A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Anuradha, C. B.
- Monetizing SDN: Emerging Business Models
Authors
1 CSI ADM & PMO, Ericsson India Global Services Pvt. Ltd., Pune, Maharashtra, IN
Source
Telecom Business Review, Vol 7, No 1 (2014), Pagination: 16-22Abstract
The networks have become critical component of infrastructure in society, and have evolved over decades, bringing in the complexities with them. However, the new requirements for the networks, such as data center networking, cloud computing, L4+ network services, and so on are finding the traditional networking approaches as huge barriers.The networks are bound to witness disruptive changes in overcoming these barriers.
At this juncture, SDN is emerging and is seen as a way to open up the closed network for innovation. SDN has now become reality and the speculation has ended, with many leading organizations strategizing around SDN.
The current vertically integrated closed telecom networks have opened up with SDN, transforming their business models and bringing in the new customers. The time is ripe, hence, for formulating the new business opportunities. The objective of this paper is to explore the ways to monetize SDN and formulate business models, through the findings by adopting Secondary Research methodology, by exploring SDN business ecosystem, driving factors and value chain. It also discusses the suitable business models that organizations could take forward, by discussing the applicability of business models in other domains to network domain, and formulating business models with Open source.
Keywords
SDN, NFV, Business Models, Open Source, Value Chain, Business Drivers.
References
- Ashton, C. (2013). Data Plane Performance Is Critical for Cost-Effective SDN Implementations. SDN Central.
- Vogel, J. (2013). Software-Defined Networking and Open-Source Business Models. Reliable SDN.
- Kevin Woods, 2013. The Business of Software Defined Networking. Retrieved from http://blogs.cisco.com
- Gupta, l. (2013). SDN: Development, Adoption and Research Trends
- Open Networking Summit. (2013). SDN: Transforming Networking to Accelerate Business Agility
- Yegulalp,S. (2013). Five SDN Benefits Enterprises Should Consider. Network Computing.
- Ortiz, S. (2013). Software defined networking-On the verge of breakthrough. IEEE Computer Society.
- Empowering the Future 5G Networks:An AI Based Approach
Authors
1 Ericsson, IN
Source
Telecom Business Review, Vol 10, No 1 (2017), Pagination: 53-59Abstract
The next telecommunications standard, 5G, envisions that the future networks will support advanced use cases, such as Internet of things while supporting voluminous simultaneous connections with high bandwidth as well as low latency. Further, these 5G deployments will not be static in nature, with new use cases and service requirements evolving in future. Such requirements pose many deployment and operational challenges to MNOs. These use cases would not only require the networks to be aware of connectivity related parameters, but also adapt intelligently based on parameters beyond the network. This requires the 5G networks to be capable of addressing conditions which are not foreseen at the time of designing them. Such capability requirements can be adequately addressed by advances in the field of AI and machine learning. The objective of this paper is to explore ways to leverage AI and machine learning for enhancing the 5G network deployments and operations. This paper attempts to decipher future demands from the 5G networks analyzing specific requirements in the areas of network planning, network operations and network optimization. This paper also discusses the strategic perspective for MNOs to benefit from applications of AI in 5G networks.Keywords
5G, AI, Machine Learning, 5G Challenges, Operations, Network Automation.References
- Pickavet, M., Develder, C., Baert, E., & Demeester, P. (2010). A.I. techniques for planning telecommunication networks. Ghent University.
- Perez-Romero, J. (2015). Artificial Intelligence-based 5g network capacity planning and operation. IEEE.
- Wang, X., Li, X., & Leung, V. C. M. (2015). Artificial intelligence-based techniques for emerging heterogeneous network. IEEE.
- Li, R., Zhao, Z., Zhou, X., & Zhang, H. (2017). Intelligent 5G: When cellular networks meet artificial intelligence. IEEE.
- Naimat, A. (2016). The new artificial intelligence market. Oreilly.
- Riccio, J. (2016). 62% of telecoms using AI by 2018. Infogix.
- Ericsson. 2017. 5G systems - Enabling The Transformation Of Industry And Society.
- Ericsson. (2017). NFV. Retrieved from https://www.ericsson.com/en/networks/topics/nfv
- Ericsson. (2017). Network Slicing. Retrieved from https://www.ericsson.com/en/networks/topics/network-slicing