Distributed Layered Resource Management Model in Blue Whale Distributed File System

Huang Hua; Zhang Jiangang; Xu Lu

Journal of Computer Research and Development > 2005 > 42(6): 1034-1038.

Huang Hua, Zhang Jiangang, Xu Lu. Distributed Layered Resource Management Model in Blue Whale Distributed File System[J]. Journal of Computer Research and Development, 2005, 42(6): 1034-1038.

Citation:

Huang Hua, Zhang Jiangang, Xu Lu. Distributed Layered Resource Management Model in Blue Whale Distributed File System[J]. Journal of Computer Research and Development, 2005, 42(6): 1034-1038.

Citation:

Huang Hua, Zhang Jiangang, Xu Lu. Distributed Layered Resource Management Model in Blue Whale Distributed File System[J]. Journal of Computer Research and Development, 2005, 42(6): 1034-1038.

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Distributed Layered Resource Management Model in Blue Whale Distributed File System

1(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080) 2(Graduate School of the Chinese Academy of Sciences, Beijing 100039)

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Published Date: June 14, 2005

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Abstract

Abstract

In order to manage massive storage efficiently, Blue Whale distributed file system discards the traditional central resource management model, and adopts a distributed layered resource management model. This model supports multiple storage nodes and a cluster of metadata servers. The out-of-band data transportation alleviates the bottlenecks of performance, and enables metadata server cluster to handle metadata concurrently and efficiently, and also provides load balancing in the system. Theoretical analysis and test results show that this model outperforms in capability and scalability in various circumstances.
- file system,
- distributed file system,
- Blue Whale,
- resource management

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[2]	Zhang Yiwen, Guo Ruifeng, Deng Changyi. Low Power Scheduling Algorithm for Mix Tasks Based on Constant Bandwidth Server[J]. Journal of Computer Research and Development, 2015, 52(9): 2094-2104. DOI: 10.7544/issn1000-1239.2015.20140611
[3]	Li Yibin, Jia Zhiping, Xie Shuai, and Liu Fucai. Partial Dynamic Reconfigurable WSN Node with Power and Area Efficiency[J]. Journal of Computer Research and Development, 2014, 51(1): 173-179.
[4]	Qi Shubo, Li Jinwen, Yue Daheng, Zhao Tianlei, and Zhang Minxuan. Adaptive Buffer Management for Leakage Power Optimization in NoC Routers[J]. Journal of Computer Research and Development, 2011, 48(12): 2400-2409.
[5]	Liu Zhenglin, Han Yu, Zou Xuecheng, and ChenYicheng. Power Analysis Attacks Against AES Based on Maximal Bias Signal[J]. Journal of Computer Research and Development, 2009, 46(3): 370-376.
[6]	Zhou Hongwei, Zhang Chengyi, and Zhang Minxuan. A Method of Statistics-Based Cache Leakage Power Estimation[J]. Journal of Computer Research and Development, 2008, 45(2): 367-374.
[7]	Wen Dongxin, Yang Xiaozong, and Wang Ling. A High Level Synthesis Scheme and Its Realization for Low Power Design in VLSI[J]. Journal of Computer Research and Development, 2007, 44(7): 1259-1264.
[8]	Zhao Jia, Zeng Xiaoyang, Han Jun, Wang Jing, and Chen Jun. VLSI Implementation of an AES Algorithm Resistant to Differential Power Analysis Attack[J]. Journal of Computer Research and Development, 2007, 44(3).
[9]	Wang Wei, Han Yinhe, Hu Yu, Li Xiaowei, Zhang Yousheng. An Effective Low-Power Scan Architecture—PowerCut[J]. Journal of Computer Research and Development, 2007, 44(3).
[10]	Ma Zhiqiang, Ji Zhenzhou, and Hu Mingzeng. A Low-Power Instruction Cache Design Based on Record Buffer[J]. Journal of Computer Research and Development, 2006, 43(4): 744-751.