Scheduling and Control of Queueing Networks

Weiss, Gideon

doi:10.1017/9781108233217

Cited by 8 publications

(3 citation statements)

References 202 publications

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“…The diffusion limits, which are obtained from the convergence in distribution similarly to (18), [61,Sec. 3.1], are…”

Section: G Performance Parameter: Delay δ F (T)mentioning

confidence: 99%

Resource-Efficient QoS-Aware Video Streaming Using UAV-Assisted Networks

Bhar,

Ghosh,

Agrell

2024

IEEE Trans. Cogn. Commun. Netw.

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Emerging video services are associated with stringent quality-of-service (QoS) and high data-rate requirements. Moreover, the presence of data-rate-hungry mobile users in future networks necessitate sophisticated design strategies. The deployment of unmanned aerial vehicle access point (UAP)assisted networks (UANs) has been proposed to ensure high data-rates to mobile users. Moreover, UAPs can be equipped with energy-efficient caches to facilitate video delivery with stringent QoS. However, the mobility of users and UAPs may cause temporal variations in the QoS experienced by users. This paper conducts an extensive performance evaluation of a UAN, by studying the effect of user behavior, mobility of users and UAPs, and a temporal variation of video popularity on the QoS. The QoS is measured in terms of the delay experienced by the users. To that end, a time-dependent queueing model and its associated fluid approximation models are derived, which are illustrated to be reasonably accurate in an appropriate asymptotic regime. A detailed analysis of these models reveals that low delay, i.e., high QoS, can be ensured in UANs. Finally, a reinforcement-learning (RL) approach based on these models is utilized to minimize the number of deployed UAPs and the playout buffer size while guaranteeing a certain QoS.

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“…The diffusion limits, which are obtained from the convergence in distribution similarly to (18), [61,Sec. 3.1], are…”

Section: G Performance Parameter: Delay δ F (T)mentioning

confidence: 99%

Resource-Efficient QoS-Aware Video Streaming Using UAV-Assisted Networks

Bhar,

Ghosh,

Agrell

2024

IEEE Trans. Cogn. Commun. Netw.

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“…Among the most important terms that comprise the queuing theory [24][25][26] are "Customers," which refer to the entity that arrives at the system, such as: Cars waiting at a traffic light, machines waiting to be repaired, airplanes waiting to land, among others. "Arrivals" refers to the number of customers arriving at the service facility.…”

Section: Queuing Theory Kendall's Notationmentioning

confidence: 99%

Minimizing Intersection Waiting Time: Proposal of a Queue Network Model Using Kendall’s Notation in Panama City

Rovetto,

Cruz,

Nuñez

et al. 2023

Applied Sciences

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The paper presents a proposed queuing model based on Kendall’s notation for the intersection of two streets in Panama City (53 East and 56 East). The proposed model is based on a set of traffic lights that controls the flow of vehicles at the intersection according to a predetermined schedule. The model analyzes the stability of the system and simulations are performed to evaluate its performance. The main objective of the paper is to optimize the vehicle flow by minimizing the waiting time for passage. In the study, it was observed that the current traffic light system on Calle 50 (50th Street) is unstable and oversaturated during weekdays, which generates large vehicle queues with no estimated exit times. It was proposed to increase the system capacity to 1300 vehicles per hour to achieve reasonable stability and provide a solution to improve traffic signal timing on 50th Street. The need to increase the system capacity has been demonstrated and an optimal value has been suggested. The evaluation of other models and the use of AI can further strengthen the system and improve the prediction accuracy in different traffic scenarios.

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“…As opposed to that under (A5), much research has gone into finding scheduling policies that stabilize the network. For general MCQN such policies exist, see for example [6,35] and references therein. In the case of generalized Jackson networks (single class meaning that |C j | = 1 for all servers j), under (A5) and (A6) networks are stable, see for example [2].…”

Section: Structural Network Assumptionsmentioning

confidence: 99%

Diffusion parameters of flows in stable multi-class queueing networks

2022

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We consider open multi-class queueing networks with general arrival processes, general processing time sequences and Bernoulli routing. The network is assumed to be operating under an arbitrary work-conserving scheduling policy that makes the system stable. We study the variability of flows within the network. Computable expressions for quantifying flow variability have previously been discussed in the literature. However, in this paper, we shed more light on such analysis to justify the use of these expressions in the asymptotic analysis of network flows. Toward that end, we find a simple diffusion limit for the inter-class flows and establish the relation to asymptotic (co-)variance rates.

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Scheduling and Control of Queueing Networks

Cited by 8 publications

References 202 publications

Resource-Efficient QoS-Aware Video Streaming Using UAV-Assisted Networks

Resource-Efficient QoS-Aware Video Streaming Using UAV-Assisted Networks

Minimizing Intersection Waiting Time: Proposal of a Queue Network Model Using Kendall’s Notation in Panama City

Diffusion parameters of flows in stable multi-class queueing networks

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