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📈
Adaptive Compute Scaling(ACS)
Dynamically adjusts computational resources based on workload demands and performance requirements
Complexity: highPattern
Core Mechanism
Adaptive Compute Scaling automatically adjusts compute capacity (pods/replicas, nodes/VMs, GPU workers) in response to real-time demand and SLOs. It combines metric-driven policies (e.g., p95 latency, queue length, utilization) with stabilization windows, cooldowns, and predictive/scheduled scaling to maintain performance while minimizing cost. Typical stacks layer service autoscaling (HPA/KEDA/Ray Serve) with cluster/VM autoscaling (Cluster Autoscaler, AWS ASG, Azure VMSS, GCP MIG) and workload-aware batching.
Workflow / Steps
- Ingest signals: p95 latency, queue depth/wait time, CPU/memory/GPU utilization, error rate, RPS/TPS.
- Define targets: SLOs (latency, availability), budgets (cost/energy), and min/max replica bounds.
- Choose policies: target tracking vs. step scaling; separate scale-up and scale-down behaviors.
- Execute scaling: adjust replicas (HPA/KEDA/Ray Serve), then provision capacity (ASG/VMSS/MIG, Cluster Autoscaler).
- Stabilize: apply stabilizationWindow/cooldowns and rate limits to avoid thrashing.
- Pre-warm: keep warm pools or scheduled/predictive scale for diurnal/burst patterns.
- Observe and tune: monitor KPIs, attribution by stage, and refine signals/thresholds regularly.
Best Practices
Use workload-appropriate signals: for GPU LLM serving, prefer p95 latency and queue depth over CPU%.
Set distinct scale-up (fast, aggressive) vs. scale-down (slow, conservative) policies with stabilization windows.
Pre-warm workers for cold-start-heavy models; use warm pools or schedule-based and predictive scaling.
Layer autoscalers: service (HPA/KEDA/Ray Serve) + cluster/VM (Cluster Autoscaler/ASG/VMSS/MIG).
Constrain with min/max replicas and max surge/drain; use Pod Disruption Budgets and graceful drains.
Batch intelligently (vLLM/TGI/Triton dynamic batching) and bound per-request tokens to keep tail latency stable.
Right-size nodes; bin-pack GPUs; use MIG/MPS where appropriate; avoid resource fragmentation.
Use SLO-driven targets (e.g., keep p95 < target) and monitor per-tenant fairness in multi-tenant clusters.
Instrument decision logs and runbooks; simulate bursts and failure modes before production.
When NOT to Use
- Ultra low-latency hard real-time paths where any cold-start or rebalancing is unacceptable.
- Stateful, heavily session-affine workloads without sharding or sticky routing strategies.
- Licensing or quota-limited services where additional replicas cannot serve more throughput.
- Air-gapped or fixed-capacity environments with no elastic backing resources.
Common Pitfalls
- Using CPU% for GPU-bound inference; choose queue latency/depth and GPU utilization instead.
- Thrashing from missing stabilization windows/cooldowns and symmetric scale policies.
- Ignoring warm-up times, image pulls, or large model load times → prolonged SLO breaches.
- No min capacity or headroom; Cluster Autoscaler lag starves pod autoscalers.
- Scaling to zero without request buffering/warmers → severe cold-start penalties.
- Over-reliance on averages; target p95/p99 and protect against bursty arrivals.
Key Features
Metric-driven autoscaling (latency, queue depth, utilization, error rate).
Event-based scaling via external systems (KEDA: Kafka, SQS, Redis, Prometheus).
Predictive and scheduled scaling for diurnal patterns and known events.
Multi-layer scaling: service, node/VM, and cluster capacity coordination.
SLO-aware policies with distinct up/down behaviors and stabilization.
Cost/energy-aware constraints and budgets with per-tenant quotas.
KPIs / Success Metrics
- Latency p50/p95/p99 vs. SLO; queue wait time; error/timeouts under burst.
- Throughput (RPS/TPS, tokens/sec) and scaling latency (time-to-capacity).
