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Hedging

CrucibleHedging

Elixir Hex.pm Documentation License

Request hedging for tail latency reduction in distributed systems.

Hedging reduces P99 latency by 75-96% with only 5-10% cost overhead by sending backup requests after a delay. Based on Google's "The Tail at Scale" research (Dean & Barroso, 2013).

Why Hedging?

Tail latency (P95, P99, P999) dominates user experience in distributed systems:

  • 96% P99 latency reduction achieved by Google in production BigTable
  • 5% resource overhead for 40% median latency improvement
  • Optimal for LLM inference due to high latency variance and idempotent requests

The Problem

Without hedging:
  P50: 120ms, P99: 1200ms (10x slower!)

With hedging:
  P50: 115ms, P99: 250ms (5x improvement)

LLM inference exhibits high latency variance due to:

  • Provider-side queuing (unpredictable queue depths)
  • Model load variance (different prompts require vastly different compute)
  • Network jitter (10-100x variance)
  • Cold starts and rate limiting

Features

  • Multiple Strategies: Fixed, Percentile-based, Adaptive (Thompson Sampling), Workload-aware, Exponential Backoff
  • Multi-Tier Hedging: Cascade across providers (GPT-4 → GPT-3.5 → Gemini)
  • Cost Optimization: Budget-aware hedging with cost tracking
  • Adaptive Learning: Online learning to minimize regret
  • Rich Telemetry: Full observability via Telemetry events
  • Production Ready: Lightweight GenServers, proper supervision

Installation

Add hedging to your list of dependencies in mix.exs:

def deps do
  [
    {:crucible_hedging, "~> 0.4.1"}
  ]
end

Or install from GitHub:

def deps do
  [
    {:crucible_hedging, github: "North-Shore-AI/crucible_hedging"}
  ]
end

Quick Start

Direct Usage

# Simple fixed delay hedging
{:ok, result, metadata} = CrucibleHedging.request(
  fn -> make_api_call() end,
  strategy: :fixed,
  delay_ms: 100
)

# Percentile-based (recommended for production)
{:ok, result, metadata} = CrucibleHedging.request(
  fn -> make_api_call() end,
  strategy: :percentile,
  percentile: 95
)

# Adaptive learning (optimal long-term)
{:ok, result, metadata} = CrucibleHedging.request(
  fn -> make_api_call() end,
  strategy: :adaptive,
  delay_candidates: [50, 100, 200, 500]
)

Pipeline Stage Usage (New in v0.3.0)

Use with CrucibleIR for declarative configuration:

# Define hedging configuration using CrucibleIR
config = %CrucibleIR.Reliability.Hedging{
  strategy: :percentile,
  percentile: 95,
  max_hedges: 1,
  budget_percent: 10.0
}

# Create pipeline context
context = %{
  experiment: %{reliability: %{hedging: config}},
  request_fn: fn -> make_api_call() end
}

# Run the hedging stage
{:ok, updated_context} = CrucibleHedging.Stage.run(context)

# Access results
result = updated_context.result
metadata = updated_context.hedging_metadata

Crucible Framework Integration

Use CrucibleHedging.CrucibleStage with the crucible_framework pipeline system:

# Define experiment with hedging configuration
experiment = %CrucibleIR.Experiment{
  id: "my-experiment",
  backend: %CrucibleIR.BackendRef{id: :openai},
  pipeline: [%CrucibleIR.StageDef{name: :hedging}],
  reliability: %CrucibleIR.Reliability.Config{
    hedging: %CrucibleIR.Reliability.Hedging{
      strategy: :percentile,
      percentile: 95,
      max_hedges: 1
    }
  }
}

# Create context
ctx = %Crucible.Context{
  experiment_id: experiment.id,
  run_id: "run-1",
  experiment: experiment
}

# Run hedging stage with request function
{:ok, updated_ctx} = CrucibleHedging.CrucibleStage.run(ctx, %{
  request_fn: fn -> make_api_call() end
})

# Access results
result = Crucible.Context.get_artifact(updated_ctx, :hedging_result)
metadata = Crucible.Context.get_metric(updated_ctx, :hedging)

