Server Load Performance Model
00
Estimate server response time in milliseconds based on user load and cache efficiency.
NOTEAbout This Model
## Estimate server response time in milliseconds based on user load and cache efficiency. This model estimates **System Response Time Ms** based on uncertain inputs: Concurrent Users, Cache Hit Rate, Origin Proc Time, Cache Proc Time. Run the simulation to see the distribution of possible outcomes.
VARIABLECache Hit Rate
beta(14.9, 5.1)
Percentage of requests served from cache vs origin.
cache_hit_rate
VARIABLECache Proc Time
beta(2, 19.49)
Time to serve a request directly from cache in seconds.
cache_proc_time
VARIABLEOrigin Proc Time
beta(5.5, 14.5)
Time to process a database or logic heavy request (no cache) in seconds.
origin_proc_time
VARIABLEConcurrent Users
poisson(505)
Number of simultaneous active users hitting the server.
concurrent_users
FORMULAEffective Time S
(cache_hit_rate * cache_proc_time) + ((1 - cache_hit_rate) * origin_proc_time)
Blended average processing time per request.
effective_time_s
FORMULASystem Response Time Ms
effective_time_s * (1 + (concurrent_users / max_concurrent_capacity)) * 1000
Response time in ms including queuing effects modeled as a factor of user load.
system_response_time_ms
OUTPUTSystem Response Time Ms
system_response_time_ms
Expected system response time under load. Values above your SLA threshold indicate capacity problems. The distribution shows how concurrent user variability affects performance.
CONSTANTMax Concurrent Capacity
200
Maximum concurrent users before queueing effects dominate response time.
max_concurrent_capacity
What is Carlo?
Carlo is a visual tool for Monte Carlo simulation. Model uncertainty by dragging probability distributions, connecting them visually, and running thousands of scenarios instantly.