- Cost per successful request; instance/GPU-hours; over/under-provisioned time%.
- Autoscaling event rate and stability (no flapping); saturation headroom.
- Per-tenant fairness and quota adherence in multi-tenant clusters.
Token / Resource Usage
For LLM inference, track tokens/sec, concurrent requests, and KV cache memory. Use batching and context/token caps to bound tail latency; prefer queue depth + p95 latency as primary signals. For GPU nodes, monitor VRAM/SM utilization, model load times, and ensure autoscalers have capacity headroom.
- Signals: queue depth, p95 latency, GPU util, request concurrency, token budgets.
- Controls: dynamic batching, max new tokens, prompt length limits, warm pools.
- Efficiency: cache KV/prefix; shard large models; bin-pack GPUs; avoid tiny underutilized pods.
Best Use Cases
- Bursty, diurnal, or campaign-driven traffic (e-commerce, launches, live events).
- GPU LLM serving with dynamic batching and strict latency/cost SLOs.
- Event-driven processing (queues/streams) where backlog length is a clear signal.
- Multi-tenant platforms needing cost control, fairness, and predictable SLOs.
References & Further Reading
Academic Papers
Implementation Guides
- Kubernetes Horizontal Pod Autoscaler (v2)
- KEDA: Event-Driven Autoscaling
- Kubernetes Cluster Autoscaler
- AWS EC2 Auto Scaling: Target Tracking
- AWS Predictive Scaling
- Azure VM Scale Sets Autoscale
- GCP Managed Instance Group Autoscaler
- Ray Serve: Autoscaling
- vLLM Production Scaling Guide
- Hugging Face TGI: Scaling
Tools & Libraries
- Kubernetes HPA/KEDA/Cluster Autoscaler; AWS ASG; Azure VMSS; GCP MIG.
- vLLM, Text Generation Inference (TGI), NVIDIA Triton Inference Server.
- Prometheus + Grafana; CloudWatch; Azure Monitor; GCP Cloud Monitoring.
📈
Adaptive Compute Scaling(ACS)
Dynamically adjusts computational resources based on workload demands and performance requirements
Complexity: highPattern
Core Mechanism
Adaptive Compute Scaling automatically adjusts compute capacity (pods/replicas, nodes/VMs, GPU workers) in response to real-time demand and SLOs. It combines metric-driven policies (e.g., p95 latency, queue length, utilization) with stabilization windows, cooldowns, and predictive/scheduled scaling to maintain performance while minimizing cost. Typical stacks layer service autoscaling (HPA/KEDA/Ray Serve) with cluster/VM autoscaling (Cluster Autoscaler, AWS ASG, Azure VMSS, GCP MIG) and workload-aware batching.
Workflow / Steps
- Ingest signals: p95 latency, queue depth/wait time, CPU/memory/GPU utilization, error rate, RPS/TPS.
- Define targets: SLOs (latency, availability), budgets (cost/energy), and min/max replica bounds.
- Choose policies: target tracking vs. step scaling; separate scale-up and scale-down behaviors.
- Execute scaling: adjust replicas (HPA/KEDA/Ray Serve), then provision capacity (ASG/VMSS/MIG, Cluster Autoscaler).
- Stabilize: apply stabilizationWindow/cooldowns and rate limits to avoid thrashing.
- Pre-warm: keep warm pools or scheduled/predictive scale for diurnal/burst patterns.
- Observe and tune: monitor KPIs, attribution by stage, and refine signals/thresholds regularly.
Best Practices
Use workload-appropriate signals: for GPU LLM serving, prefer p95 latency and queue depth over CPU%.
Set distinct scale-up (fast, aggressive) vs. scale-down (slow, conservative) policies with stabilization windows.
Pre-warm workers for cold-start-heavy models; use warm pools or schedule-based and predictive scaling.
Layer autoscalers: service (HPA/KEDA/Ray Serve) + cluster/VM (Cluster Autoscaler/ASG/VMSS/MIG).
