The statistical characteristics of network traffic- in particular the observation that it can exhibit long range dependence- have received considerable attention from the research community over the past few years. In addition, the recent claims that the TCP protocol can generate traffic with long rage dependent behavior has also received much attention. Contrary to the latter claims, in this paper we show that the TCP protocol can generate traffic with correlation structures that spans only an analytically predictable finite range of time-scales. We identify and analyze separately the two mechanisms within TCP that are responsible for this scaling be-havior: timeouts and congestion avoidance. We provide analytical models for both mechanisms that, under the proper loss probabilities, accurately predict the range in time-scales and the strength of the sustained correlation structure of the traffic sending rate of a single TCP source. We also analyze an existing comprehensive model of TCP that accounts for both mechanisms and show that TCP itself exhibits a predictable finite range of time-scales under which traffic presents sustained correlations. Our claims and results are derived from Markovian models that are supported by simulations. We note that traffic generated by TCP can be misinterpreted to have long range dependence, but that long range dependence is not possible due to inherent finite time-scales of the mechanisms of TCP.

This is a survey paper on fluid queues, with a strong emphasis on recent attempts to represent phenomena like long-range dependence. The central model of the paper is a fluid queueing system fed by N independent sources that alternate between silence and activity periods. The distribution of the activity periods of at least one source is assumed to be long-tailed, which may give rise to long-range dependence. We consider the effect of this tail behaviour on the steady-state distributions of the buffer content at embedded points in time and at arbitrary time, and on the busy period distribution. Both exact results and bounds are discussed.

. A fluid queue with ON periods arriving according to a Poisson process and having a long--tailed distribution has long range dependence. As a result, its performance deteriorates. The extent of this performance deterioration depends on a quantity determined by the average values of the system parameters. In the case when the the performance deterioration is the most extreme, we quantify it by studying the time until the amount of work in the system causes an overflow of a large buffer. This turns out to be strongly related to the tail behavior of the increase in the buffer content during a busy period of the M=G=1 queue feeding the buffer. A large deviation approach provides a powerful method of studying such tail behavior. 1. Introduction We consider the following fluid queuing model. Sessions arrive to a network server (multiplexer) according to a Poisson process with rate ? 0. Each session remains active for a random length of time with distribution F and a finite mean ¯. We assum...

Consider a single server queue with i.i.d. arrival and service processes, {A, An, n � 0} and {C, Cn, n � 0}, respectively, and a finite buffer B. The queue content process {Q B n, n � 0} is recursively defined as Q B n+1 = min((Q B n + An+1 − Cn+1) +, B), q + = max(0, q). When E(A − C) < 0, and A has a subexponential distribution, we show that the stationary expected loss rate for this queue E(Q B n + An+1 − Cn+1 − B) + has the following explicit asymptotic characterization: E(Q B n + An+1 − Cn+1 − B) + ∼ E(A − B) + as B →∞, independently of the server process Cn. For a fluid queue with capacity c, M/G/ ∞ arrival process At, characterized by intermediately regularly varying on periods τ on, which arrive with Poisson rate Λ, the average loss rate λ B loss satisfies λ B loss ∼ Λ E(τ on η − B) + as B →∞, where η = r + ρ − c, ρ = EAt <c; r (c � r) is the rate at which the fluid is arriving during an on period. Accuracy of the above asymptotic relations is verified with extensive numerical and simulation experiments. These explicit formulas have potential application in designing communication networks that will carry traffic with long-tailed characteristics, e.g., Internet data services.

