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Inter-correlation of Resource-/Flow-Level Visibility for APM Over OF@TEIN SDN-Enabled Multi-site Cloud

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Quality, Reliability, Security and Robustness in Heterogeneous Networks (QShine 2016)

Abstract

Cloud computing and SDN technologies have potential to bring advanced capabilities to data centers and inter-connecting networks by maximizing resources utilization. However, SDN/Cloud composition also brings monitoring and visibility challenges, for operators to diagnose p+vResources (server, network and storage), flows and cloud based applications for end-to-end performance management, because legacy tools lack adequate visibility support for such kind of dynamic environments. In this paper, by developing and integrating open source tools, we present a unified visibility solution to inter-correlate Resource-/Flow-level visibility over OF@TEIN to assist cloud-based APM (Application Performance Management). Furthermore, a verification environment has been setup, to show the feasibility of our inter-correlation approach.

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Acknowledgment

This work was supported in part by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R7117-16-0218, Development of Automated SaaS Compatibility Techniques over Hybrid/Multisite Clouds). This work was also supported in part by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. B190-15-2012, Global SDN/NFV Open-Source Software Core Module/Function Development).

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Correspondence to Muhammad Usman .

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Usman, M., Risdianto, A.C., Han, J., Kim, J. (2017). Inter-correlation of Resource-/Flow-Level Visibility for APM Over OF@TEIN SDN-Enabled Multi-site Cloud. In: Lee, JH., Pack, S. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-319-60717-7_48

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  • DOI: https://doi.org/10.1007/978-3-319-60717-7_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60716-0

  • Online ISBN: 978-3-319-60717-7

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