Congestion Calculator
Estimate network congestion level from link utilization and traffic load.
About Congestion Calculator
Network congestion occurs when offered traffic load approaches or exceeds link capacity. As utilization rises above ~70%, queuing delays increase nonlinearly. When load exceeds capacity, excess packets are dropped (tail-drop) or marked (ECN), causing retransmissions that further increase effective load.
Formula
Link utilization
utilization% = offered_load / link_capacity * 100
- lambda
- Offered traffic load
- C
- Link capacity
Worked example
With the default values below, Congestion Calculator returns Link Utilization of 80.00%. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Offered Traffic Loadminimum 0 Mbps
- 800 Mbps
- Link Capacityminimum 0.1 Mbps
- 1,000 Mbps
- Buffer Sizeminimum 0 ms
- 50 ms
Results
- Link Utilization
- 80.00%
- Congestion Level
- High — approaching saturation
- Excess Traffic (above capacity)
- 0.00 Mbps
- Estimated Packet Loss
- 0.00%
Step by step
- 1Link utilization= 80%
- 2Excess traffic= 0 Mbps
- 3Estimated packet loss= 0%
Assumptions and limits
- The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax or compounding frequency into account.
- Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Why does congestion increase nonlinearly near capacity?
Queuing theory (M/M/1 model) shows that as utilization approaches 100%, average queue length and delay grow toward infinity — small increases in load cause disproportionately large increases in delay, which is why keeping utilization below 70-80% is a common engineering target.
How does TCP respond to congestion?
TCP congestion control algorithms (Reno, CUBIC, BBR) reduce sending rate when packet loss or increased delay signals congestion, then gradually probe for available capacity — this feedback loop is what prevents network collapse but also causes throughput fluctuations.