---
type: claim
domain: internet-finance
description: "Different thresholds for adding versus removing resources prevent rapid oscillation in auto-scaling systems"
confidence: proven
source: "Tournaire et al., 'Optimal Control Policies for Resource Allocation in the Cloud' (2021); established operations research principle"
created: 2026-03-11
sourced_from:
- inbox/archive/internet-finance/2021-04-00-tournaire-optimal-control-cloud-resource-allocation-mdp.md
---

# Hysteresis in autoscaling prevents oscillation by using asymmetric thresholds for scale-up and scale-down

Hysteresis in auto-scaling systems—using different thresholds for scaling up versus scaling down—prevents oscillation where resources are rapidly added and removed in response to workload fluctuations near a single threshold.

For example, a system might scale up when queue length reaches 10 but only scale down when queue length drops to 3. This asymmetry creates a "dead zone" between thresholds that absorbs short-term fluctuations without triggering scaling actions.

Tournaire et al. (2021) demonstrate this principle in cloud VM provisioning, where MDP-based optimal control policies automatically discover the optimal hysteresis gap given cost structure (energy + SLA violations). The principle is well-established in operations research and control theory more broadly.

## Why Hysteresis Works

Without hysteresis, a system operating near a single threshold (e.g., scale at queue=5) will constantly add and remove resources as the queue fluctuates around that value. Each scaling action has overhead cost (VM startup time, worker initialization, context switching), making oscillation expensive.

Hysteresis trades increased resource utilization during the dead zone (queue between 3-10 in the example) for reduced scaling overhead and more stable operation.

## Application to Pipeline Management

For autonomous pipeline workers:
- Scale up threshold: unprocessed queue > N sources
- Scale down threshold: unprocessed queue < M sources (where M < N)
- Dead zone width (N-M) should be tuned to workload volatility and worker startup cost

The optimal gap depends on:
- Worker initialization time (longer startup → wider gap)
- Cost per worker-minute (higher cost → narrower gap, more aggressive scaling down)
- Workload volatility (higher variance → wider gap to avoid thrashing)

---

Relevant Notes:
- [[mdp-based-autoscaling-with-hysteresis-outperforms-simple-threshold-heuristics-for-cloud-resource-allocation]]

Topics:
- domains/internet-finance/_map
