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:  Workload Prediction Data Source. 
1. Abstract of Cited Paper (BIB001):  One of the biggest advantages of cloud infrastructures is the elasticity. Cloud services are monitored and based on the resource utilization and performance load, they get scaled up or down, by provision or de-provision of cloud resources. The goal is to guarantee the customers an acceptable performance with a minimum of resources. Such Quality of Service (QoS) characteristics are stated in a contract, called Service Level Agreement (SLA) negotiated between customer and provider. The approach of this paper shows that with additional imprecise information (e.g. expected daytime/week- time performance) modeled with fuzzy logic and used in a behavior, load and performance prediction model, the up and down scaling mechanism of a cloud service can be optimized. Evaluation results confirm, that using this approach, SLA violation can be minimized.

Literature Review:
It is necessary to study the past workload history to understand the characteristics of the workload, including its intensity and mix during each time frame, to predict it. Besides workload history, individual applications can rely on available information from other aspects to predict request bursts that are impossible to be derived from past workload data alone, such as weather information for an outdoor application, and political events for a news feed application. However, the relevant parameters are application specific and thus this feature is hard to integrate into a general-purpose auto-scaler. Besides, it is also challenging to devise a prediction algorithm with real-time accuracy for resource provisioning, because there are too many parameters in the model and errors can quickly accumulate. The work by Frey et al. BIB002 considers multiple outside parameters in an auto-scaler. Their approach integrates all the prediction information into a fuzzy controller. Though it is challenging to predict the exact workload intensity using outside information, it is viable to in a timely manner detect events that may affect incoming workload intensity through social media and other channels BIB001 . Since this is a broad topic itself, we focus on prediction algorithms only based on workload history.