Performance Engineering · Cluster
Capacity Planning
Load models and headroom.
- 01 Headroom Arithmetic: Turning a Load Forecast into a Replica Count How arrival rate, service time, and utilization combine into a replica count, why the nonlinearity of queueing makes the last few percent of utilization decisive, and how safety margins compound.
- 02 Utilization Targets and the Knee of the Latency Curve Why latency stays flat until utilization approaches one and then rises sharply, how arrival variability moves the knee, and how to locate the operating point from measurements rather than doctrine.
- 03 Little's Law as a Consistency Check on Load Measurements How the identity connecting concurrency, arrival rate, and residence time exposes inconsistent measurement sets, plus the sampling errors and boundary conventions that make a correct system appear to violate it.
- 04 Amdahl and Gustafson Bounds in Capacity Models Why a serial fraction caps the throughput a fixed workload can reach, how scaled workload changes the bound, and what the usable-scalability limit means for sizing a fleet.
- 05 Variance and the Effective Capacity of a Replica Pool How service-time variance and partially correlated demand shrink the throughput a pool can sustain at a latency target, and why replica count, not average load, sets the tail.
- 06 Provisioning Lag and the Reaction Time of a Fleet How the delay between a load increase and usable capacity determines the headroom a plan needs, why boot time and image pull dominate the loop, and how the lag compounds with forecast error.
- 07 Modeling Throughput Growth and Service Demand Drift Why per-request cost rises across releases and with load, how to separate traffic growth from efficiency change in a capacity series, and what a drift budget means for a sizing horizon.
- 08 Feedback Effects That Change Service Demand How retries, timeouts, circuit breakers, and admission control alter the load a system receives, and why a positive feedback loop makes a capacity plan invalid at exactly the moment it is needed.
- 09 Load Balancing Policy and Effective Capacity How assignment policy, connection reuse, and request-class knowledge change the throughput a pool can sustain, and why a policy tuned for latency can reduce total capacity.
- 10 Capacity Failure Modes Under Peak Load The specific mechanisms that turn a load peak into an outage: queue overload, resource leaks under retry, cold cache amplification, and the asymmetry between degradation and recovery.