Abstract
With the exponential growth of information technology, the amount of data to be processed is also increased enormously. Managing such a huge data has emerged as the “Big Data storage issue”, which can only be addressed with new computing paradigms and platforms. Hadoop Distributed File System (HDFS), the principal component of Hadoop, has been evolved to provide the storage service in the vicinity of Big Data paradigm. Although, several studies have been conducted on HDFS few works focus on the storage service dependability analysis of HDFS. This work aims to develop a mathematical model to represent the storage service activities of HDFS and formulates its dependability attributes. To achieve this, a stochastic Petri net (SPN) based modeling technique is put forward. The proposed model accurately quantify two important dependability metrics namely storage service reliability and availability of HDFS.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
To obtain the RG, we simulate the proposed GSPNs model in PIPEv4.3.0 tool, for detail visit: http://pipe2.sourceforge.net/.
References
Ajmone Marsan, M., Conte, G., Balbo, G.: A class of generalized stochastic Petri nets for the performance evaluation of multiprocessor systems. ACM Trans. Comput. Syst. (TOCS) 2(2), 93–122 (1984)
Apache Hadoop: Hadoop Distributed File System (HDFS) (2017). http://hadoop.apache.org/. Available via Internet Source. Cited 22 Mar 2018
Apache Hadoop: HDFS Federation (2017). https://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/Federation.html. Available via Internet Source. Cited 22 Mar 2018
Bruneo, D.: A stochastic model to investigate data center performance and QoS in IaaS cloud computing systems. IEEE Trans. Parallel Distrib. Syst. 25(3), 560–569 (2014)
Bruneo, D., Longo, F., Hadas, D., Kolodner, E.K.: Analytical investigation of availability in a vision cloud storage cluster. Scalable Comput. Pract. Exp. 14(4), 279–290 (2014)
Bruneo, D., Longo, F., Hadas, D., Kolodner, H.: Availability assessment of a vision cloud storage cluster. In: European Conference on Service-Oriented and Cloud Computing, pp. 71–82. Springer (2013)
Dantas, J., Matos, R., Araujo, J., Maciel, P.: Models for dependability analysis of cloud computing architectures for eucalyptus platform. Int. Trans. Syst. Sci. Appl. 8, 13–25 (2012)
Elsayed, E.A.: Reliability Engineering, vol. 88. Wiley (2012)
Ghemawat, S., Gobioff, H., Leung, S.T.: The google file system. In: Proceedings of 19th ACM symposium on Operating systems principles (SIGOPS), vol. 37. ACM (2003)
Ghosh, R., Longo, F., Frattini, F., Russo, S., Trivedi, K.S.: Scalable analytics for IaaS cloud availability. IEEE Trans. Cloud Comput. 2(1), 57–70 (2014)
Li, H., Zhao, Z., He, L., Zheng, X.: Model and analysis of cloud storage service reliability based on stochastic petri nets. J. Inf. Comput. Sci. 11(7), 2341–2354 (2014)
Lin, C., Marinescu, D.C.: Stochastic high-level Petri nets and applications. In: High-Level Petri Nets, pp. 459–469. Springer (1988)
Longo, F., Ghosh, R., Naik, V.K., Trivedi, K.S.: A scalable availability model for infrastructure-as-a-service cloud. In: 41st International Conference on Dependable Systems & Networks (DSN), pp. 335–346. IEEE (2011)
McKusick, K., Quinlan, S.: GFS: evolution on fast-forward. Commun. ACM 53(3), 42–49 (2010)
MooseFS: Can Petabyte Storage be super efficient (2017). https://moosefs.com/. Available via Internet Source. Cited 22 Mar 2018
Rodeh, O., Teperman, A.: zFS-a scalable distributed file system using object disks. In: Proceedings of 20th Conference on Mass Storage Systems and Technologies (MSST), pp. 207–218. IEEE (2003)
Shafer, J., Rixner, S., Cox, A.L.: The hadoop distributed filesystem: Balancing portability and performance. In: International Symposium on Performance Analysis of Systems & Software (ISPASS), pp. 122–133. IEEE (2010)
Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10. IEEE (2010)
Wang, J.: Petri nets for dynamic event-driven system modeling. Handb. Dyn. Syst. Model. 1 (2007)
Wu, X., Liu, Y., Gorton, I.: Exploring performance models of hadoop applications on cloud architecture. In: 11th International ACM SIGSOFT Conference on Quality of Software Architectures (QoSA), pp. 93–101. IEEE (2015)
Zeng, R., Jiang, Y., Lin, C., Shen, X.: Dependability analysis of control center networks in smart grid using stochastic petri nets. IEEE Trans. Parallel Distrib. Syst. 23(9), 1721–1730 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chattaraj, D., Sarma, M., Samanta, D. (2019). Stochastic Petri Net Based Modeling for Analyzing Dependability of Big Data Storage System. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-13-1498-8_42
Download citation
DOI: https://doi.org/10.1007/978-981-13-1498-8_42
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1497-1
Online ISBN: 978-981-13-1498-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)