Dynamic Slicing Approach for Multi-Tenant 5G Transport Networks [Invited]

Raza, Muhammad; Fiorani, Matteo; Rostami, A.; Öhlen, P.; Wosinska, Lena; Monti, Paolo

doi:10.1364/jocn.10.000a77

Cited by 77 publications

(43 citation statements)

References 21 publications

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“…Looking at the literature, many research contributions related to queue systems make use of discrete-event simulation assuming M/M/1 queues [13], where arrivals follow a Poisson distribution and holding times are exponentially distributed. Even recent research works presenting hot topic networking use cases rely on such model [14]. However, these M/M/1 memoryless processes do not fit with the behavior of real dynamic traffic, as suggested in [15]; this can be extended to the traffic generated by 5G services.…”

Section: Introductionmentioning

confidence: 99%

CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis

Ruiz¹,

Coltraro²,

Velasco³

2018

J. Opt. Commun. Netw.

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The rapid availability of new services makes that network operators cannot exhaustively test their impact on the network or anticipate any capacity exhaustion. This situation will be worse with the imminent introduction of the 5G technology and the kind of totally new services that it will support. In addition, the increasing complexity of the network makes unreachable analyzing its behavior in front of the specific traffic that needs to be supported, which prevents from training human operators and much less, machine learning algorithms that might automatize network operation. In this paper, we present CURSA-SQ, a methodology to analyze the network behavior when the specific traffic that would be generated by groups of service consumers is injected. CURSA-SQ includes input traffic flow modelling with second and sub-second granularity based on specific service and consumer behaviors, as well as a continuous G/G/1/k queue model based on the logistic function. The methodology allows to accurately study traffic flows at the input and outputs of complex scenarios with multiples queues systems, as well as other metrics such as delays, while showing noticeable scalability. Application use cases include, packet and optical network planning, service introduction assessment, and autonomic networking, just to mention a few.

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Section: Introductionmentioning

confidence: 99%

CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis

Ruiz¹,

Coltraro²,

Velasco³

2018

J. Opt. Commun. Netw.

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“…In the case of variable service requirements, a static slicing approach may result in the resources overprovisioning. Therefore, dynamic slicing 12 is a more flexible solution.…”

Section: Principles Of Network Slicing In 5g Mobile Systemsmentioning

confidence: 99%

TOPSIS‐based approach for network slice selection in 5G mobile systems

Bakmaz

Bojković

Bakmaz

2020

Int J Communication

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Summary Fifth‐generation (5G) mobile networks can be perceived as highly manageable systems that provide increased performance while supporting a variety of services with widely diverse requirements. Recently, network slicing has been proposed by academia and industry as a resource provisioning technique capable to meet these requirements with reduced operating costs while opening new horizons for network efficiency. The aim of network slice selection function (NSSF) is an optimal selection of network instances serving the users, based on local configuration, and other available information including radio access networks (RANs) performances in the registration area. In this paper, NSSF based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed. Here, the network slices are treated as alternatives while their performance indicators are considered as the criteria for decision making. The influences of various alternative techniques, taking into account all the three stages of decision making process, that is, normalization, weighting, and ranking, are analyzed through the rank reversal phenomenon and computational complexity. Simulation results reveal that the proposed techniques can significantly improve the network slice selection procedure.

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“…The performance of the BDA-based slice admission policy described above is evaluated by applying it to the dynamic slicing approach presented in [1]. We consider a 12-node network topology [8] covering two geo-types (i.e., business and residential) comprising 6 nodes each.…”

Section: Slice Admission Using Big Data Analytics: Use Case Definitiomentioning

confidence: 99%

“…In the 5G paradigm, infrastructure providers (InPs) will have to support a variety of services over a single network infrastructure to improve resource use efficiency and to reduce cost [1]. Software defined networking (SDN) and network function virtualization (NFV) are two promising technologies to enable such a vision [2].…”

Section: Introductionmentioning

confidence: 99%

“…In the presence of temporal variations of such requirements, an InP can provision slices that can be scaled up/down during their service time [1]. In this scenario, the profit of an InP can be maximized by: (i) accepting as many slice requests as possible (i.e., to increase revenue), and (ii) matching the variation of the slice requirements as closely as possible (i.e., to contain penalties proportional to performance degradation, if a slice cannot be scaled up due to resource contention) [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Methods for Slice Admission in 5G Networks

Raza

Natalino

Wosinska

et al. 2019

2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and

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The paper discusses how the slice admission problem can be aided by machine learning strategies. Results show that both supervised and reinforcement learning might lead to profit maximization while containing losses due to performance degradation.Keywords: Data analytics for network control and management in optical core/data center networks; Optical core/metro/data-center network architecture, design, virtualization, slice, control and management

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Dynamic Slicing Approach for Multi-Tenant 5G Transport Networks [Invited]

Cited by 77 publications

References 21 publications

CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis

CURSA-SQ: A Methodology for Service-Centric Traffic Flow Analysis

TOPSIS‐based approach for network slice selection in 5G mobile systems

Machine Learning Methods for Slice Admission in 5G Networks

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