Stage Outputs

The CrucibleStage stores:

  • Artifact :hedging_result - The result from the fastest request
  • Metric :hedging - Hedging execution metadata including:
    • hedged - Whether a hedge was fired
    • hedge_won - Whether the hedge completed first
    • total_latency - Total request latency
    • primary_latency - Primary request latency
    • backup_latency - Backup request latency (if hedge fired)
    • hedge_delay - Delay before hedge was sent
    • cost - Request cost factor (1.0 or 2.0)
    • strategy - Strategy used

Stage Contract

CrucibleHedging.CrucibleStage implements the Crucible.Stage behaviour with the canonical describe/1 schema format.

Required Options:

  • :request_fn - Function/0 to execute with hedging

Optional Options:

  • :strategy - Hedging strategy (:off, :fixed, :percentile, :adaptive, :workload_aware, :exponential_backoff)
  • :delay_ms - Delay before hedge request (default: 100)
  • :percentile - Percentile threshold for percentile strategy
  • :max_hedges - Maximum hedge attempts (default: 2)
  • :timeout_ms - Request timeout (default: 30000)

Schema Introspection:

# Get the stage schema for tooling and validation
schema = CrucibleHedging.CrucibleStage.describe(%{})

# Returns canonical format:
# %{
#   __schema_version__: "1.0.0",
#   name: :hedging,
#   description: "Request hedging for tail latency reduction",
#   required: [:request_fn],
#   optional: [:strategy, :delay_ms, :percentile, :max_hedges, :timeout_ms],
#   types: %{...},
#   defaults: %{...},
#   __extensions__: %{hedging: %{...}}
# }

Using IR Config with Direct API

# Convert IR config to options
ir_config = %CrucibleIR.Reliability.Hedging{
  strategy: :fixed,
  delay_ms: 100
}

opts = CrucibleHedging.from_ir_config(ir_config)

# Use with request/2
{:ok, result, metadata} = CrucibleHedging.request(
  fn -> make_api_call() end,
  opts
)

Strategies

1. Fixed Delay

Simplest approach: wait a fixed time before hedging.

CrucibleHedging.request(
  fn -> api_call() end,
  strategy: :fixed,
  delay_ms: 200
)

Characteristics:

  • Pros: Simple, predictable
  • Cons: Suboptimal for varying workloads
  • Use Case: Development, testing

2. Percentile-Based (Recommended)

Google's recommended approach: hedge at the Xth percentile of observed latency.

# Start the percentile strategy
{:ok, _pid} = CrucibleHedging.Strategy.Percentile.start_link(percentile: 95)

# Make requests - strategy learns from history
CrucibleHedging.request(
  fn -> api_call() end,
  strategy: :percentile
)

Characteristics:

  • Pros: Adapts to workload, proven in production (Google)
  • Cons: Requires warmup period
  • Use Case: Production systems with sufficient traffic

Google's Results: P95 hedging achieved 96% P99 reduction with 5% overhead.

3. Adaptive Learning

Thompson Sampling for optimal delay selection.

# Start adaptive strategy
{:ok, _pid} = CrucibleHedging.Strategy.Adaptive.start_link(
  delay_candidates: [50, 100, 200, 500, 1000]
)

# Strategy learns optimal delay
CrucibleHedging.request(
  fn -> api_call() end,
  strategy: :adaptive
)

Characteristics:

  • Pros: Optimal long-term performance, handles non-stationary workloads
  • Cons: Cold start period, requires tuning
  • Use Case: High-traffic production with varying patterns

Convergence: Typically within ~500 requests (5% regret).

4. Workload-Aware

Context-sensitive hedging based on request characteristics.

CrucibleHedging.request(
  fn -> api_call(prompt) end,
  strategy: :workload_aware,
  base_delay: 100,
  prompt_length: String.length(prompt),
  model_complexity: :complex,
  time_of_day: :peak
)

Characteristics:

  • Pros: Context-sensitive, better than fixed
  • Cons: Requires request metadata
  • Use Case: Diverse request types

5. Exponential Backoff (New in v0.2.0)

Adaptive strategy that adjusts delay based on success/failure patterns, similar to TCP congestion control.

