A Model of a System With Stream and Elastic Traffic

Hanczewski, Sławomir; Stasiak, Maciej; Weissenberg, Joanna

doi:10.1109/access.2021.3049437

Cited by 18 publications

(13 citation statements)

References 28 publications

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“…As future work, we intend to include in these models the important cases of elastic and adaptive service-classes. The main characteristic of such service-classes is the fact that the bandwidth (resource units) occupied by in-service calls is not constant but may fluctuate between a maximum and a minimum value [59][60][61]. Because of this, the steady-state distribution cannot be described by a PFS [62], a fact that may require additional changes in our convolution algorithms.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

et al. 2022

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“…in order to be accepted in the system. Such extensions have been considered in the literature in wired/wireless networks but not in the case of mobile hotspots [28][29][30][31][32][33][34][35][36].…”

Section: Discussionmentioning

confidence: 99%

Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

Keramidi

Moscholios

Sarigiannidis

2021

Telecom

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In this paper we study a mobility-aware call admission control algorithm in a mobile hotspot. To this end, a vehicle is considered which has an access point with a fixed capacity. The vehicle alternates between stop and moving phases. When the vehicle is in the stop phase, it services new and handover calls by prioritizing them via a probabilistic bandwidth reservation (BR) policy. Based on this policy, new handover calls may enter the reservation space with a predefined probability. When the vehicle is in the moving phase, it services new calls only. In that phase, two different policies are considered: (a) the classical complete sharing (CS) policy, where new calls are accepted in the system whenever there exists available bandwidth, and (b) the probabilistic BR policy. Depending on the selected policy in the moving phase, we propose the probabilistic BR loss model (if the CS policy is selected) and the generalized probabilistic BR loss model (if the probabilistic BR policy is selected). In both stop and moving phases, where the call arrival process is Poisson, calls require a single bandwidth unit in order to be accepted in the system, while the service time is exponentially distributed. To analytically determine call blocking probabilities and the system’s utilization, we propose efficient iterative algorithms based on two-dimensional Markov chains. The accuracy of the proposed algorithms is verified via simulation.

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“…The accuracy of these algorithms was verified via simulation. As a future extension of this paper, we will study either single or multi-cluster C-RAN that accommodate many serviceclasses whose in-service calls may occupy RUs between a minimum and a maximum value, expressing the so called "elastic traffic" [45], [46]. .…”

Section: Discussionmentioning

confidence: 99%

Multiservice Loss Models for Cloud Radio Access Networks

et al. 2021

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In this paper, a cloud radio access network (C-RAN) is considered where the remote radio heads (RRHs) are separated from the baseband units which form a common pool of computational resource units. Depending on their capacity, the RRHs may form one or more clusters. Each RRH accommodates multiservice traffic, i.e., calls from different service-classes with different radio and computational resource requirements. Arriving calls follow a Poisson process and simultaneously require radio and computational resource units in order to be accepted in the serving RRH. If their resource requirements cannot be met then calls are blocked and lost. Otherwise, calls remain in the serving RRH for a generally distributed service time. Assuming the single-cluster C-RAN, we model it as a multiservice loss system, prove that a product form solution exists for the steady-state probabilities and determine call blocking probabilities via an efficient convolution algorithm whose accuracy is validated via simulation. Furthermore, we generalize the previous multiservice loss model by considering the more complex multi-cluster case where RRHs of the same capacity are grouped in different clusters.

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A Model of a System With Stream and Elastic Traffic

Cited by 18 publications

References 28 publications

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

Multiservice Loss Models for Cloud Radio Access Networks

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