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:  Self-Adaptive 
1. Abstract of Cited Paper (BIB001):  Data center virtualization allows cost-effective server consolidation which can increase system throughput and reduce power consumption. Resource management of virtualized servers is an important and challenging task, especially when dealing with fluctuating workloads and complex multi-tier server applications. Recent results in control theory-based resource management have shown the potential benefits of adjusting allocations to match changing workloads. This paper presents a new resource management scheme that integrates the Kalman filter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. The novelty of our approach is the use of the Kalman filter-the optimal filtering technique for state estimation in the sum of squares sense-to track the CPU utilizations and update the allocations accordingly. Our basic controllers continuously detect and self-adapt to unforeseen workload intensity changes. Our more advanced controller self-configures itself to any workload condition without any a priori information. Indicatively, it results in within 4.8% of the performance of workload-aware controllers under high intensity workload changes, and performs equally well under medium intensity traffic. In addition, our controllers are enhanced to deal with multi-tier server applications: by using the pair-wise resource coupling between application components, they provide a 3% on average server performance improvement when facing large unexpected workload increases when compared to controllers with no such resource-coupling mechanism. We evaluate our techniques by controlling a 3-tier Rubis benchmark web site deployed on a prototype Xen-virtualized cluster. 
2. Abstract of Cited Paper (BIB002):  Nowadays, more and more the industry and market depend on cloud-based infrastructures for delivering IT services. To this aim cloud-based infrastructures are changing continuously, increasing their complexity especially for the management of cloud resources. Control and management of resources (e.g., virtual machines, VMs) are of paramount importance to adjust resources automatically allocated to an application and for delivering quality-assured services to final users. In this paper, we propose a feedback-based control approach for the management of VMs in the AWS EC2 public cloud. First, we evaluate the proposed Gain Scheduling policy against different workloads. Second, we provide results on the robustness of the proposed Gain Scheduling policy in presence of failures. Finally, we compare our approach to state-of-the-art control approaches for cloud resources. Our results indicate that the proposed control strategy guarantees, without the need of a priori information on system dynamics or complex estimations of the operating conditions, high performance with respect to both constant and time- varying workloads as well as in spite of sudden VM failures.

Literature Review:
Self-adaptive auto-scalers are superior to their nonadaptive counterparts. Though the core control models in them are fixed as well, they are capable of autonomously tuning themselves according to real-time quality of the control actions observed. In this way, the designer only needs to determine the core control model, such as whether it is linear or quadratic, and the auto-scaler will adjust and evolve itself to meet the target performance. This feature can be implemented through extending existing self-adaptive control frameworks in control theory, such as the works done by Kamra et al. BIB001 , Kalyvianaki et al. BIB002 , and Grimaldi et al. BIB003 . Self-adaptivity can also be realized through dynamic measurement or correction of parameters in analytical models and machinelearning approaches, such as reinforcement learning and regression. Detailed explanations of them are given in Section 8. The benefit of introducing self-adaptivity is that it significantly reduces the amount of offline preparation required to utilize an auto-scaler. Furthermore, once substantial changes are detected, self-adaptive approaches can autonomously abort the current model and retrain itself, thus reducing maintenance efforts. Their primary drawback is that it usually takes time for them to converge to a good model and the application will suffer from bad performance during the early stage of training.