Abstract
Network resilience represents one of the major requirements of next generation networks. It refers to an increased level of availability, which is of high importance especially for certain critical services. In this work, we argue for resilience as an intrinsic feature that spans multiple network domains, thereby providing a network-wide failsafe operation. Particular focus is put on virtualized architectures envisioned for 5G and beyond. Contrary to traditional architectures where all network functions were hardware-dependent, a virtualized architecture allows a portion of such functions to run in virtualized environment, i.e., in a telco cloud, allowing thus for a wider deployment flexibility. Nonetheless, parts of this architecture such as radio access might still have strong hardware dependency due to, for instance, performance requirements of the physical nature of the network elements. Capitalizing on this architecture, we shed light onto the techniques designed to guarantee resilience at the radio access as well as the telco cloud network domains. Moreover, we highlight the ability of the envisioned architecture to address security-related issues by applying threat monitoring and prevention mechanisms, along with proper reaction approaches that isolate security intrusions to limited zones.
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Notes
- 1.
We adopt “Option 2” from the candidate function split options provided in [16] since it is the most suitable to the use of data duplication.
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Acknowledgment
This work has been performed in the framework of the H2020-ICT-2016-2 project 5G-MoNArch. The authors would like to acknowledge the contributions of their colleagues. This information reflects the consortiums view, but the consortium is not liable for any use that may be made of any of the information contained therein.
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Michalopoulos, D.S., Gajic, B., Gallego-Nicasio Crespo, B., Gopalasingham, A., Belschner, J. (2018). Network Resilience in Virtualized Architectures. In: Auer, M., Tsiatsos, T. (eds) Interactive Mobile Communication Technologies and Learning. IMCL 2017. Advances in Intelligent Systems and Computing, vol 725. Springer, Cham. https://doi.org/10.1007/978-3-319-75175-7_81
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