Analise de desempenho da disciplina de serviço "generalized processor sharing" sob trafego auto-similar

2001 ◽  
Author(s):  
Flavio de Melo Pereira
Keyword(s):  
Processor Sharing ◽  
Generalized Processor Sharing

AnRG-Factorization Approach for a BMAP/M/1 Generalized Processor-Sharing Queue

2005 ◽  
Vol 21 (2-3) ◽  
pp. 507-530 ◽  
Author(s):  
Quan-Lin Li ◽  
Zhaotong Lian ◽  
Liming Liu
Keyword(s):  
Processor Sharing ◽  
Factorization Approach ◽  
Generalized Processor Sharing

Generalized Processor Sharing with Multiple FBM-based Traffic Arrivals

2012 ◽  
Vol 7 (5) ◽  
pp. 129-139
Author(s):  
Changting Lin ◽  
Chuanhuang Li ◽  
Weiming Wang
Keyword(s):  
Processor Sharing ◽  
Generalized Processor Sharing

scholarly journals A note on large-buffer asymptotics for generalized processor sharing with Gaussian inputs

2007 ◽  
Vol 55 (4) ◽  
pp. 251-254 ◽  
Author(s):  
Krzysztof Dębicki ◽  
Michel Mandjes
Keyword(s):  
Processor Sharing ◽  
Generalized Processor Sharing

Analysis of generalized processor-sharing systems with two classes of customers and exponential services

2004 ◽  
Vol 41 (3) ◽  
pp. 832-858 ◽  
Author(s):  
Fabrice Guillemin ◽  
Didier Pinchon
Keyword(s):  
Hilbert Problem ◽  
Probability Distributions ◽  
Probability Generating Function ◽  
Joint Probability ◽  
Processor Sharing ◽  
Generalized Processor Sharing ◽  
Quarter Plane ◽  
Explicit Formulae ◽  
Gps System ◽  
Number Of Customers

We derive in this paper closed formulae for the joint probability generating function of the number of customers in the two FIFO queues of a generalized processor-sharing (GPS) system with two classes of customers arriving according to Poisson processes and requiring exponential service times. In contrast to previous studies published on the GPS system, we show that it is possible to establish explicit expressions for the generating functions of the number of customers in each queue without calling for the formulation of a Riemann–Hilbert problem. We specifically prove that the problem of determining the unknown functions due to the reflecting conditions on the boundaries of the positive quarter plane can be reduced to a Poisson equation. The explicit formulae are then used to derive some characteristics of the GPS system (in particular the tails of the probability distributions of the numbers of customers in each queue).

Generalized processor sharing queues with heterogeneous traffic classes

2003 ◽  
Vol 35 (3) ◽  
pp. 806-845 ◽  
Author(s):  
Sem Borst ◽  
Michel Mandjes ◽  
Miranda van Uitert
Keyword(s):  
Service Differentiation ◽  
Heterogeneous Traffic ◽  
Processor Sharing ◽  
Generalized Processor Sharing ◽  
Integrated Networks ◽  
Fair Queueing ◽  
Constant Rate ◽  
Processor Sharing Queues ◽  
Workload Distribution ◽  
Heavy Tailed

We consider a queue fed by a mixture of light-tailed and heavy-tailed traffic. The two traffic flows are served in accordance with the generalized processor sharing (GPS) discipline. GPS-based scheduling algorithms, such as weighted fair queueing (WFQ), have emerged as an important mechanism for achieving service differentiation in integrated networks. We derive the asymptotic workload behaviour of the light-tailed traffic flow under the assumption that its GPS weight is larger than its traffic intensity. The GPS mechanism ensures that the workload is bounded above by that in an isolated system with the light-tailed flow served in isolation at a constant rate equal to its GPS weight. We show that the workload distribution is, in fact, asymptotically equivalent to that in the isolated system, multiplied by a certain prefactor, which accounts for the interaction with the heavy-tailed flow. Specifically, the prefactor represents the probability that the heavy-tailed flow is backlogged long enough for the light-tailed flow to reach overflow. The results provide crucial qualitative insight in the typical overflow scenario.

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