A lightweight heuristic for cost-efficient IaaS auto-scaling of small-scale web applications

Abstract

Abstract Pay-per-use Infrastructure-as-a-Service (IaaS) makes web-application hosting affordable for small organisations, yet cost-efficient elasticity remains unsolved for deployments of two to eight virtual machine instances: enterprise auto-scalers demand weeks of traffic history and dozens of tuning parameters, while naive fixed-threshold policies react only after service degradation has begun. This paper proposes the Lightweight Adaptive Scheduling Heuristic (LASH), an O (1)-state two-phase algorithm that minimises hourly IaaS cost subject to a 200 ms P99 latency SLA. Phase 1 applies double exponential smoothing to forecast request rate one VM warm-up horizon ahead; phase 2 selects the minimum-cost instance count while a two-clause minimum-lifetime / billing-aware flag suppresses premature scale-in. LASH is evaluated against four competitive baselines (fixed-threshold, moving-average, recursive-least-squares regression, and AWS Target Tracking) in a trace-driven discrete-time simulation calibrated to AWS EC2 and Azure VM pricing, instance warm-up, and queueing behaviour, across six synthetic load profiles ( $$n = 10$$ seeded runs per cell; 600 simulated experiments) and, for the AWS EC2 configuration only, the real FIFA World Cup 1998 24-hour production trace ( $$n = 10$$ replays). In simulation, LASH dominates every baseline on cost across all six profiles and on P99 latency across all but the lowest-CoV profiles, where the regression forecaster $$\pi _\text {LR}$$ is competitive. The mean cost reduction versus the fixed-threshold baseline is 41.9 % (BCa 95 % CI [40.7 %, 43.1 %], quantifying simulator run-to-run variability rather than deployment uncertainty), with a 23.7 % P99 latency reduction and a 75.9 % SLA-violation reduction; against a CPU-target reactive policy modelled on AWS Target Tracking the cost reduction is 13.5 %. All improvements are statistically significant under the matched-block Friedman test ( $$p < 0.001$$ , Friedman $$\varepsilon ^{2} = 0.97$$ ) and a corroborating linear mixed-effects model on run-level data. As a simulation study, these results characterise expected behaviour under the modelling assumptions stated in the paper and are not a substitute for measurement on production infrastructure.