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Item

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Optimum Checkpoint Interval for MapReduce Fault-Tolerance

http://hdl.handle.net/20.500.12678/0000005341
73ee94e5-489d-46fe-a857-8f0ab43085b2
f2839568-b077-4848-b0b1-17a0b73c04df
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Optimum Optimum Checkpoint Interval for MapReduce Fault-Tolerance.pdf (471 Kb)
Publication type
Conference paper
Upload type
Publication
Title
Title Optimum Checkpoint Interval for MapReduce Fault-Tolerance
Language en
Publication date 2017-11-01
Authors
Naychi Nway Nway
Julia Myint
Description
MapReduce is the efficient framework for parallel
processing of distributed big data in cluster environment.
In such a cluster, task failures can impact on performance
of applications. Although MapReduce automatically
reschedules the failed tasks, it takes long completion time
because it starts from scratch. The checkpointing
mechanism is the valuable technique to avoid reexecution
of failed tasks in MapReduce. However,
defining incorrect checkpoint interval can still decrease
the performance of MapReduce applications and job
completion time. In this paper, the optimum checkpoint
interval is proposed to reduce MapReduce job completion
time when failures occur. The proposed system defines
checkpoint interval that is based on five parameters:
expected job completion time without checkpointing,
checkpoint overhead time, rework time, down time and
restart time. Therefore, because of proposed checkpoint
interval, MapReduce does not need to re-execute the
failed tasks, so it reduces job completion time when
failures occur. The proposed system reduces job
completion time even though the number of failures
increases and the performance of this system can be
improved 4 times better than the original MapReduce.
Keywords
MapReduce, Big data, Task failures, Completion time, Checkpoint interval
Conference papers
ICAIT
1 November, 2017
International Conference on Advanced Information Technologies
Yangon, Myanamar
https://www.uit.edu.mm/icait-2017/
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