๐Ÿ› Latency Budget Simulator

Published:

What This Playground Demonstrates

The simulator helps engineers understand end-to-end request latency as a budget split across network, application logic, cache, and database layers.

Instead of arguing about โ€œthe slow service,โ€ you can see exactly which segment dominates p95 and how each optimization changes total response time.

Scenario Setup

SegmentBaseline p95 (ms)
Client -> Edge40
Edge -> API25
API compute60
Cache lookup15
DB query90
Serialization20
Response transfer30

Baseline end-to-end p95 is the sum of segment contributions.

Model Formula

Use a simple additive model for first-pass reasoning:

end_to_end_p95 =
  client_edge +
  edge_api +
  api_compute +
  cache_lookup +
  db_query +
  serialization +
  response_transfer

Then apply modifiers for retries, jitter, and queueing effects in advanced mode.

Suggested Interactions

  • Adjust cache hit rate and watch DB latency contribution shrink.
  • Add network jitter to evaluate resilience.
  • Apply query optimization and compare before/after p95.
  • Increase traffic burst multiplier and observe queue spillover.
  • Toggle regional failover and compare additional network cost.

Exploration Paths

Path A: Quick Wins

  1. Increase cache hit rate from 60% to 85%.
  2. Cut DB p95 from 90ms to 55ms with indexing.
  3. Recompute end-to-end latency and compare gain.

Path B: Resilience Under Stress

  1. Add jitter and packet loss.
  2. Increase retry counts.
  3. Validate whether p95 remains within SLO.

Path C: Capacity Planning

  1. Simulate burst traffic x2 and x4.
  2. Add queue processing delay.
  3. Identify threshold where user-facing SLO is breached.

Visual Architecture

flowchart LR; Client-->Edge; Edge-->API; API-->Cache; API-->DB; API-->Serializer; Serializer-->Client;

Educational Prompts for Users

  • Which segment is the largest absolute contributor today?
  • If you can optimize only one segment this sprint, which yields the best SLO gain?
  • At what burst level does the system need architectural change instead of tuning?

Learning Goal

Latency optimization is highest leverage when you target dominant contributors first instead of micro-optimizing low-impact segments.

Stretch Features

FeatureWhy It Matters
p50/p95/p99 overlaysteaches percentile-aware decisions
โ€œwhat changedโ€ diffshelps communicate optimization impact
cost-per-ms estimatealigns engineering tradeoffs with budget
scenario presetsspeeds up team onboarding

Next Iteration

  • Add percentile curves (p50/p95/p99)
  • Add traffic burst simulation
  • Export comparison snapshots