textabstractHighly-aggregated traffic in communication networks is often modeled as fractional Brownian motion (fBm). This is justified by the theoretical result that the sum of a large number of on-off inputs, with either on-times or off-times having a heavy-tailed distribution with infinite variance, converges to fBm, after rescaling time appropriately. For performance analysis purposes, the key question is whether this convergence carries over to the stationary buffer content process. In this paper it is shown that, in a heavy-traffic queueing environment, this property indeed hold
Cumulative broadband network traffic is often thought to be well modeled by fractional Brownian moti...
We show that, contrary to the common wisdom, the workload process in a fluid queue with a cluster Po...
We model the workload of a network device responding to a random flux of work requests with various ...
The presence of long-range dependence in broadband network and of self-similar traf-fic patterns in ...
Gaussian processes are a powerful tool in networkmodeling since they permit to capture the longmemor...
AbstractWe consider a family of non-deterministic fluid models that can be approximated under heavy ...
It has become common practice to use heavy-tailed distributions in order to describe the variations ...
Cumulative broadband network traffic is often thought to be well modeled by fractional Brownian moti...
Consider a queue with a stochastic fluid input process modeled as fractional Brownian motion (fBM). ...
Empirical studies of data traffic in high-speed networks suggest that network traffic exhibits self-...
Cumulative broadband network traffic is often thought to be wellmodeled by fractional Brownian motio...
The result provided in this paper helps complete a unified picture of the scaling behavior in heavy-...
This paper shows that fractional Brownian motion with H < 1=2 can arise as a limit of a simple cl...
The Fractional Brownian motion (fBm) traffic model is important because it captures the self-similar...
We study the heavy traffic regime of a discrete-time queue driven by correlated inputs, namely the M...
Cumulative broadband network traffic is often thought to be well modeled by fractional Brownian moti...
We show that, contrary to the common wisdom, the workload process in a fluid queue with a cluster Po...
We model the workload of a network device responding to a random flux of work requests with various ...
The presence of long-range dependence in broadband network and of self-similar traf-fic patterns in ...
Gaussian processes are a powerful tool in networkmodeling since they permit to capture the longmemor...
AbstractWe consider a family of non-deterministic fluid models that can be approximated under heavy ...
It has become common practice to use heavy-tailed distributions in order to describe the variations ...
Cumulative broadband network traffic is often thought to be well modeled by fractional Brownian moti...
Consider a queue with a stochastic fluid input process modeled as fractional Brownian motion (fBM). ...
Empirical studies of data traffic in high-speed networks suggest that network traffic exhibits self-...
Cumulative broadband network traffic is often thought to be wellmodeled by fractional Brownian motio...
The result provided in this paper helps complete a unified picture of the scaling behavior in heavy-...
This paper shows that fractional Brownian motion with H < 1=2 can arise as a limit of a simple cl...
The Fractional Brownian motion (fBm) traffic model is important because it captures the self-similar...
We study the heavy traffic regime of a discrete-time queue driven by correlated inputs, namely the M...
Cumulative broadband network traffic is often thought to be well modeled by fractional Brownian moti...
We show that, contrary to the common wisdom, the workload process in a fluid queue with a cluster Po...
We model the workload of a network device responding to a random flux of work requests with various ...