An Effective Remote Data Disaster Recovery Plan for the Space TT&C System

Weijie Han; Jingfeng Xue; Fuquan Zhang; Zhonghui Sun

doi:10.1007/978-3-030-62460-6_4

An Effective Remote Data Disaster Recovery Plan for the Space TT&C System

Weijie Han^*, Jingfeng Xue, Fuquan Zhang, Zhonghui Sun

^*Corresponding author for this work

School of Computer Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The critical asset data of the Space Tracking Telemetry and Command (TT&C) System plays an important role in fulfilling space missions. According to analyze the current storing methods and disaster recovery requirements of the data, the remote data disaster recovery techniques are studied based on the remote replication capability of the Oracle database, and the remote data disaster recovery plan is developed for the space TT&C system. Furthermore, the experiment is conducted to validate the plan by building a simulation environment. The experiment results demonstrate that the plan can reach the fifth degree of the disaster recovery level and satisfy the following three performance requirements including recoverability, reliability and real-time performance, and therefore realize remote disaster recovery of the critical asset data for the space TT&C system efficiently.

Original language	English
Title of host publication	Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings
Editors	Xiaofeng Chen, Hongyang Yan, Qiben Yan, Xiangliang Zhang
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	31-41
Number of pages	11
ISBN (Print)	9783030624590
DOIs	https://doi.org/10.1007/978-3-030-62460-6_4
Publication status	Published - 2020
Event	3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 - Guangzhou, China Duration: 8 Oct 2020 → 10 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12487 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020
Country/Territory	China
City	Guangzhou
Period	8/10/20 → 10/10/20

Keywords

Database disaster recovery
Oracle DataGuard
Oracle GoldenGate
Remote data disaster recovery
The space TT&C system

Access to Document

10.1007/978-3-030-62460-6_4

Cite this

Han, W., Xue, J., Zhang, F., & Sun, Z. (2020). An Effective Remote Data Disaster Recovery Plan for the Space TT&C System. In X. Chen, H. Yan, Q. Yan, & X. Zhang (Eds.), Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings (pp. 31-41). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12487 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-62460-6_4

Han, Weijie ; Xue, Jingfeng ; Zhang, Fuquan et al. / An Effective Remote Data Disaster Recovery Plan for the Space TT&C System. Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. editor / Xiaofeng Chen ; Hongyang Yan ; Qiben Yan ; Xiangliang Zhang. Springer Science and Business Media Deutschland GmbH, 2020. pp. 31-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "An Effective Remote Data Disaster Recovery Plan for the Space TT&C System",

abstract = "The critical asset data of the Space Tracking Telemetry and Command (TT&C) System plays an important role in fulfilling space missions. According to analyze the current storing methods and disaster recovery requirements of the data, the remote data disaster recovery techniques are studied based on the remote replication capability of the Oracle database, and the remote data disaster recovery plan is developed for the space TT&C system. Furthermore, the experiment is conducted to validate the plan by building a simulation environment. The experiment results demonstrate that the plan can reach the fifth degree of the disaster recovery level and satisfy the following three performance requirements including recoverability, reliability and real-time performance, and therefore realize remote disaster recovery of the critical asset data for the space TT&C system efficiently.",

keywords = "Database disaster recovery, Oracle DataGuard, Oracle GoldenGate, Remote data disaster recovery, The space TT&C system",

author = "Weijie Han and Jingfeng Xue and Fuquan Zhang and Zhonghui Sun",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020 ; Conference date: 08-10-2020 Through 10-10-2020",

year = "2020",

doi = "10.1007/978-3-030-62460-6_4",

language = "English",

isbn = "9783030624590",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Xiaofeng Chen and Hongyang Yan and Qiben Yan and Xiangliang Zhang",

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}

Han, W, Xue, J, Zhang, F & Sun, Z 2020, An Effective Remote Data Disaster Recovery Plan for the Space TT&C System. in X Chen, H Yan, Q Yan & X Zhang (eds), Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12487 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 31-41, 3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020, Guangzhou, China, 8/10/20. https://doi.org/10.1007/978-3-030-62460-6_4

An Effective Remote Data Disaster Recovery Plan for the Space TT&C System. / Han, Weijie; Xue, Jingfeng; Zhang, Fuquan et al.
Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. ed. / Xiaofeng Chen; Hongyang Yan; Qiben Yan; Xiangliang Zhang. Springer Science and Business Media Deutschland GmbH, 2020. p. 31-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12487 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Han, Weijie

AU - Xue, Jingfeng

AU - Zhang, Fuquan

AU - Sun, Zhonghui

PY - 2020

Y1 - 2020

N2 - The critical asset data of the Space Tracking Telemetry and Command (TT&C) System plays an important role in fulfilling space missions. According to analyze the current storing methods and disaster recovery requirements of the data, the remote data disaster recovery techniques are studied based on the remote replication capability of the Oracle database, and the remote data disaster recovery plan is developed for the space TT&C system. Furthermore, the experiment is conducted to validate the plan by building a simulation environment. The experiment results demonstrate that the plan can reach the fifth degree of the disaster recovery level and satisfy the following three performance requirements including recoverability, reliability and real-time performance, and therefore realize remote disaster recovery of the critical asset data for the space TT&C system efficiently.

AB - The critical asset data of the Space Tracking Telemetry and Command (TT&C) System plays an important role in fulfilling space missions. According to analyze the current storing methods and disaster recovery requirements of the data, the remote data disaster recovery techniques are studied based on the remote replication capability of the Oracle database, and the remote data disaster recovery plan is developed for the space TT&C system. Furthermore, the experiment is conducted to validate the plan by building a simulation environment. The experiment results demonstrate that the plan can reach the fifth degree of the disaster recovery level and satisfy the following three performance requirements including recoverability, reliability and real-time performance, and therefore realize remote disaster recovery of the critical asset data for the space TT&C system efficiently.

KW - Database disaster recovery

KW - Oracle DataGuard

KW - Oracle GoldenGate

KW - Remote data disaster recovery

KW - The space TT&C system

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AN - SCOPUS:85097170571

SN - 9783030624590

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings

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A2 - Yan, Hongyang

A2 - Yan, Qiben

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PB - Springer Science and Business Media Deutschland GmbH

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ER -

Han W, Xue J, Zhang F, Sun Z. An Effective Remote Data Disaster Recovery Plan for the Space TT&C System. In Chen X, Yan H, Yan Q, Zhang X, editors, Machine Learning for Cyber Security - Third International Conference, ML4CS 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 31-41. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-62460-6_4

An Effective Remote Data Disaster Recovery Plan for the Space TT&C System

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Keywords

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