Analysis of component-based approaches toward componentized 5G

5G is expected to be modular by design toward autonomic and agile networks. In this regards, the 5G functional architecture is designed as service-based seeking to support the concept of Network Slicing. This leads us to the question: what componentization approach to implement this modular architecture? Is there a componentization approach that is suitable for all the network functions? Which design approach will help to have autonomic and cognitive networks? In this paper we shed the light on the different component-based approaches. In addition, we reviewed the state of the art addressing the applicability of component-based approaches to build autonomic networks. Therefore, we present discussion, comparison and synthesis as input to 5G related activities

Information model for managing autonomic functions in future networks

Future Internet (FI), a dynamic and complex environment, imposes management requirements, complexity and volume of data which can hardly be handled by traditional management schemes. Autonomic network and service management can be a powerful vision; a promising solution paving the way towards fully autonomic systems provides a three-level management approach and develops Information Modelling extensions for semantic continuity. This paper aims at proposing an Information Model for abstracting autonomic mechanisms for network management tasks and convincing on the relevance of using/extending standardized information models for system specification. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2013

Can machine learning aid in delivering new use cases and scenarios in 5G?

5G represents the next generation of communication networks and services, and will bring a new set of use cases and scenarios. These in turn will address a new set of challenges from the network and service management perspective, such as network traffic and resource management, big data management and energy efficiency. Consequently, novel techniques and strategies are required to address these challenges in a smarter way. In this paper, we present the limitations of the current network and service management and describe in detail the challenges that 5G is expected to face from a management perspective. The main contribution of this paper is presenting a set of use cases and scenarios of 5G in which machine learning can aid in addressing their management challenges. It is expected that machine learning can provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network