TY - JOUR
T1 - A fluid model for a relay node in an ad hoc network : evaluation of resource sharing policies
AU - Mandjes, M.R.H.
AU - Scheinhardt, W.R.W.
PY - 2008
Y1 - 2008
N2 - Fluid queues offer a natural framework for analyzing waiting times in a relay node of an ad hoc network. Because of the resource sharing policy applied, the input and output of these queues are coupled. More specifically, when there are n users who wish to transmit data through a specific node, each of them obtains a share 1/(n+w) of the service capacity to feed traffic into the queue of the node, whereas the remaining fraction w/(n+w) is used to serve the queue; here w>0 is a free design parameter. Assume now that jobs arrive at the relay node according to a Poisson process, and that they bring along exponentially distributed amounts of data. The case w=1 has been addressed before; the present paper focuses on the intrinsically harder case w>1, that is, policies that give more weight to serving the queue. Four performance metrics are considered: (i) the stationary workload of the queue, (ii) the queueing delay, that is, the delay of a "packet" (a fluid particle) that arrives at an arbitrary point in time, (iii) the flow transfer delay, (iv) the sojourn time, that is, the flow transfer time increased by the time it takes before the last fluid particle of the flow is served. We explicitly compute the Laplace transforms of these random variables.
AB - Fluid queues offer a natural framework for analyzing waiting times in a relay node of an ad hoc network. Because of the resource sharing policy applied, the input and output of these queues are coupled. More specifically, when there are n users who wish to transmit data through a specific node, each of them obtains a share 1/(n+w) of the service capacity to feed traffic into the queue of the node, whereas the remaining fraction w/(n+w) is used to serve the queue; here w>0 is a free design parameter. Assume now that jobs arrive at the relay node according to a Poisson process, and that they bring along exponentially distributed amounts of data. The case w=1 has been addressed before; the present paper focuses on the intrinsically harder case w>1, that is, policies that give more weight to serving the queue. Four performance metrics are considered: (i) the stationary workload of the queue, (ii) the queueing delay, that is, the delay of a "packet" (a fluid particle) that arrives at an arbitrary point in time, (iii) the flow transfer delay, (iv) the sojourn time, that is, the flow transfer time increased by the time it takes before the last fluid particle of the flow is served. We explicitly compute the Laplace transforms of these random variables.
U2 - 10.1155/2008/518214
DO - 10.1155/2008/518214
M3 - Article
SN - 1048-9533
VL - 2008
SP - 518214-1/25
JO - Journal of Applied Mathematics and Stochastic Analysis
JF - Journal of Applied Mathematics and Stochastic Analysis
M1 - 518214
ER -