Analytical Modeling of Switching Fabrics of Elastic Optical Networks

Głąbowski, Mariusz; Leitgeb, Erich; Sobieraj, Maciej; Stasiak, Maciej

doi:10.1109/access.2020.3033186

Cited by 7 publications

(8 citation statements)

References 46 publications

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“…More specifically, they require a single RRU (from the RRH that will serve them) and a single CRU from the BBU pool. However, in contemporary networks it is essential, for network planning, to consider a multiservice environment where MUs generate calls of various resource requirements [34][35][36][37][38][39][40][41][42][43][44][45][46]. In [45], we introduced two such multirate loss models for the C-RAN architecture and named them multi-class single-cluster and multiclass multi-cluster models (MC-SC and MC-MC, respectively).…”

Section: Introductionmentioning

confidence: 99%

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

et al. 2022

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In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units (RUs) and are separated from the remote radio heads (RRHs). The RRHs are grouped into clusters based on their capacity in radio RUs. Each RRH serves different service-classes whose calls have different requirements in terms of radio and computational RUs and follow a compound Poisson process. This means that calls arrive in batches while each batch of calls follows a Poisson process. If the RUs’ requirements of an arriving call are met, then the call is accepted in the serving RRH for an exponentially distributed service time. Otherwise, call blocking occurs. We initially start our analysis with a single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time and call congestion probabilities via a convolution algorithm. Furthermore, the previous model is extended to include the more complex case of many clusters of RRHs.

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Section: Introductionmentioning

confidence: 99%

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

et al. 2022

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“…To use the resources of EONs effectively, it is necessary to make use of optical switching networks that are capable of converting and transmitting optical signals in different lengths of light waves. One of the most common switching networks extensively employed in EONs are 3-stage W-S-W (wavelength-space-wavelength) switching networks [9], [17]- [20]. The switches of the first and the third stage allow both the frequency slot (wavelength) and the output of a switch (optical fiber) to be changed.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…[23], [24]. The first attempt to take into consideration the limitations in the occupation of frequency slots was undertaken in [25] and [20]. The aforementioned works apply the concept of virtual resources that define the possibility of setting up connections in a number of adjacent FSUs of the connection path in an EON.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Then, on the basis of the occupancy distribution, it is possible to determine the blocking probability for calls of particular traffic classes. The analytical model of a single link proposed by the authors will also be used in the future to increase the accuracy of the already proposed methods for determining the blocking probability in EON nodes [20].…”

Section: Introduction and Related Workmentioning

confidence: 99%

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A Multi-Service Model of Resources With the Neighboring Choice of Allocation Units

2021

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This article discusses a discretized model of resources to which a mixture of multi-service traffic streams is offered and the connections of individual traffic classes that require a large number of allocation units are always executed using adjacent allocation units. The necessity of occupying the adjacent allocation units is typical, for example, for elastic optical networks, in which these units are termed frequency slots. The present article proposes a new occupancy distribution specially designed for these systems. The new occupancy distribution discussed in the article is based on the approximation of the service process by a reversible Markov process. A particular attention is given to the method for the calculations of the so-called conditional transition probability of the transitions between neighboring states in the Markov process. The analytical results of the modeling are compared with the results of simulations of corresponding network systems.

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“…However, in modern networks it is significant to be able to study a multiservice environment where calls need more resources. The analysis at call-level of such contemporary networks is essential in network planning procedures [31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

et al. 2021

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In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units and are separated from the remote radio heads (RRHs). Based on their radio capacity, the RRHs may form one or many clusters: a single cluster when all RRHs have the same capacity and multi-clusters where RRHs of the same radio capacity are grouped in the same cluster. Each RRH services the so-called multiservice traffic, i.e., calls from many service classes with various radio and computational resource requirements. Calls arrive in the RRHs according to a quasi-random process. This means that new calls are generated by a finite number of mobile users. Arriving calls require simultaneously computational and radio resource units in order to be accepted in the system, i.e., in the serving RRH. If their requirements are met, then these calls are served in the (serving) RRH for a service time which is generally distributed. Otherwise, call blocking occurs. We start with the single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time congestion probabilities via a convolution algorithm whose accuracy is validated with the aid of simulation. Furthermore, the previous model is generalized to include the more complex case of more than one clusters.

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Analytical Modeling of Switching Fabrics of Elastic Optical Networks

Cited by 7 publications

References 46 publications

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

A Multi-Service Model of Resources With the Neighboring Choice of Allocation Units

Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

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