A Novel Scheduling Algorithm based on Clustering Analysis and Data Partitioning For Big Data
- DOI
- 10.2991/iccnce.2013.136How to use a DOI?
- Keywords
- big data, clustering analysis, cloud computing.
- Abstract
With the development of the computer technology and network technology, the size of data collected is increasing rapidly. It is difficult to process and analyze so big data in real-time. Cloud computing is an effective tool for real-time processing for big data. How to make full use of cloud computing to analyze big data is an hot issue in recent year. Because of the limit of current network transmission speed, there is much communication between the selected cloud computing nodes during big data processing, which will cause a heavy transmission delay and lead to the reduce of real-time. The current methods select cloud nodes for data processing depend on the quality of services of nodes, which cannot reflect the relations between different cloud nodes. To address this problem, the paper proposes a novel scheduling algorithm based on clustering analysis for big data analysis in cloud environment. The presented method divides the cloud nodes into clusters according to communication cost between different nodes, and then selects a cluster for the big data analysis services. Experimental results show the effectiveness of our scheduling algorithm.
- Copyright
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Weiqi Cui AU - Nan Liu AU - Yihuan Dong AU - Jiaqi Li AU - Qingchen Zhang PY - 2013/07 DA - 2013/07 TI - A Novel Scheduling Algorithm based on Clustering Analysis and Data Partitioning For Big Data BT - Proceedings of the International Conference on Computer, Networks and Communication Engineering (ICCNCE 2013) PB - Atlantis Press SP - 549 EP - 551 SN - 1951-6851 UR - https://doi.org/10.2991/iccnce.2013.136 DO - 10.2991/iccnce.2013.136 ID - Cui2013/07 ER -