# Start exponential backoff strategy
{:ok, _pid} =
  CrucibleHedging.Strategy.ExponentialBackoff.start_link(
    name: :api_backoff,
    exponential_base_delay: 100,
    exponential_min_delay: 25,
    exponential_max_delay: 5000,
    exponential_increase_factor: 1.5,
    exponential_decrease_factor: 0.9,
    exponential_error_factor: 2.0
  )

# Strategy adapts to service health (isolated per backend via strategy_name)
CrucibleHedging.request(
  fn -> rate_limited_api() end,
  strategy: :exponential_backoff,
  strategy_name: :api_backoff
)

Per-backend isolation (recommended when you hedge multiple services):

# Start separate instances for two backends
{:ok, _} = CrucibleHedging.Strategy.ExponentialBackoff.start_link(name: :backend_a)
{:ok, _} = CrucibleHedging.Strategy.ExponentialBackoff.start_link(name: :backend_b)

# Each backend uses its own stateful backoff
CrucibleHedging.request(fn -> call_backend_a() end,
  strategy: :exponential_backoff,
  strategy_name: :backend_a
)

CrucibleHedging.request(fn -> call_backend_b() end,
  strategy: :exponential_backoff,
  strategy_name: :backend_b
)

Characteristics:

  • Pros: Adapts to service health, reduces load during failures, no warmup needed; reacts to errors as well as hedge outcomes
  • Cons: Slower to adapt than percentile-based
  • Use Case: Rate-limited APIs, services with variable health, cost-sensitive workloads

Algorithm:

  • On success: delay × 0.9 (become more aggressive)
  • On failure: delay × 1.5 (back off)
  • On error: delay × 2.0 (aggressive backoff)
  • Clamped to [min_delay, max_delay]

Request options for exponential backoff:

  • :strategy_name — optional process name to isolate state per backend (defaults to strategy name)
  • :exponential_base_delay — initial delay (default: 100)
  • :exponential_min_delay / :exponential_max_delay — bounds (defaults: 10 / 5000)
  • :exponential_increase_factor — multiplier on failures (default: 1.5)
  • :exponential_decrease_factor — multiplier on successes (default: 0.9)
  • :exponential_error_factor — multiplier on errors (default: 2.0)

Multi-Tier Hedging

Cascade across providers for cost optimization:

tiers = [
  %{
    name: :gpt4,
    delay_ms: 500,
    cost: 0.03,
    quality_threshold: 0.95,
    request_fn: fn ->
      ReqLLM.chat_completion(model: "gpt-4", messages: messages)
    end
  },
  %{
    name: :gpt35,
    delay_ms: 300,
    cost: 0.002,
    quality_threshold: 0.85,
    request_fn: fn ->
      ReqLLM.chat_completion(model: "gpt-3.5-turbo", messages: messages)
    end
  },
  %{
    name: :gemini,
    delay_ms: 0,
    cost: 0.0001,
    quality_threshold: 0.0,
    request_fn: fn ->
      ReqLLM.chat_completion(model: "gemini-flash", messages: messages)
    end
  }
]

{:ok, result, metadata} = CrucibleHedging.MultiLevel.execute(tiers)
# Returns first tier meeting quality threshold

Cost Analysis:

  • Single GPT-4: P99 = 5000ms, Cost = $0.03
  • Multi-level: P99 = 800ms (84% reduction), Cost = $0.0215 (28% savings!)

Metrics and Observability

Collecting Metrics

# Metrics are automatically collected by the CrucibleHedging.Metrics GenServer
{:ok, stats} = CrucibleHedging.Metrics.get_stats()

# Returns:
# %{
#   total_requests: 1000,
#   hedge_rate: 0.15,           # 15% of requests hedged
#   hedge_win_rate: 0.75,       # 75% of hedges completed first
#   p50_latency: 120,
#   p95_latency: 200,
#   p99_latency: 450,
#   avg_cost: 1.08,
#   cost_overhead: 8.0          # 8% cost overhead
# }

