Abstract
There has been increasing complexity of cloud infrastructure to sustain the growth of enterprise applications and so as the need to constantly monitor loads and resource utilization. Numerous sophisticated techniques are applied to achieve a unified observation but disparate environments, sources and policies restrain the objective to be achieved using a standard methodology. The paper tries to present a model for standardizing the monitoring platform for applications which are highly environment aware and are restraint by governance using a novel algorithmic approach. The models tries to instrument APIs to monitor single to multitude of parameters to cover the transactions across geography. The model also covers a timeline for evolving big data analytic methods for application performance monitoring systems for environment based applications covering the high data rates and computation requirements. The concept of Data Lake brings a unique dimension to the model and resource utilization and performance metrics for varied workloads and also configuration complexities.
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
References
Page, A., et al.: Cloud-based secure health monitoring: optimizing fully-homomorphic encryption for streaming algorithms. In: 2014 IEEE Globecom Workshops (GC Wkshps). IEEE (2014)
Jia, Z., et al.: Characterizing data analysis workloads in data centers. In: 2013 IEEE International Symposium on Workload Characterization (IISWC). IEEE (2013)
Kononenko, O., et al.: Mining modern repositories with Elasticsearch. In: Proceedings of 11th Working Conference on Mining Software Repositories. ACM (2014)
Turnbull, J.: The Logstash Book. James Turnbull (2013)
Gupta, Y.: Kibana Essentials. Packt Publishing Ltd, Birmingham (2015)
Casalicchio, E., Colajanni, M.: A client-aware dispatching algorithm for web clusters providing multiple services. In: Proceedings of 10th International Conference on World Wide Web. ACM (2001)
Reelsen, A.: Using Elasticsearch, Logstash and Kibana to create realtime dashboards (2014). Dostupné z: https://secure.trifork.com/dl/goto-berlin-2014/GOTO_Night/logstash-kibana-intro.pdf
Dasgupta, S.S., Mahanta, P., Pradeep, S., Subramanian, G.: Reporting optimizations with bill of materials hierarchy traversal in in-memory database domain using set oriented technique. In: Meersman, R., et al. (eds.) OTM 2014. LNCS, vol. 8842, pp. 91–95. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45550-0_13
Mahanta, P., Jain, S.: Determination of manufacturing unit root-cause analysis based on conditional monitoring parameters using in-memory paradigm and data-hub rule based optimization platform. In: Ciuciu, I., et al. (eds.) OTM 2015. LNCS, vol. 9416, pp. 41–48. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26138-6_6
Burzacca, P., Paternò, F.: Analysis and visualization of interactions with mobile web applications. In: Kotzé, P., Marsden, G., Lindgaard, G., Wesson, J., Winckler, M. (eds.) INTERACT 2013. LNCS, vol. 8120, pp. 515–522. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40498-6_40
Tang, D., Stolte, C., Bosch, R.: Design choices when architecting visualizations. Inf. Vis. 3(2), 65–79 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Mahanta, P., Pandey, H. (2017). Big Data Concept to Address Performance Aware Infrastructure Monitoring Challenge for Hybrid Cloud. In: Ciuciu, I., et al. On the Move to Meaningful Internet Systems: OTM 2016 Workshops. OTM 2016. Lecture Notes in Computer Science(), vol 10034. Springer, Cham. https://doi.org/10.1007/978-3-319-55961-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-55961-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55960-5
Online ISBN: 978-3-319-55961-2
eBook Packages: Computer ScienceComputer Science (R0)