You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey 
Chapter Title:  Exhaustion 
1. Abstract of Cited Paper (BIB001):  Auto-scaling is a key feature in clouds responsible for adjusting the number of available resources to meet service demand. Resource pool modifications are necessary to keep performance indicators, such as utilisation level, between user-defined lower and upper bounds. Auto-scaling strategies that are not properly configured according to user workload characteristics may lead to unacceptable QoS and large resource waste. As a consequence, there is a need for a deeper understanding of auto-scaling strategies and how they should be configured to minimise these problems. In this work, we evaluate various auto-scaling strategies using log traces from a production Google data centre cluster comprising millions of jobs. Using utilisation level as performance indicator, our results show that proper management of auto-scaling parameters reduces the difference between the target utilisation interval and the actual values -- we define such difference as Auto-scaling Demand Index. We also present a set of lessons from this study to help cloud providers build recommender systems for auto-scaling operations.

Literature Review:
The above methods are only capable of mitigating the possibility of oscillations. If in theory we can identify the settings that might cause oscillations and thus pose restrictions on such settings, the risk of oscillation will be eliminated. Cunha et al. BIB001 and Netto et al. BIB002 adopted this approach and presented models that identify the potential oscillation conditions in their rule-based autoscalers.