Each station behaves like an exact stable node once its effective arrival rate is known. The node traffic intensities are exact fractions, and their interpretation is a busy fraction rather than a job count. This lesson checks local stability before using network-level product form.

highlighted = computed this step

Station A utilization

Station A utilization is 1/3. Why: divide station A arrivals by station A service capacity to get the long-run fraction of time that station is busy. A utilization is not a count of jobs; it is a load ratio that tells whether the node has enough service capacity for its effective arrivals.

ρA=1/3\rho_A=1/3
Node utilizationsEach station has an exact traffic intensity.lambda mu rholambdamurho131/3121/2

Station B utilization

Station B utilization is 1/2. Why: station B is slower, so the same effective arrivals create heavier traffic there. That is the first network lesson: two stations can see the same flow but have different congestion because their service rates differ.

ρB=1/2\rho_B=1/2
Node utilizationsEach station has an exact traffic intensity.lambda mu rholambdamurho131/3121/2

Both stations stable

Both utilizations are below 1. Why: each station must have service capacity above its effective arrival rate before a steady-state product-form calculation is claimed. Stability is checked locally at every node, because one unstable station would make the network mean grow without bound.

ρA<1ρB<1\rho_A<1\quad \rho_B<1
Node utilizationsEach station has an exact traffic intensity.lambda mu rholambdamurho131/3121/2

Diagram note

The table recomputes both utilizations from exact arrival and service rates. Read rho as a busy-fraction at a node, not as a network probability by itself. The exact network results that follow rely on these stable node loads inside an idealized open Jackson tandem. Pixel positions are rounded for layout; every number shown is exact.

node traffic intensities are exact\text{node traffic intensities are exact}
Node utilizationsEach station has an exact traffic intensity.lambda mu rholambdamurho131/3121/2