Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey <s> Exhaustion <s> An important feature of cloud computing is its elasticity, that is, the ability to have resource capacity dynamically modified according to the current system load. Auto-scaling is challenging because it must account for two conflicting objectives: minimising system capacity available to users and maximising QoS, which typically translates to short response times. Current auto-scaling techniques are based solely on load forecasts and ignore the perception that users have from cloud services. As a consequence, providers tend to provision a volume of resources that is significantly larger than necessary to keep users satisfied. In this article, we propose a scheduling algorithm and an auto-scaling triggering technique that explore user patience in order to identify critical times when auto-scaling is needed and the appropriate volume of capacity by which the cloud platform should either extend or shrink. The proposed technique assists service providers in reducing costs related to resource allocation while keeping the same QoS to users. Our experiments show that it is possible to reduce resource-hour by up to approximately 8% compared to auto-scaling based on system utilisation. <s> BIB001 </s> Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey <s> Exhaustion <s> 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. <s> BIB002
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.