Consider a network multiplexer with a finite buffer fed by a superposition of independent heterogeneous On-Off sources. An On-Off source consists of a sequence of alternating independent activity and silence periods. During its activity period a source produces fluid with constant rate. For this system, under the assumption that the residual activity periods are intermediately regularly varying, we derive explicit and asymptotically exact formulas for approximating the stationary overflow probability and loss rate. The derived asymptotic formulas, in addition to their analytical tractability, exhibit excellent quantitative accuracy, which is illustrated by a number of simulation experiments. We demonstrate through examples how these results can be used for efficient computing of capacity regions for network switching elements. Furthermore, the results provide important insight into qualitative tradeoffs between the overflow probability, offered traffic load, available capacity, and buffer space. Overall, they provide a new set of tools for designing and provisioning of networks with heavytailed traffic streams. Keywords---Network multiplexer, Finite buffer fluid queue, On-Off process, Heavy-tailed distributions, Subexponential distributions, Long-range dependence I.

. We consider a fluid queue with sessions arriving according to a Poisson process. A long--tailed distribution of session lengths induces long range dependence in the system and causes its performance to deteriorate. The deterioration is due to occurrence of load regimes far from average ones. Nonetheless, the extent of this performance deterioration is shown to depend crucially on the average values of the system parameters. 1. Introduction We consider the following fluid queuing model. Sessions (ON periods) are initiated at a network server or multiplexer according to a Poisson process with rate ? 0. Each session is active for a random length of time with distribution F and a finite mean ; during this time it generates network traffic at unit rate. We assume that the lengths of different sessions are independent, and they are also independent of the Poisson arrival process. The service rate is r ? 0 units of traffic per unit time. If X(t) denotes the amount of work (measured in unit...

We consider a fluid queue fed by a superposition of n homogeneous on-off sources with generally distributed on- and off-periods. We scale buffer space B and link rate C by n, such that we get nb and nc, respectively. Then we let n grow large. In this regime, the overflow probability decays exponentially in the number of sources n; we specifically examine the situation in which also b is large. We explicitly compute asymptotics for the case in which the on-periods have a subexponential distribution, e.g., Pareto, Lognormal, or Weibull. We provide a detailed interpretation of our results. Crucial is the shape of the function v(t) := -log P(A* > t) for large t, A* being the residual on-period. If v(·) is slowly varying (e.g., Pareto, Lognormal), then, during the trajectory to overflow, the input rate will only slightly exceed the link rate. Consequently, the buffer will fill `slowly', and the typical time to overflow will be `more than linear' in the buffer size. In contrast, if v(·) ...

This paper is another contribution to understanding the effect that long-range dependent traffic can have on the performance of communication networks. Our goal here is to investigate the role of scheduling policies in controlling such effects. We carry out this investigation in the framework of a single server queuing model, described below

In this paper we consider a multi-buffered system consisting of N buffers accessed by heterogeneous longtailed sessions and served according to the Generalized Processor Sharing (GPS) discipline with weights fOE i g. We assume that sessions arrive according to a Poisson process. A session of type i transmits at rate r i and has a duration whose distribution is longtailed of the form P ( i ? t) ff i t \Gamma(1+fi i ) where ff i , fi i ? 0. We obtain the large buffer asymptotics under very general stability hypotheses. In particular we show that recent results on the GPS asymptotics obtained by Borst, Boxma and Jelenkovic can be recovered and there are important cases for which we obtain exact asymptotes for which the previous results do not apply. The methodology exploits the sample-path description of the workload evolution under GPS as well as the marked Poisson structure of the inputs.

Consider a fluid queue with a finite buffer B and capacity c fed by a superposition of N independent On-Off processes. An On-Off process consists of a sequence of alternating independent activity and silence periods. Successive activity, as well as silence, periods are identically distributed. The process is active with probability p on and during its activity period produces fluid with constant rate r. For this queueing system, under the assumption that the residual activity periods are intermediately regularly varying, we derive explicit and asymptotically exact formulas for approximating the stationary loss probability and loss rate. In the case of homogeneous sources with residual activity periods equal in distribution to on r , the queue overflow probability is asymptotically, as B !1, equal to P[Q B = B] = ` N k 0 ' p k 0 on P on r ? B k 0 (r \Gamma ae) +N ae \Gamma c k 0 (1 + o(1)); where ae = rp on , N ae ! c and k 0 is the smallest integer greater than (c...