Abstract
Software-Defined Networking (SDN) is an emerging new norm for networks which deals heavily on dynamic nature of higher bandwidth network applications to address the service velocity. SDN’s features such as dynamic configuration of network elements, allowing appropriate open standards and centrally controllable tasks will make it suitable to introduce new applications for communication networks. In particular, by considering the voluminous telecom subscriber’s transactions and supporting high performing applications in the event of network element failure, a manual Intervention is required for tuning of network elements. Even the recently introduced network resources for tuning could also fail due to unexpected flow of traffic without considering the exact load features dynamically. Any sudden failure in network functioning could bring huge revenue loss and also reduces considerable Quality of Experience of the service provider. In this paper, we have presented use cases relevant to Online Charging System (OCS) that highlight the integration of SDN with communication networks for managing optimized network utilization. Moreover, SDN with the use of machine learning techniques will take the proactive measure before the network node goes down. To support this feature of SDN, we have proposed Autonomous Resource Monitoring and Deployer application that monitors the continuous traffic flow in OCS, intelligently reroutes the traffic with the use of SDN controller by introducing new resources. Our approach handles specific network key performance indicators dynamically, which reduces maintenance and Operational Expenditure costs.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Saravanan, M., Sundaramurthy, A.S., Sundar, D., Hiba Sadia, K. (2016). Extending SDN Framework for Communication Networks. In: Mandler, B., et al. Internet of Things. IoT Infrastructures. IoT360 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 170. Springer, Cham. https://doi.org/10.1007/978-3-319-47075-7_58
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DOI: https://doi.org/10.1007/978-3-319-47075-7_58
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