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¶
3 citations across 3 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.
Supported in partVerified against the publisher's abstract
The review's abstract only sorts cloud auto-scaling techniques into five categories, naming control theory as one; it does not mention rate-limiters, sensitivity parameters, or slow controllers wrapping fast ones.
“We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis.”
- 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.
Domain-specific¶
- Min–Max Inventory
- in cloud autoscaling (add capacity above a high-water mark, shed it below a low-water mark, the band preventing scale-up/scale-down thrash on small fluctuations)
This sourceSurvey of cloud auto-scaling that identifies static threshold-based rules as one of five main technique families.
Supported in partVerified against the source
- in cloud autoscaling (add capacity above a high-water mark, shed it below a low-water mark, the band preventing scale-up/scale-down thrash on small fluctuations)
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¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 2 of 3 citations: 2 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
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