Checkpointing Methods for Fault Tolerance on MapReduce: A Review
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 6
Abstract
Big Data is a term for information sets that are huge and complicated in which the traditional data processing applications are not capable to manage with them. MapReduce is a programming model for processing and producing these huge data sets. Programs written in this are naturally parallelized and runs on a huge cluster of commodity machines. Mapreduce executes fault tolerance at the task level, so when a task failure happens the whole task will be re-executed once more. In the case of long time running subtasks and then a single task failure is very large and cannot be avoided. The effect of failures can be considerable in terms of performance. As a result fault tolerance is significant for MapReduce. This Paper summarizes the checkpointing methods for fault tolerance on MapReduce.
Authors and Affiliations
Shani Sunny, Jemily Elsa Rajan
Keywords
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