Queueing model of a multi-service system with elastic and adaptive traffic

Hanczewski, Sławomir; Stasiak, Maciej; Weissenberg, Joanna

doi:10.1016/j.comnet.2018.09.023

Cited by 23 publications

(22 citation statements)

References 42 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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“…The proper adoption of management mechanisms is of particular importance in networks with limited resources, e.g., in mobile networks. Depending on the properties of the transmitted data stream, it is possible to use several traffic management mechanisms, e.g., threshold and thresholdless compression [5], [6], [7], [8], resource reservation [9], [10], overflow traffic [11], redirections, priorities [12], [13], or queueing [14], [15].…”

Section: Introductionmentioning

confidence: 99%

“…[30] proposed the interpretation of an occupancy distribution, determined in [5], as an occupancy distribution of a multi-service queueing system with stream traffic. In [14] and [15], a model of a queueing system with elastic and adaptive traffic is proposed.…”

Section: Introductionmentioning

confidence: 99%

A Model of a System With Stream and Elastic Traffic

2021

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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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Queueing model of a multi-service system with elastic and adaptive traffic

Cited by 23 publications

References 42 publications

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

A Model of a System With Stream and Elastic Traffic

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

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