Constrain with min/max replicas and max surge/drain; use Pod Disruption Budgets and graceful drains.
Batch intelligently (vLLM/TGI/Triton dynamic batching) and bound per-request tokens to keep tail latency stable.
Right-size nodes; bin-pack GPUs; use MIG/MPS where appropriate; avoid resource fragmentation.
Use SLO-driven targets (e.g., keep p95 < target) and monitor per-tenant fairness in multi-tenant clusters.
Instrument decision logs and runbooks; simulate bursts and failure modes before production.
When NOT to Use
- Ultra low-latency hard real-time paths where any cold-start or rebalancing is unacceptable.
- Stateful, heavily session-affine workloads without sharding or sticky routing strategies.
- Licensing or quota-limited services where additional replicas cannot serve more throughput.
- Air-gapped or fixed-capacity environments with no elastic backing resources.
Common Pitfalls
- Using CPU% for GPU-bound inference; choose queue latency/depth and GPU utilization instead.
- Thrashing from missing stabilization windows/cooldowns and symmetric scale policies.
- Ignoring warm-up times, image pulls, or large model load times → prolonged SLO breaches.
- No min capacity or headroom; Cluster Autoscaler lag starves pod autoscalers.
- Scaling to zero without request buffering/warmers → severe cold-start penalties.
- Over-reliance on averages; target p95/p99 and protect against bursty arrivals.
Key Features
Metric-driven autoscaling (latency, queue depth, utilization, error rate).
Event-based scaling via external systems (KEDA: Kafka, SQS, Redis, Prometheus).
Predictive and scheduled scaling for diurnal patterns and known events.
Multi-layer scaling: service, node/VM, and cluster capacity coordination.
SLO-aware policies with distinct up/down behaviors and stabilization.
Cost/energy-aware constraints and budgets with per-tenant quotas.
KPIs / Success Metrics
- Latency p50/p95/p99 vs. SLO; queue wait time; error/timeouts under burst.
- Throughput (RPS/TPS, tokens/sec) and scaling latency (time-to-capacity).
- Cost per successful request; instance/GPU-hours; over/under-provisioned time%.
- Autoscaling event rate and stability (no flapping); saturation headroom.
- Per-tenant fairness and quota adherence in multi-tenant clusters.
Token / Resource Usage
For LLM inference, track tokens/sec, concurrent requests, and KV cache memory. Use batching and context/token caps to bound tail latency; prefer queue depth + p95 latency as primary signals. For GPU nodes, monitor VRAM/SM utilization, model load times, and ensure autoscalers have capacity headroom.
- Signals: queue depth, p95 latency, GPU util, request concurrency, token budgets.
- Controls: dynamic batching, max new tokens, prompt length limits, warm pools.
- Efficiency: cache KV/prefix; shard large models; bin-pack GPUs; avoid tiny underutilized pods.
Best Use Cases
- Bursty, diurnal, or campaign-driven traffic (e-commerce, launches, live events).
- GPU LLM serving with dynamic batching and strict latency/cost SLOs.
- Event-driven processing (queues/streams) where backlog length is a clear signal.
- Multi-tenant platforms needing cost control, fairness, and predictable SLOs.
References & Further Reading
Academic Papers
Implementation Guides
- Kubernetes Horizontal Pod Autoscaler (v2)
- KEDA: Event-Driven Autoscaling
- Kubernetes Cluster Autoscaler
- AWS EC2 Auto Scaling: Target Tracking
- AWS Predictive Scaling
- Azure VM Scale Sets Autoscale
- GCP Managed Instance Group Autoscaler
- Ray Serve: Autoscaling
- vLLM Production Scaling Guide
- Hugging Face TGI: Scaling
Tools & Libraries
- Kubernetes HPA/KEDA/Cluster Autoscaler; AWS ASG; Azure VMSS; GCP MIG.
- vLLM, Text Generation Inference (TGI), NVIDIA Triton Inference Server.
- Prometheus + Grafana; CloudWatch; Azure Monitor; GCP Cloud Monitoring.