Past exam of the mathematics course of the University of Cambridge / 2003 / iii / Paper 79 / 4 / Solution

Consider a stationary nonnegative work-input process in slotted time, with cumulative work $A_n$ over the last $n$ slots and a work-conserving server of capacity $c$ per slot. Assume its past average tends almost surely to $m<c$. The Lindley recursion then defines the finite stationary queue workloadThis is work remaining, rather than the number of customers when customer service requirements vary. With unit work per customer the two notions coincide. Stationarity makes the distribution of a past cumulative window equal to that of a forward window of the same length.

For a parameter $\theta >0$ and a positive window length $n$, the effective bandwidth isIt measures a traffic rate through exponential moments, so it retains information about rare bursts that the mean alone discards. If the limiting scaled cumulant-generating function exists, putThe finite-window effective bandwidth matters when the relevant overflow window is short or when arrivals are correlated; one should not replace it by its long-window limit without justification.

The following proof gives an actual upper bound for the infinite-horizon queue workload. For $B>0$, if $Q>B$, at least one positive integer $n$ has $A_n-cn>B$. The union bound followed by the Chernoff bound givesAssume each finite-window exponential moment is finite and $\kappa (\theta )<c\theta$. Choose $\eta >0$ such that $\kappa (\theta )+\eta <c\theta$. For all sufficiently large $n$, the summand is at most $e^{-n(c\theta -\kappa (\theta )-\eta )}$. Its tail is a convergent geometric series, and its finite prefix is finite. Denote the resulting sum by $K_{\theta }$. The exponential workload bound from cumulative arrival moments therefore proves, for $b>0$,Optimizing over admissible parameters givesOnly parameters with the stated finite-window moments are included. If there are no such positive parameters, the argument yields only the trivial exponent zero. Infinite ${\theta }_{\ast }$ means that the upper rate is $-\infty$. For a non-strict threshold $Q\ge Lb$, first bound it by $Q>L(b-\epsilon )$ and then let $\epsilon \downarrow 0$. This proof permits dependence between slots. It proves the required upper bound; it does not assert a matching lower bound for every arrival process.

This also gives a closed-set large-deviation upper bound for $Q/L$: set $I_{+}(q)={\theta }_{\ast }q$ for $q>0$, $I_{+}(0)=0$, and $I_{+}(q)=+\infty$ for $q<0$. If a closed set $F$ contains zero, its desired upper bound is the trivial bound zero. If $F$ contains positive points but not zero, its positive part has a smallest point $b>0$, and $\mathbb{P}(Q/L\in F)\le \mathbb{P}(Q\ge Lb)$ gives the bound $-{\inf }_F{I}_{+}$. If it contains no nonnegative points, its probability is zero. This verifies the upper half of a large deviation principle; the actual rate function can be larger when a matching lower bound is unavailable.

For independent and identically distributed slot arrivals $a_t$, let $\kappa (\theta )=\log \mathbb{E}{e}^{\theta a_0}$. Now $\log \mathbb{E}{e}^{\theta A_n}=n\kappa (\theta )$, so effective bandwidth is independent of window length. For $\kappa (\theta )<c\theta$, the geometric-series prefactor is explicitlyThus the familiar workload Chernoff bound for independent increments is a special case, and the largest admissible parameter is often the positive root of $\kappa (\theta )=c\theta$.

For these independent increments there is a sharper argument that works at the root itself when its exponential moment is finite. Along the past read in reverse order, put $S_n=A_n-cn$ and $M_n=e^{\theta S_n}$. If $\mathbb{E}{e}^{\theta (a_0-c)}\le 1$, then $M_n$ is a nonnegative supermartingale starting at one. For the first crossing time ${\tau }_B=\inf \{n\ge 0:S_n\ge B\}$, bounded optional stopping givesLet $N\to \infty$. Strict negative drift makes $S_n\to -\infty$ almost surely, so its supremum is attained and the crossing event agrees with $\{Q\ge B\}$. ConsequentlyThis strengthens the prefactor but uses temporal independence, which was unnecessary for the previous proof.

Several examples show what the effective bandwidth constraint means. Deterministic traffic of $r$ work units per slot has $\kappa (\theta )=\theta r$ and effective bandwidth $r$ at every parameter; if $c\ge r$ there is no stationary backlog. For independent on-off slots carrying $r$ units with probability $p$ and zero otherwise, the Bernoulli effective bandwidth isFor $0<p<1$, it rises from the mean $pr$ to the peak $r$. When $pr<c<r$, strict convexity of $\kappa (\theta )-c\theta$, its negative derivative at zero, and its divergence to infinity give a unique positive root. When $c\ge r$, no slot can increase the queue workload. In the specific case $r=2,c=1,p<1/2$, the workload increases by one with probability $p$ and decreases by one, reflected at zero, with probability $1-p$. Its stationary probabilities satisfyThe service-root equation has $e^{{\theta }_{\ast }}=(1-p)/p$, and thus $\mathbb{P}(Q\ge k)=q^k=e^{-{\theta }_{\ast }k}$. This example realizes the upper exponent exactly.

For independent Poisson slot counts of mean $\nu$, with unit work per arrival, the Poisson effective bandwidth isStability requires $c>\nu$. There is a unique positive root of $\nu (e^{\theta }-1)=c\theta$, again by strict convexity, negative initial derivative after subtracting $c\theta$, and growth to infinity. For independent exponentially distributed work with rate $\beta$, the corresponding expressions areIf $c>1/\beta$, the positive service root lies strictly below $\beta$. The finite moment-generating function domain cannot be ignored.

To see statistical aggregation explicitly, let the entire arrival processes of $k$ sources be independent, though each source may itself have dependence over time. Their cumulative totals satisfyThus the shared queue has an exponential overflow bound when ${\sum }_j{\alpha }_j(\theta )<c$, with the finite-window conditions proved above. One must use a common parameter $\theta$; adding individual service-root exponents is not the rule. For example, an independent Poisson source of mean $\nu$ and an independent on-off source of size $r$ and probability $p$ have aggregate cumulant-generating functionFor $\nu >0$ and $c>\nu +pr$, their positive service root exists and determines the optimized upper exponent. Several independent Poisson sources similarly have the same formula as one source with intensity ${\sum }_j{\nu }_j$.

The connection with large deviation principles also appears in a many-source scaling. For $L$ independent copies of a source, aggregate service $Lc$ and buffer $Lb$, fix a window $n$. Let $X_i(n)$ be each copy's cumulative input in that window. Their total exceeds $L(b+cn)$ only if the empirical mean exceeds $b+cn$. Its Chernoff bound has exponentIf exponential moments exist near zero, Cramér's theorem gives the corresponding finite-window large deviation principle. The threshold exceeds the mean because $m<c$ and $b>0$. A finite union of windows has upper exponential rate at most $-{\min }_n{V}_n(b)$ over those windows. Passing to infinitely many windows requires uniform control of long-window probabilities, not simply an interchange of a limit with an infinite union. A good sample-path large deviation principle in a topology where the queue workload is continuous, as in Question 3, instead permits the contraction principle for large deviations to derive a workload rate function and its closed-set overflow upper bound.

Finally, the mean and peak limits of effective bandwidth quantify the safety margin. With exponential moments near zero, independent slot work of mean $m$ and variance $v$ hasFor bounded work, its large-parameter limit is the essential peak. Stronger tail requirements select larger $\theta$ and hence more capacity; finite-window effective bandwidths also reflect temporal burst correlations. A stable heavy-tailed input can lack every positive exponential moment, in which case this exponential-bound method supplies no positive decay rate. Stability and exponentially small overflow are distinct requirements.