teleo/teleo-codex
8
0
Fork 0
Code Issues 22 Pull requests 43 Projects Releases Packages Wiki Activity Actions 422
teleo-codex/domains/internet-finance/mdp-based-autoscaling-with-hysteresis-outperforms-simple-threshold-heuristics-for-cloud-resource-allocation.md
m3taversal be8ff41bfe link: bidirectional source↔claim index — 414 claims + 252 sources connected 
Wrote sourced_from: into 414 claim files pointing back to their origin source.
Backfilled claims_extracted: into 252 source files that were processed but
missing this field. Matching uses author+title overlap against claim source:
field, validated against 296 known-good pairs from existing claims_extracted.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-21 11:55:18 +01:00

3.1 KiB
Raw Blame History

type domain description confidence source created related reweave_edges sourced_from
claim internet-finance Structured MDP algorithms that incorporate hysteresis properties achieve better performance and faster execution than simple threshold heuristics in cloud VM provisioning likely Tournaire et al., 'Optimal Control Policies for Resource Allocation in the Cloud' (2021) 2026-03-11
hysteresis-in-autoscaling-prevents-oscillation-by-using-asymmetric-thresholds-for-scale-up-and-scale-down
optimal-queue-policies-have-threshold-structure-making-simple-rules-near-optimal
pipeline-state-space-size-determines-whether-exact-mdp-solution-or-threshold-heuristics-are-optimal
hysteresis-in-autoscaling-prevents-oscillation-by-using-asymmetric-thresholds-for-scale-up-and-scale-down|related|2026-04-18
optimal-queue-policies-have-threshold-structure-making-simple-rules-near-optimal|related|2026-04-19
pipeline-state-space-size-determines-whether-exact-mdp-solution-or-threshold-heuristics-are-optimal|related|2026-04-19
inbox/archive/internet-finance/2021-04-00-tournaire-optimal-control-cloud-resource-allocation-mdp.md

MDP-based autoscaling with hysteresis outperforms simple threshold heuristics for cloud resource allocation

Markov Decision Process formulations that incorporate hysteresis properties (different thresholds for scaling up versus scaling down) outperform simple threshold heuristics in both execution time and accuracy for cloud auto-scaling problems. The MDP approach automatically discovers optimal hysteresis thresholds rather than requiring manual tuning.

The problem formulation treats VM provisioning as a sequential decision problem where:

  • States = queue lengths + active VMs
  • Actions = add/remove VMs
  • Rewards = negative cost (energy + SLA violations)

Value iteration and policy iteration algorithms find optimal threshold policies that prevent oscillation by using different thresholds for scaling up (e.g., queue=10) versus scaling down (e.g., queue=3).

Tournaire et al. (2021) demonstrate that structured MDP algorithms incorporating hysteresis properties outperform heuristic approaches in both execution time and accuracy. The key insight is that hysteresis—different thresholds for scaling up versus scaling down—prevents oscillation, and MDP algorithms can discover these optimal thresholds automatically rather than through manual tuning.

Relevance to Pipeline Architecture

This formulation maps directly to autonomous pipeline management:

  • States = (unprocessed queue, in-flight extractions, open PRs, active workers)
  • Actions = (spawn worker, kill worker, wait)
  • Cost = (Claude compute cost per worker-minute + delay cost per queued source)

The hysteresis insight is particularly valuable for preventing worker thrashing in variable-load scenarios. Simple threshold policies (scale up at queue=N, scale down at queue=M where M < N) provide reasonable baseline performance, but MDP optimization can find better thresholds given cost structure and workload patterns.

Relevant Notes:

  • domains/internet-finance/_map

Topics:

  • domains/internet-finance/_map