ray-project--ray
wehub-resource-sync
111 行
3.6 KiB
Python
111 行
3.6 KiB
Python
"""
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Application-level autoscaling policy for video processing pipeline.
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Scaling strategy:
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- VideoAnalyzer: scales based on its load (error_ratio = requests / capacity)
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- VideoEncoder: scales based on its load, with floor = VideoAnalyzer replicas
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- MultiDecoder: 0.5x VideoEncoder replicas
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Error ratio formula:
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error_ratio = total_ongoing_requests / (target_per_replica × current_replicas)
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- error_ratio > 1.0 → over capacity → scale up
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- error_ratio < 1.0 → under capacity → scale down
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- error_ratio = 1.0 → at capacity → no change
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"""
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import math
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from typing import Dict, Tuple
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from ray.serve._private.common import DeploymentID
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from ray.serve.config import AutoscalingContext
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def _get_error_ratio(ctx: AutoscalingContext) -> float:
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"""
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Calculate error ratio: how much over/under target capacity we are.
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Returns 1.0 when idle to maintain current replicas.
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"""
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target_per_replica = ctx.config.target_ongoing_requests or 1
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total_requests = ctx.total_num_requests
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current_replicas = ctx.current_num_replicas
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if total_requests == 0:
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return 1.0 # Idle: maintain current replicas
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total_capacity = target_per_replica * current_replicas
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return total_requests / total_capacity
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def _scale_by_error_ratio(ctx: AutoscalingContext, floor: int = 0) -> int:
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"""
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Calculate target replicas based on error ratio.
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Args:
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ctx: Deployment autoscaling context
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floor: Minimum replicas (in addition to capacity_adjusted_min)
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Returns:
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Target replica count, clamped to min/max limits
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"""
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error_ratio = _get_error_ratio(ctx)
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# Scale current replicas by error ratio
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target = int(math.ceil(ctx.current_num_replicas * error_ratio))
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# Apply floor (e.g., encoder should have at least as many as analyzer)
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target = max(target, floor)
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# Clamp to configured limits
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return max(
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ctx.capacity_adjusted_min_replicas,
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min(ctx.capacity_adjusted_max_replicas, target),
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)
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def _find_deployment(
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contexts: Dict[DeploymentID, AutoscalingContext],
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name: str,
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) -> Tuple[DeploymentID, AutoscalingContext]:
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"""Find deployment by name."""
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for dep_id, ctx in contexts.items():
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if dep_id.name == name:
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return dep_id, ctx
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raise KeyError(f"Deployment '{name}' not found")
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def coordinated_scaling_policy(
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contexts: Dict[DeploymentID, AutoscalingContext],
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) -> Tuple[Dict[DeploymentID, int], Dict]:
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"""
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Coordinated scaling for video processing pipeline.
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Scaling rules:
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VideoAnalyzer: scale by its own load
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VideoEncoder: scale by its own load, floor = analyzer replicas
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MultiDecoder: 0.5x encoder replicas
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"""
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decisions: Dict[DeploymentID, int] = {}
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# 1. VideoAnalyzer: scale by load
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analyzer_id, analyzer_ctx = _find_deployment(contexts, "VideoAnalyzer")
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analyzer_replicas = _scale_by_error_ratio(analyzer_ctx)
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decisions[analyzer_id] = analyzer_replicas
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# 2. VideoEncoder: scale by load, but at least as many as analyzer
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encoder_id, encoder_ctx = _find_deployment(contexts, "VideoEncoder")
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encoder_replicas = _scale_by_error_ratio(encoder_ctx, floor=analyzer_replicas)
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decisions[encoder_id] = encoder_replicas
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# 3. MultiDecoder: 0.5x encoder replicas
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decoder_id, decoder_ctx = _find_deployment(contexts, "MultiDecoder")
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decoder_replicas = max(1, math.ceil(encoder_replicas / 2))
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decoder_replicas = max(
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decoder_ctx.capacity_adjusted_min_replicas,
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min(decoder_ctx.capacity_adjusted_max_replicas, decoder_replicas),
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)
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decisions[decoder_id] = decoder_replicas
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return decisions, {}
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