A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments.¶
Lorido-Botran, T., Miguel-Alonso, J., & Lozano, J. A. (2014). A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments. Journal of Grid Computing, 12(4), 559-592.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Metaplasticity
- And in software, adaptive auto-scalers and rate-limiters adjust their own sensitivity parameters based on recent load history — slow controllers wrapped around fast ones.
This sourceSurveys cloud auto-scalers, including adaptive/control-theoretic controllers that adjust their own gain and sensitivity parameters from recent load history—slow controllers wrapped around fast resource adjustments.
- And in software, adaptive auto-scalers and rate-limiters adjust their own sensitivity parameters based on recent load history — slow controllers wrapped around fast ones.
Mechanisms¶
- Scheduled Elastic Scaling
- It misleads when the forecast is wrong — the peak arrives off-schedule, or a regime shift breaks the historical cycle
This sourceWarns that proactive auto-scaling depends on forecast accuracy, which varies with workload pattern, burstiness, history window, and prediction interval.
- It misleads when the forecast is wrong — the peak arrives off-schedule, or a regime shift breaks the historical cycle
Verification¶
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