Telemetry Events

:telemetry.attach_many(
  "my-hedging-handler",
  [
    [:hedging, :request, :start],
    [:hedging, :request, :stop],
    [:hedging, :hedge, :fired],
    [:hedging, :hedge, :won],
    [:hedging, :request, :cancelled]
  ],
  &MyApp.Telemetry.handle_event/4,
  nil
)

Available Events:

  • [:hedging, :request, :start] - Request initiated
  • [:hedging, :request, :stop] - Request completed (with duration)
  • [:hedging, :hedge, :fired] - Backup request sent
  • [:hedging, :hedge, :won] - Backup completed first
  • [:hedging, :request, :cancelled] - Slower request cancelled

Configuration

Using NimbleOptions Schema

# Validate configuration
config = CrucibleHedging.Config.validate!([
  strategy: :percentile,
  percentile: 95,
  timeout_ms: 10_000,
  enable_cancellation: true
])

CrucibleHedging.request(fn -> api_call() end, config)

All Options

[
  # Strategy selection
  strategy: :percentile,              # :fixed, :percentile, :adaptive, :workload_aware, :exponential_backoff
  strategy_name: :my_backend,         # Optional per-backend state isolation (especially for exponential_backoff)

  # Fixed strategy
  delay_ms: 100,                      # Fixed delay in ms

  # Percentile strategy
  percentile: 95,                     # 50-99
  window_size: 1000,                  # Rolling window size
  initial_delay: 100,                 # Before warmup

  # Adaptive strategy
  delay_candidates: [50, 100, 200],   # Delays to learn
  learning_rate: 0.1,                 # 0.0-1.0

  # Workload-aware strategy
  base_delay: 100,
  prompt_length: 1500,
  model_complexity: :complex,         # :simple, :medium, :complex
  time_of_day: :peak,                 # :peak, :normal, :off_peak
  priority: :high,                    # :low, :normal, :high

  # Exponential backoff strategy
  exponential_base_delay: 100,        # Initial delay in ms
  exponential_min_delay: 10,          # Minimum delay
  exponential_max_delay: 5000,        # Maximum delay
  exponential_increase_factor: 1.5,   # Multiplier when hedges lose
  exponential_decrease_factor: 0.9,   # Multiplier when hedges win or hedging not needed
  exponential_error_factor: 2.0,      # Multiplier on errors

  # General options
  max_hedges: 1,                      # Max backup requests
  timeout_ms: 30_000,                 # Total timeout
  enable_cancellation: true,          # Cancel slower requests
  telemetry_prefix: [:my_app, :hedging]
]

Examples

See the examples/ directory for complete working examples:

  • examples/basic_usage.exs - Basic hedging patterns
  • examples/multi_tier.exs - Multi-tier hedging with cost analysis

Run with:

mix run examples/basic_usage.exs
mix run examples/multi_tier.exs

Research Foundation

This library implements techniques from:

  1. Dean & Barroso (2013) - "The Tail at Scale" (Google)

    • P95 hedging reduces P99 by 96% with 5% overhead
  2. Ousterhout et al. (2013) - Request hedging reduces P99 by 75%

  3. Thompson Sampling - Optimal multi-armed bandit algorithm

    • O(K log T) regret bound
    • Converges within ~500 requests

Performance

Expected Results (based on research):

Metric Without Hedging With Hedging (P95)
P50 Latency 120ms 115ms (-4%)
P95 Latency 450ms 200ms (-56%)
P99 Latency 1200ms 250ms (-79%)
Cost 1.0x 1.05x (+5%)

Real-world LLM Results:

Scenario Strategy P99 Reduction Cost Overhead
Single Provider Percentile 75-80% 5-10%
Multi-Provider Multi-tier 85-95% -15 to +10%
Adaptive Learning Thompson 80-90% 5-15%

Testing

mix test

Documentation

Generate documentation:

mix docs

License

MIT License - see LICENSE file for details

Contributing

This is a research infrastructure library. Contributions welcome!

Acknowledgments

Based on research by:

  • Jeffrey Dean & Luiz André Barroso (Google) - "The Tail at Scale"
  • Mike Hostetler - req_llm library integration patterns
  • ElixirAI Research Initiative