges in the automation and scalability of the very large data sets. Scheduling has been an active area of research in computing systems since their inception. The main objective is to study MapReduce framework, MapReduce model, scheduling in hadoop, various scheduling algorithms and various optimization techniques in job scheduling. Scheduling algorithms of MapReduce model using hadoop vary with design and behaviour, and are used for handling many issues like data locality, awareness with resource, energy and time.