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

Buda, Teodora Sandra; Assem, Haytham; Xu, Lei; Raz, Danny; Margolin, Udi; Rosensweig, Elisha J.; López, Diego; Corici, Marius; Smirnov, Michael; Mullins, Robert; Uryupina, Olga; Mozó, Alberto; Ordozgoiti, Bruno; Martín, Ángel; Alloush, Alaa; O’Sullivan, Pat; Yahia, Imen Grida Ben

doi:10.1109/noms.2016.7503003

Cited by 34 publications

(21 citation statements)

References 6 publications

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“…There are a number of fundamental questions on how to obtain efficient resource utilization, guaranteed QoS, and proper isolation between slices. Some works proposed to use artificial intelligence and machine learning to deal with these issues; see, for instance, [45,46]. However, these techniques are unproven and their ability to deal with unforeseen catastrophic events are questioned.…”

Section: Complexitymentioning

confidence: 99%

Orchestration and Control in Software‐Defined 5G Networks: Research Challenges

Nencioni

Garroppo

Gonzalez

et al. 2018

Wireless Communications and Mobile Computing

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The fifth generation (5G) of cellular networks promises to be a major step in the evolution of wireless technology. 5G is planned to be used in a very broad set of application scenarios. These scenarios have strict heterogeneous requirements that will be accomplished by enhancements on the radio access network and a collection of innovative wireless technologies. Softwarization technologies, such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), will play a key role in integrating these different technologies. Network slicing emerges as a cost-efficient solution for the implementation of the diverse 5G requirements and verticals. The 5G radio access and core networks will be based on a SDN/NFV infrastructure, which will be able to orchestrate the resources and control the network in order to efficiently and flexibly and with scalability provide network services. In this paper, we present the up-to-date status of the software-defined 5G radio access and core networks and a broad range of future research challenges on the orchestration and control aspects.

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Section: Complexitymentioning

confidence: 99%

Orchestration and Control in Software‐Defined 5G Networks: Research Challenges

Nencioni

Garroppo

Gonzalez

et al. 2018

Wireless Communications and Mobile Computing

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“…Instead, we argue that a data-driven approach is suitable, with which we consider the entire system as a black box and measure the input and output of the system to understand the system's behavior. We believe that machine learning is a key technology for this data-driven approach [167]. There a few studies in this domain for NFV [168]- [171].…”

Section: Trouble Shootingmentioning

confidence: 99%

Research Challenges for Network Function Virtualization - Re-Architecting Middlebox for High Performance and Efficient, Elastic and Resilient Platform to Create New Services -

Shiomoto

2018

IEICE Trans. Commun.

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SUMMARYToday's enterprise, data-center, and internet-serviceprovider networks deploy different types of network devices, including switches, routers, and middleboxes such as network address translation and firewalls. These devices are vertically integrated monolithic systems. Software-defined networking (SDN) and network function virtualization (NFV) are promising technologies for dis-aggregating vertically integrated systems into components by using "softwarization". Software-defined networking separates the control plane from the data plane of switch and router, while NFV decouples high-layer service functions (SFs) or Network Functions (NFs) implemented in the data plane of a middlebox and enables the innovation of policy implementation by using SF chaining. Even though there have been several survey studies in this area, this area is continuing to grow rapidly. In this paper, we present a recent survey of this area. In particular, we survey research activities in the areas of re-architecting middleboxes, state management, high-performance platforms, service chaining, resource management, and trouble shooting. Efforts in these research areas will enable the development of future virtual-network-function platforms and innovation in service management while maintaining acceptable capital and operational expenditure. key words: network function virtualization, software-defined networking, service chain, policy management, resource management

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“…The management and rapid response to unexpected problems in the network (link failure, congestion, Distributed Denial of Service-DDoS, delay) is fundamental to guarantee QoS/QoE to users. Network intelligence mechanisms are needed in order to resolve/mitigate possible problems, to decrease the service recovery time and the operational costs [14]. Moreover, the use of advanced techniques such as artificial intelligence, data mining or pattern recognition enables proactive and reactive self-management actions capable of preventing potential problems and maintaining the subscribed network services.…”

Section: Self-organized Networkmentioning

confidence: 99%

“…In order to tackle operation and management capabilities and enable ubiquitous connectivity, the research community proposes the introduction of some key technologies, such as SDN [10], NFV [11], Cloud Computing [12], Self-Organized Network (SON) [13] and Machine Learning [14]. SDN is based on the separation of the control plane from the data plane in traditional network devices.…”

mentioning

confidence: 99%

Key Technologies in the Context of Future Networks: Operational and Management Requirements

et al. 2016

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The concept of Future Networks is based on the premise that current infrastructures require enhanced control, service customization, self-organization and self-management capabilities to meet the new needs in a connected society, especially of mobile users. In order to provide a high-performance mobile system, three main fields must be improved: radio, network, and operation and management. In particular, operation and management capabilities are intended to enable business agility and operational sustainability, where the addition of new services does not imply an excessive increase in capital or operational expenditures. In this context, a set of key-enabled technologies have emerged in order to aid in this field. Concepts such as Software Defined Network (SDN), Network Function Virtualization (NFV) and Self-Organized Networks (SON) are pushing traditional systems towards the next 5G network generation.This paper presents an overview of the current status of these promising technologies and ongoing works to fulfill the operational and management requirements of mobile infrastructures. This work also details the use cases and the challenges, taking into account not only SDN, NFV, cloud computing and SON but also other paradigms.

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

Cited by 34 publications

References 6 publications

Orchestration and Control in Software‐Defined 5G Networks: Research Challenges

Orchestration and Control in Software‐Defined 5G Networks: Research Challenges

Research Challenges for Network Function Virtualization - Re-Architecting Middlebox for High Performance and Efficient, Elastic and Resilient Platform to Create New Services -

Key Technologies in the Context of Future Networks: Operational and Management Requirements

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