Sample-path large deviations for tandem and priority queues with Gaussian inputs

This paper considers Gaussian flows multiplexed in a queueing network. A single node being a useful but often incomplete setting, we examine more advanced models. We focus on a (two-node) tandem queue, fed by a large number of Gaussian inputs. With service rates and buffer sizes at both nodes scaled appropriately, Schilder's sample-path large-deviations theorem can be applied to calculate the asymptotics of the overflow probability of the second queue. More specifically, we derive a lower bound on the exponential decay rate of this overflow probability and present an explicit condition for the lower bound to match the exact decay rate. Examples show that this condition holds for a broad range of frequently used Gaussian inputs. The last part of the paper concentrates on a model for a single node, equipped with a priority scheduling policy. We show that the analysis of the tandem queue directly carries over to this priority queueing system.Comment: Published at http://dx.doi.org/10.1214/105051605000000133 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Resource dimensioning through buffer sampling

Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship among the traffic offered (in terms of the mean offered load , but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulas that estimate the required capacity $c$ as a function of the input traffic and the performance target. For the special case of Gaussian input traffic, these formulas reduce to $c = M + \alpha V$, where directly relates to the performance requirement (as agreed upon in a service level agreement) and $V$ reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level, the Gaussianity assumption is justified.\ud As estimating $M$ is relatively straightforward, the remaining open issue concerns the estimation of $V$. We argue that particularly if corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of $V$ is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate

Resource dimensioning through buffer sampling

Link dimensioning, i.e., selecting a (minimal) link capacity such that the users’ performance requirements are met, is a crucial component of network design. It requires insight into the interrelationship between the traffic offered (in terms of the mean offered load M, but also its fluctuation around the mean, i.e., ‘burstiness’), the envisioned performance level, and the capacity needed. We first derive, for different performance criteria, theoretical dimensioning formulae that estimate the required capacity C as a function of the input traffic and the performance target. For the special case of Gaussian input traffic these formulae reduce to C = M+V , where directly relates to the performance requirement (as agreed upon in a service level agreement) and V reflects the burstiness (at the timescale of interest). We also observe that Gaussianity applies for virtually all realistic scenarios; notably, already for a relatively low aggregation level the Gaussianity assumption is justified.\ud As estimating M is relatively straightforward, the remaining open issue concerns the estimation of V . We argue that, particularly if V corresponds to small time-scales, it may be inaccurate to estimate it directly from the traffic traces. Therefore, we propose an indirect method that samples the buffer content, estimates the buffer content distribution, and ‘inverts’ this to the variance. We validate the inversion through extensive numerical experiments (using a sizeable collection of traffic traces from various representative locations); the resulting estimate of V is then inserted in the dimensioning formula. These experiments show that both the inversion and the dimensioning formula are remarkably accurate

Large deviations for complex buffer architectures: the short-range dependent case

This paper considers Gaussian flows multiplexed in a queueing network, where the underlying correlation structure is assumed to be short-range dependent. Whereas previous work mainly focused on the FIFO setting, this paper addresses overflow characteristics of more complex buffer architectures. We subsequently analyze the tandem queue, a priority system, and generalized processor sharing. In a many-sources setting, we explicitly compute the exponential decay rate of the overflow probability. Our study relies on large-deviations arguments, e.g., Schilder's theore

Sample-path large deviations for tandem and priority queues with Gaussian inputs

This paper considers Gaussian flows multiplexed in a queueing network. A single node being a useful but often incomplete setting, we examine more advanced models. We focus on a (two-node) tandem queue, fed by a large number of Gaussian inputs. With service rates and buffer sizes at both nodes scaled appropriately, Schilder's sample-path large deviations theorem can be applied to calculate the asymptotics of the overflow probability of the second queue. More specifically, we derive a lower bound on the exponential decay rate of this overflow probability and present an explicit condition for the lower bound to match the exact decay rate. Examples show that this condition holds for a broad range of frequently-used Gaussian inputs. The last part of the paper concentrates on a model for a single node, equipped with a priority scheduling policy. We show that the analysis of the tandem queue directly carries over to this priority queueing system. iffalse {it Perhaps:} We conclude by presenting a number of motivated conjectures for the analysis of a queue operating under the generalized processor sharing discipline

Survey of traffic control schemes and error control schemes for ATM networks

Among the techniques proposed for B-ISDN transfer mode, ATM concept is considered to be the most promising transfer technique because of its flexibility and efficiency. This paper surveys and reviews a number of topics related to ATM networks. Those topics cover congestion control, provision of multiple classes of traffic, and error control. Due to the nature of ATM networks, those issues are far more challenging than in conventional networks. Sorne of the more promising solutions to those issues are surveyed, and the corresponding results on performance are summarized. Future research problems in ATM protocol aspect are also presented

Sample-path large deviations for generalized processor sharing queues with Gaussian inputs

In this paper we consider the Generalized Processor Sharing (GPS) mechanism serving two traffic classes. These classes consist of a large number of independent identically distributed Gaussian flows with stationary increments. We are interested in the logarithmic asymptotics or exponential decay rates of the overflow probabilities. We first derive both an upper and a lower bound on the overflow probability. Scaling both the buffer sizes of the queues and the service rate with the number of sources, we apply Schilder's sample-path large deviations theorem to calculate the logarithmic asymptotics of the upper and lower bound. We discuss in detail the conditions under which the upper and lower bound match. Finally we show that our results can be used to choose the values of the GPS weights. The results are illustrated by numerical examples