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:  Hybrid 
1. Abstract of Cited Paper (BIB001):  The adoption of virtualization and Cloud Computing technologies promises a number of benefits such as increased flexibility, better energy efficiency and lower operating costs for IT systems. However, highly variable workloads make it challenging to provide quality-of-service guarantees while at the same time ensuring efficient resource utilization. To avoid violations of service-level agreements (SLAs) or inefficient resource usage, resource allocations have to be adapted continuously during operation to reflect changes in application workloads. In this paper, we present a novel approach to self-adaptive resource allocation in virtualized environments based on online architecture-level performance models. We present a detailed case study of a representative enterprise application, the new SPECjEnterprise2010 benchmark, deployed in a virtualized cluster environment. The case study serves as a proof-of-concept demonstrating the effectiveness and practical applicability of our approach. 
2. Abstract of Cited Paper (BIB002):  Elastic resource provisioning is a key feature of cloud computing, allowing users to scale up or down resource allocation for their applications at run-time. To date, most practical approaches to managing elasticity are based on allocation/de-allocation of the virtual machine (VM) instances to the application. This VM-level elasticity typically incurs both considerable overhead and extra costs, especially for applications with rapidly fluctuating demands. In this paper, we propose a lightweight approach to enable cost-effective elasticity for cloud applications. Our approach operates fine-grained scaling at the resource level itself (CPUs, memory, I/O, etc) in addition to VM-level scaling. We also present the design and implementation of an intelligent platform for light-weight resource management of cloud applications. We describe our algorithms for light-weight scaling and VM-level scaling and show their interaction. We then use an industry standard benchmark to evaluate the effectiveness of our approach and compare its performance against traditional approaches. 
3. Abstract of Cited Paper (BIB003):  Enterprise clouds today support an on demand resource allocation model and can provide resources requested by applications in a near online manner using virtual machine resizing or cloning. However, in order to take advantage of an on demand resource model, enterprise applications need to be automatically scaled in a way that makes the most efficient use of resources. In this work, we present the SmartScale automated scaling framework. SmartScale uses a combination of vertical (adding more resources to existing VM instances) and horizontal (adding more VM instances) scaling to ensure that the application is scaled in a manner that optimizes both resource usage and the reconfiguration cost incurred due to scaling. The SmartScale methodology is proactive and ensures that the application converges quickly to the desired scaling level even when the workload intensity changes significantly. We evaluate SmartScale using real production traces on Olio, an emerging cloud benchmark, running on a ???-based cloud testbed. We present both theoretical and experimental evidence that comprehensively establish the effectiveness of SmartScale. 
4. Abstract of Cited Paper (BIB004):  Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low. 
5. Abstract of Cited Paper (BIB005):  Cloud computing offers the flexibility to dynamically size the infrastructure in response to changes in workload demand. While both horizontal and vertical scaling of infrastructure is supported by major cloud providers, these scaling options differ significantly in terms of their cost, provisioning time, and their impact on workload performance. Importantly, the efficacy of horizontal and vertical scaling critically depends on the workload characteristics, such as the workload's parallelizability and its core scalability. In today's cloud systems, the scaling decision is left to the users, requiring them to fully understand the tradeoffs associated with the different scaling options. In this paper, we present our solution for optimizing the resource scaling of cloud deployments via implementation in OpenStack. The key component of our solution is the modelling engine that characterizes the workload and then quantitatively evaluates different scaling options for that workload. Our modelling engine leverages Amdahl's Law to model service time scaling in scaleup environments and queueing-theoretic concepts to model performance scaling in scale-out environments. We further employ Kalman filtering to account for inaccuracies in the model-based methodology, and to dynamically track changes in the workload and cloud environment.

Literature Review:
As mentioned previously, horizontal scaling is slow in provisioning and vertical scaling is restricted by resources available in the host. It is natural to employ vertical scaling and horizontal scaling together to mitigate these issues. The idea is to utilize vertical scaling when possible to quickly adapt to changes and only conduct horizontal scaling when vertical scaling reaches its limit. Urgaonkar et al. BIB001 , Huber et al. BIB002 , Rui et al. BIB003 , and Yang et al. BIB005 followed this strategy. Mixing vertical scaling and horizontal scaling can also bring cost benefits. Dutta et al. BIB004 and Gandhi et al. BIB006 explored optimization techniques to search for the scaling plan that incurs the least cost with a hybrid of vertical and horizontal scaling. Vertical scaling and horizontal scaling can be separately applied to different components of the application as well since some parts such as database servers are difficult to horizontally scale. Nisar et al.  demonstrated this approach in a case study.