Effective bandwidth ($\alpha (s,t)=\log \mathbb{E}[e^{sX(t)}]/(st)$)

Effective bandwidth describes the exponential-moment traffic rate at a specified time scale and tail parameter. Independent sources have additive effective bandwidths. The Chernoff bound connects their sum to overflow bounds. For a single time unit, the small-parameter limit is the expected value and the large-parameter limit is the essential supremum, with appropriate moment or extended-value conventions.

For independent Poisson arrival counts of mean $\nu$ per slot, with one unit of work per arrival, the cumulant-generating function is $\nu (e^{\theta }-1)$. Its effective bandwidth is the displayed expression. If $c>\nu$, the equation $\nu (e^{\theta }-1)=c\theta$ has a unique positive root: strict convexity, negative initial derivative after subtracting $c\theta$, and divergence to infinity prove existence and uniqueness. Independent Poisson streams combine by replacing $\nu$ with the sum of their intensities.

Independent slots carry either zero work or $r>0$ units, the latter with probability $p$. Their effective bandwidth is the displayed expression, with small-parameter limit $pr$ and large-parameter limit $r$ when $0<p<1$. For $pr<c<r$, the positive root of $\log (1-p+p{e}^{\theta r})=c\theta$ determines the optimized exponential overflow bound. In the special case $r=2,c=1,p<1/2$, the stationary queue workload is geometric with ratio $p/(1-p)$, and the root is ${\theta }_{\ast }=\log ((1-p)/p)$, giving exactly $\mathbb{P}(Q\ge k)=e^{-{\theta }_{\ast }k}$ for integers $k\ge 0$.

Let a slotted queue have stationary input, constant service $c$, and finite stationary queue workload $Q={\sup }_{n\ge 0}(A_n-cn)$. Write $K_n(\theta )=\log \mathbb{E}{e}^{\theta A_n}$ for its past cumulative arrivals. The union bound and Chernoff bound give the displayed inequality for $B>0$. It requires no temporal independence. If each $K_n(\theta )$ is finite and $K_n(\theta )/n\to \kappa (\theta )<c\theta$, the series is finite: its tail is bounded by a geometric series. Consequently ${\mathrm{limsup}}_{L\to \infty }{L}^{-1}\log \mathbb{P}(Q>Lb)\le -\theta b$ for $b>0$. The condition is that the asymptotic effective bandwidth $\kappa (\theta )/\theta$ be less than service capacity.

For a nondecreasing process $A$ with stationary increments and independent increments, suppose $\mathbb{E}{e}^{\theta A(t)}=e^{t\Lambda (\theta )}$. If $\Lambda (\theta )\le C\theta$, then $e^{\theta (A(t)-Ct)}$ is a nonnegative supermartingale. Applying the maximal inequality for a nonnegative supermartingale up to time $T$ and then letting $T\to \infty$ givesThe stationary workload of a stable queue fed by $A$ has this supremum distribution, using the past input and stationary increments and independent increments. For independent flows, the constraint is ${\sum }_j{a}_j(\theta )\le C$, where $a_j(\theta )={\Lambda }_j(\theta )/\theta$ is the long-time effective bandwidth. The result bounds a continuous-time supremum directly; a one-time Chernoff bound by itself does not do that.

For $s>0$ in the finite moment-generating function domain and integrable $X$, Jensen inequality gives the lower bound and $X\le \mathrm{esssup}X$ almost surely gives the upper bound. The Holder inequality makes $K(s)=\log \mathbb{E}{e}^{sX}$ convex with $K(0)=0$. Thus $K(t)\le (t/s)K(s)$ for $0<t<s$, and the effective bandwidth is nondecreasing. It need not be strictly increasing: deterministic traffic gives a constant bandwidth. Infinite peaks or exponential moments require extended-value conventions.

For independent identically distributed traffic increments and constant service $C$, let $a(s)=\mathbb{E}[e^{s(X-C)}]$. If $a(s)<1$, apply the Chernoff bound at each candidate workload window and sum a geometric series. This bounds the stationary random-walk supremum. It explains the effective bandwidth criterion $\alpha (s)<C$ and distinguishes a queue's infinite-horizon overflow event from a single-window traffic event.

For nonnegative traffic with exponential moments near zero, its effective bandwidth has expansion $\mathbb{E}X+s\mathrm{Var}(X)/2+O(s^2)$. For bounded traffic with essential supremum $M$, it increases toward $M$: the exponential moment is at most $e^{sM}$, and any positive probability of $X>a$ gives $\alpha (s)\ge a+s^{-1}\log \mathbb{P}(X>a)$. Unbounded traffic has an infinite large-parameter limit when all positive exponential moments exist; a finite moment domain must be respected.

For a normal distribution $N(\lambda ,{\sigma }^2)$, the logarithm of its moment-generating function is $s\lambda +s^2{\sigma }^2/2$. Dividing by $s>0$ gives its effective bandwidth. Independent demands add these bandwidths. For total mean $m$ and positive total variance $v$, optimizing a Chernoff bound with target $e^{-\gamma }$, $\gamma >0$, yields the sufficient margin $C\ge m+\sqrt{2\gamma v}$.

For a normal distribution with mean $m$, positive variance $v$, and $\gamma >0$, the target $\mathbb{P}(X\ge C)\le e^{-\gamma }$ is equivalent to the displayed constraint, where $\Phi$ is the standard normal distribution function. This follows from $\mathbb{P}(X\ge C)=1-\Phi ((C-m)/\sqrt{v})$ and strict monotonicity of $\Phi$. Unlike the sufficient Chernoff bound margin $\sqrt{2\gamma v}$, this constraint is necessary as well as sufficient. The exact quantile can be negative when $e^{-\gamma }>1/2$.

If a random variable has variance zero and mean $m$, it equals $m$ almost surely. For a target probability strictly between zero and one, $\mathbb{P}(X\ge C)$ meets the target exactly when $m<C$. Substituting zero variance into a positive-variance non-strict quantile constraint incorrectly includes $m=C$, where the overflow probability is one. The distinction comes from the atom at the threshold.