Load and Risk Prediction

1) Why do you need it

• Capacity planning and budgeting
• SLO/SLI settings, error budgets, and alert policies
• choosing a release strategy (canary, blue-green, dark launch);
• risk management: prevention of degradation, queues, drop transactions, SLA fines.

2) Basic concepts

Load-The rate of incoming events/operations (RPS, TPS, events/sec) as well as CPU/RAM/IO/NET consumption.
Capacity-Consistently achievable performance at a given SLO and cost.
Risk: probability × impact of an unwanted event (SLA failure, incident, overspend).
Early indicators: metrics growing before the incident (latency p95/p99, queue depth, GC pauses, error rate, saturation).
Headroom-The ratio of available capacity to the current load.

3) Data sources and metrics

Sources: logs and metrics (Prometheus/OTel), traces, business events (Kafka), CDN/WAF/ALB logs, marktech data (campaigns), event calendars, billing/bones (FinOps), phicheflags/releases, queues (Kafka/Rabbit), DB/caches.

• Traffic: RPS/TPS, active users (DAU/MAU), sessions, step conversion.
• Performance: latency p50/p95/p99, throughput, errors (4xx/5xx), timeouts, retries.
• Ресурсы: CPU/LoadAvg, RAM/GC, disk IOps/lat, network bw, connection pool usage.
• Queues: backlog, lag, consumer lag, time-in-queue.
• БД: QPS, lock waits, slow queries, replication lag.
• Кэши: hit ratio, eviction rate, hot keys.
• Business level: deposits/rates per minute, payment refusals, KYC/AML queue.
• Reliability: SLI/SLO, error budget burn rate (1h/6h/24h).

4) Baseline prediction models

1. Deterministic and calendar: regression on known drivers (date/time, matches, tournaments, market pools, geo, stock fluffs).
2. Statistical: seasonality/trend (ARIMA/ETS), regression with holidays, Prophet-like approaches.
3. ML/ensembles: gradient boosting/Random Forest/XGBoost/LightGBM; add features: weather, exchange rate, sports news, competing events.
4. Mixed: statistics for baseline seasonality + ML for exogenous factors (campaigns, releases).
5. Quotas/quantiles: forecast not only average, but also p90/p95 for headroom planning.

Model outputs: prediction of RPS/TPS and latency/error distributions at T + 1h/T + 24h/T + 7d/T + 30d horizons with confidence intervals.

5) Queues and Limits: Mini Theory

Little's Law: L = λ × W (mean number in the system = intensity × mean time).
Bottlenecks: DB/cache/bus/connection pool/API provider limits.
Saturation: at load> 70-80% latency increases non-linearly.
Backpressure: consumer protection against overload (limits, queues, shed policies, feature degradation).

6) Capacity Planning

SLO method: required p99 latency and acceptable error rate → which throughput is maintained at headroom N%.
The "from scenarios" method: "Champions League match," "Black Friday," "Large-scale tournament" → the upper quantiles of traffic + failure of one AZ/node.
Method "cost-aware": select configurations by $/RPS, taking into account discounts, reservations, spot/subscriptions, autoscaling.

Artifacts: Capacity Model per service, limits and quotas (API, DB, queues), bottleneck → action table (sharding, caching, replica, CQRS, async).

7) Risk management

Risk register: identification, description, probability, impact (finance/SLA/regulatory), owners, prevention/response plans.
Categories: load (overload), infrastructure (AZ/region fail), dependencies (payment providers), release (regression), product (campaign soared stronger than expectations), compliance (limits/regulator).
Matrix: Heatmap (Low/Medium/High × Impact).
KRI (Key Risk Indicators): queue depth, p99 growth, hit-ratio drop, burn rate> 2 ×, provider errors.

8) Early warning and alerting

Early-warning SLIs: p95 growth, cache hits decrease, tail latency growth, retry/timeout growth, consumer lag increase.
Burn-rate alerts on budget errors: fast (1h) and slow (6-24h) windows.
Threshold and anomaly-based alerts: baseline thresholds + anomaly models (IQR, STL, stream detectors).
Signal aggregation: correlation of release/phicheflag/campaign events with degradation.

