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:  Dynamic Parameters 
1. Abstract of Cited Paper (BIB001):  Virtualized data centers enable sharing of resources among hosted applications. However, it is difficult to satisfy service-level objectives(SLOs) of applications on shared infrastructure, as application workloads and resource consumption patterns change over time. In this paper, we present AutoControl, a resource control system that automatically adapts to dynamic workload changes to achieve application SLOs. AutoControl is a combination of an online model estimator and a novel multi-input, multi-output (MIMO) resource controller. The model estimator captures the complex relationship between application performance and resource allocations, while the MIMO controller allocates the right amount of multiple virtualized resources to achieve application SLOs. Our experimental evaluation with RUBiS and TPC-W benchmarks along with production-trace-driven workloads indicates that AutoControl can detect and mitigate CPU and disk I/O bottlenecks that occur over time and across multiple nodes by allocating each resource accordingly. We also show that AutoControl can be used to provide service differentiation according to the application priorities during resource contention. 
2. Abstract of Cited Paper (BIB002):  Dynamic server provisioning is critical to quality-of-service assurance for multi-tier Internet applications. In this paper, we address three important and challenging problems. First, we propose an efficient server provisioning approach on multi-tier clusters based on an end-to-end resource allocation optimization model. It is to minimize the number of servers allocated to the system while the average end-to-end delay guarantee is satisfied. Second, we design a model-independent fuzzy controller for bounding an important performance metric, the 90 th -percentile delay of requests flowing through the multi-tier architecture. Third, to compensate for the latency due to the dynamic addition of servers, we design a self-tuning component that adaptively adjusts the output scaling factor of the fuzzy controller according to the transient behavior of the end-to-end delay. Extensive simulation results, using one representative customer behavior model in a typical three-tier web cluster, demonstrate that the provisioning approach is able to significantly reduce the server utilization compared to an existing representative approach. The approach integrated with the model-independent self-tuning fuzzy controller can efficiently assure the average and the 90 th -percentile end-to-end delay guarantees on multi-tier server clusters. 
3. Abstract of Cited Paper (BIB003):  Elasticity - where systems acquire and release resources in response to dynamic workloads, while paying only for what they need - is a driving property of cloud computing. At the core of any elastic system is an automated controller. This paper addresses elastic control for multi-tier application services that allocate and release resources in discrete units, such as virtual server instances of predetermined sizes. It focuses on elastic control of the storage tier, in which adding or removing a storage node or "brick" requires rebalancing stored data across the nodes. The storage tier presents new challenges for elastic control: actuator delays (lag) due to rebalancing, interference with applications and sensor measurements, and the need to synchronize the multiple control elements, including rebalancing. We have designed and implemented a new controller for elastic storage systems to address these challenges. Using a popular distributed storage system - the Hadoop Distributed File System (HDFS) - under dynamic Web 2.0 workloads, we show how the controller adapts to workload changes to maintain performance objectives efficiently in a pay-as-you-go cloud computing environment. 
4. Abstract of Cited Paper (BIB004):  Elasticity is the ability of a cloud infrastructure to dynamically change the amount of resources allocated to a running service as load changes. We build an autonomous elasticity controller that changes the number of virtual machines allocated to a service based on both monitored load changes and predictions of future load. The cloud infrastructure is modeled as a G/G/N queue. This model is used to construct a hybrid reactive-adaptive controller that quickly reacts to sudden load changes, prevents premature release of resources, takes into account the heterogeneity of the workload, and avoids oscillations. Using simulations with Web and cluster workload traces, we show that our proposed controller lowers the number of delayed requests by a factor of 70 for the Web traces and 3 for the cluster traces when compared to a reactive controller. Our controller also decreases the average number of queued requests by a factor of 3 for both traces, and reduces oscillations by a factor of 7 for the Web traces and 3 for the cluster traces. This comes at the expense of between 20% and 30% over-provisioning, as compared to a few percent for the reactive controller. 
5. Abstract of Cited Paper (BIB005):  Cloud elasticity is the ability of the cloud infrastructure to rapidly change the amount of resources allocated to a service in order to meet the actual varying demands on the service while enforcing SLAs. In this paper, we focus on horizontal elasticity, the ability of the infrastructure to add or remove virtual machines allocated to a service deployed in the cloud. We model a cloud service using queuing theory. Using that model we build two adaptive proactive controllers that estimate the future load on a service. We explore the different possible scenarios for deploying a proactive elasticity controller coupled with a reactive elasticity controller in the cloud. Using simulation with workload traces from the FIFA world-cup web servers, we show that a hybrid controller that incorporates a reactive controller for scale up coupled with our proactive controllers for scale down decisions reduces SLA violations by a factor of 2 to 10 compared to a regression based controller or a completely reactive controller.

Literature Review:
Besides static cooling time, approaches that dynamically adjust parameters to reduce the possibility of causing oscillation have been proposed. Lim et al. BIB004 BIB001 described an approach through dynamically tuning triggering thresholds for scale-down operations. The core idea is to increase the scale-down threshold when more resources are allocated to decrease the target utilization range and vice versa when resources are deallocated, which can effectively mitigate oscillation if the application resource requirement varies significantly during peak time and nonpeak time. Usually, during nonpeak time, a large target range is desirable to avoid the situation described in the poorly configured example, while during peak hours, a small target range is preferred to keep the utilization as close to the scale-out threshold as possible. Bodik et al.  introduced a mechanism that they call "hysteresis parameters," to reduce oscillation. These parameters control how quickly the controller provisions and deprovisions resources. They are determined by simulations using Pegasus, an algorithm that compares different control settings to search the suitable one. Padala et al. BIB002 used a stability factor to adjust the aggressiveness of the approach. As the factor increases, the control objective will be more affected by the previous allocation. As a result, the auto-scaler responds more slowly to the resulting errors caused by the previous actions in the following resource scaling windows and thus reduces oscillations. Lama et al. BIB003 employed a similar approach on their fuzzy-based auto-scaler. Their approach is more advanced and flexible as the factor is self-tunable during runtime according to the resulting errors. Ali-Eldin et al. BIB005 BIB006 utilized a parameter to dynamically tune the prediction horizon of the workload prediction algorithm according to its real-time error rate. In this way, the auto-scaler can reduce the number of scaling oscillations caused by inaccurate workload predictions.