@inproceedings{27d0aabc09654f98ba28ade1c7e7771d,
title = "Providing caches for reduce tasks in a MapReduce cloud",
abstract = "A MapReduce cloud is the key to the success of cloud computing nowadays by the capability of processing large datasets simultaneously on nodes in a cloud. However, a MapReduce cloud may waste many CPU resources to frequently process similar intermediate data in its Reduce tasks because specific intermediate data is always moved to specific Slave nodes. A MapReduce cloud can utilize the proposed idea of supporting the cache mechanism for Reduce tasks to avoid unnecessary computation. In experiments, a MapReduce cloud is proved to get great performance improvement from the help of the cache mechanism when running CPU-intensive applications. Accordingly, a MapReduce cloud can be justified to have the extension of the cache mechanism proposed in this paper.",
author = "Huang, {Tzu Chi} and Chu, {Kuo Chih} and Chen, {Jhe Ru} and Zeng, {Xue Yan} and Shieh, {Ce Kuen}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016 ; Conference date: 12-03-2016 Through 14-03-2016",
year = "2016",
month = jul,
day = "12",
doi = "10.1109/ICBDA.2016.7509834",
language = "English",
series = "Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016",
address = "United States",
}