9) Scenario analysis and "what-if"

"If traffic growth + 60% in 10 minutes?"

"If CDN/WAF cuts 5% of legitimate traffic?"

"If the payment provider loses 30% of authorizations?"

For each scenario: expected metrics, bottlenecks, degradation steps (toggle off non-critical features), manual/auto-scale, switching providers.

10) Testing and verification of forecasts

Load tests: synthetic traffic (k6/JMeter/Locust), real mix profiles.
Game Days/Chaos: disable AZ, degrade the database, exhaust the pool.
Shadow/Dark: traffic "into the shadow" of the new path without affecting the prod.
Accuracy retrospective: MAPE/SMAPE/RMSE + post-mortem "where were you wrong? ”.

11) Processes and roles

• Responsible: SRE/Platform/DS analysts.
• Accountable: Head of Ops/SRE.
• Consulted: Dev Leads, Marketing, Finance (FinOps).
• Informed: Support/Compliance/Business.
• Cadence: weekly forecast updates, monthly SLO/Capacity revisions, pre-event var rooms.

12) Tools and stack

Data: Kafka, ClickHouse/BigQuery, Lake/DWH, dbt.
Monitoring: Prometheus, Grafana, Tempo/Jaeger, Loki/ELK, OTel.
ML/Forecasts: Airflow/Argo, feature store, ARIMA/ETS/GBM models, forecast service (gRPC/REST).
Тесты: k6/JMeter/Locust, Fault-injection/Chaos Mesh.
Management: Feature Flags, Autoscaling (HPA/KEDA), Policy-as-Code.
FinOps: cost explorer, showback/chargeback, $/RPS dashboards.

13) Implementation Practice (roadmap)

1. Inventory of metrics and dependencies → critical path map (deposit, rate, output).
2. SLO/SLI and error budgets → target p95/p99, error-rates, burn alerts.
3. Data collection and cleaning → single event/metric layer, deduplication, latency.
4. Baseline seasonality forecast → day/week patterns, holidays/matches.
5. Expansion by drivers → market campaigns, releases, geo, payment windows.
6. Capacity models by services → headroom, limits, bottlenecks, optimization plan.
7. Scenario "what-if" and degradation table (kill-switches, read-only, grace).
8. Verification through tests/shadows → adjustment of models and thresholds.
9. Operating routine → weekly forecasts, pre-event reviews, post-event retro.
10. Automation → autoscale according to the forecast, auto-switching of providers, auto-phicheflags.

14) Antipatterns

"Medium only" prediction without p95/p99 tails.
Ignoring queues and pools - problems pop up at peak.
Manual by eye without validation and accuracy metrics.
There is no link → over-scaling costs.
Lack of degradation plan and phicheflags.

15) Dashboards and reporting

Exec-dashboard: RPS/TPS forecast (p50/p90/p95), headroom, risk card, burn-rate.
Tech-dashboard: p95/p99 latency by services, queues/lag, hit-ratio, connection pool, database/cache, external API limits.
Financial: $/RPS, cost forecast, optimization effect.
Forecast accuracy: actual vs forecast, period/geo/channel error.

16) Artifact patterns

Risk Register: ID, risk, probability/impact, owner, KRI, prevention plan, reaction plan.
Capacity Sheet: service, current throughput, limit, bottleneck, headroom, required expansion, ETA/cost.
What-If Cards: scenario, input factors, expected metrics, actions, completion criteria.
Playbook Degrade: list of features to disable, QoS levels, cache/static routes, retry/timeout limits.

17) Key KPI functions

SLO execution (% of periods in target), response time to early indicators, forecast accuracy (MAPE/SMAPE), number of incidents due to overload, share of automatic scaling, $/RPS savings without SLO degradation.

Total

System forecasting of load and risks is a bundle: quality data → meaningful metrics → testable models → scenarios and playbooks → automation of scaling and degradation. This contour provides stability, predictability of costs and a stable user experience even at extreme peaks.