Abstract
In recent years, China’s rapid growth on the demand for satellite communication application, not only impels the continuous expanding on the management scale of network operation and maintenance (O&M) system, but also puts forward higher demand for intelligent network management and control. Under this circumstance, the data management function, which services as the core of the satellite network O&M system, is faced with the serious management challenges brought by the huge amount and complex datasets. In this paper, we study the distributed cooperative storage management technologies for big data management issue in satellite network O&M. We propose a distributed cooperative big data storage model for the satellite network O&M, and further study the intra-site hybrid database management strategy and inter-site fast data synchronization technology, to improve the scalability and disaster tolerance of data service in the O&M application. Finally, we evaluate the hybrid database architecture based on Oracle and HBase using the benchmark, and compare the theoretical network traffic with the actual flow measured by GoldenGate, then perform the quantitative analysis on the system disaster tolerance of data services.
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References
Maral, G., Bousquet, M., Sun, Z.: Satellite Communications Systems: Systems, Techniques and Technology. Wiley, Chichester (2009)
Yang, W.: Theory and progress of active operation and maintenance of mobile internet based on big data. Big Data Res. 2(6), 97–109 (2016)
Suto, K., Avakul, P., Nishiyama, H., Kato, N.: An efficient data transfer method for distributed storage system over satellite networks. In: 77th IEEE Vehicular Technology Conference, vol. 14(6), pp. 1–5 (2013)
Frey, J., Corbo, T.: Managing networks in the age of cloud, SDN, and big data: network management megatrends. Enterprise Management Associates, April 2014
Das, A., Lumezanu, C., Zhang, Y., Singh, V., Jiang, G., Yu, C.: Transparent and flexible network management for big data processing in the cloud. In: Proceedings of the 5th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 2013), June 2013
Ford, D., Labelle, F., Popovici, F., et al.: Availability in globally distributed file systems. In: Operating Systems Design and Implementation (2010)
Muthitacharoen, A., Chen, B., Mazierres, D.: A low-bandwidth network file system. In: Proceedings of the 18th ACM Symposium on Operating Systems Principles, pp. 174–187, October 2001
Satyanarayanan, M., Howard, J., Nichols, D.: The ITC distributed file system: principles and design. In: Proceedings of the 10th ACM Symposium on Operating Systems Principles, Orcas Island, Washington, United States, pp. 35–50 (1985)
Sandberg, R., Golgberg, D., Kleiman, S.: Design and implementation of the sun network file system. In: Proceedings of the Summer 1985 USENIX Conference, pp. 119–130, June 1985
Schmuck, F., Haskin, R.: GPFS: a shared-disk file system for large computing clusters. In: Proceedings of the Conference on File and Storage Technologies (FAST 2002), pp. 231–244 (2002)
VMware, Inc. VMware Virtual Machine File System. White Paper (2007)
Welch, B., Unangst, M., Abbasi, Z., et al.: Scalable performance of the panasas parallel file system. In: Proceedings of the 6th USENIX Conference on File and Storage Technologies, San Jose, California (2008)
Lustre File System [EB/OL]. http://wiki.lustre.org/
Weil, S., Brandt, S., Miller, E., et al.: Ceph: a scalable, high-performance distributed file system. In: Proceedings of the 7th Symposium on Operating Systems Design and Implementation (OSDI 2006) (2006)
Ghemawat, S., Gobioff, H., Leung, S.: The google file system. In: Proceedings of SOSP 2003, Bolton Landing, New York, USA, October 2003
Hadoop [EB/OL]. http://hadoop.apache.org/
VSAN [EB/OL]. http://www.vmware.com/cn/products/virtualsan.html
ScaleIO [EB/OL]. https://www.emc.com/storage/scaleio/index.htm
Nutanix Distributed File System [EB/OL]. https://www.nutanix.com/products/software-editions/
Fusion Storage [EB/OL]. http://www.huawei.com/cn/
Kemper, A., Neumann, T.: HyPer: a hybrid OLTP& OLAP main memory database system based on virtual memory snapshots. In: Proceedings of the ICDE 2011, pp. 195–206 (2011)
Shute, J., Vingralek, R., Samwel, B., et al.: F1: a distributed SQL database that scales. In: Proceedings of the 39th International Conference on Very Large Data Bases, 26–30 August 2013, Trento, Italy (2013)
Chang, F., Dean, J., Ghemawat, S., et al.: Bigtable: a distributed storage system for structured data. In: Proceedings of OSDI 2006, pp. 205–218 (2006)
Alibaba HybridDB [EB/OL]. https://help.aliyun.com/product/35364.html
Xeround Cloud Database [EB/OL]. http://xeround.com/
Oracle [EB/OL]. https://www.oracle.com/index.html
HBase [EB/OL]. http://hbase.apache.org/
Phoenix [EB/OL]. http://phoenix.apache.org/
Thusoo, A., Sarma, J., Jain, N., et al.: Hive - a petabyte scale data warehouse using Hadoop. In: IEEE 26th International Conference on Data Engineering (ICDE), Long Beach, USA, pp. 996–1005. IEEE (2010)
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This research was supported by the National Natural Science Foundation of China under Grant No. 61402518.
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Yinjin, F., Rui, H., Jun, X. (2018). Distributed Cooperative Storage Management Framework for Big Data in Satellite Network Operation and Maintenance. In: Yu, Q. (eds) Space Information Networks. SINC 2017. Communications in Computer and Information Science, vol 803. Springer, Singapore. https://doi.org/10.1007/978-981-10-7877-4_8
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DOI: https://doi.org/10.1007/978-981-10-7877-4_8
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