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:  Pricing Models. 
1. Abstract of Cited Paper (BIB001):  Infrastructure-as-a-Service (IaaS) cloud platforms rent resources, in the form of virtual machines (VMs), under a variety of contract terms that offer different levels of risk and cost. For example, users may acquire VMs in the spot market that are often cheap but entail significant risk, since their price varies over time based on market supply and demand and they may terminate at any time if the price rises too high. Currently, users must manage all the risks associated with using spot servers. As a result, conventional wisdom holds that spot servers are only appropriate for delay-tolerant batch applications. In this paper, we propose a derivative cloud platform, called SpotCheck, that transparently manages the risks associated with using spot servers for users. SpotCheck provides the illusion of an IaaS platform that offers always-available VMs on demand for a cost near that of spot servers, and supports all types of applications, including interactive ones. SpotCheck's design combines the use of nested VMs with live bounded-time migration and novel server pool management policies to maximize availability, while balancing risk and cost. We implement SpotCheck on Amazon's EC2 and show that it i) provides nested VMs to users that are 99.9989% available, ii) achieves nearly 5x cost savings compared to using equivalent types of on-demand VMs, and iii) eliminates any risk of losing VM state. 
2. Abstract of Cited Paper (BIB002):  The use of cloud servers to host modern Internet-based services is becoming increasingly common. Today's cloud platforms offer a choice of server types, including non-revocable on-demand servers and cheaper but revocable spot servers. A service provider requiring servers can bid in the spot market where the price of a spot server changes dynamically according to the current supply and demand for cloud resources. Spot servers are usually cheap, but can be revoked by the cloud provider when the cloud resources are scarce. While it is well-known that spot servers can reduce the cost of performing time-flexible interruption-tolerant tasks, we explore the novel possibility of using spot servers for reducing the cost of hosting an Internet-based service such as an e-commerce site that must {\em always} be on and the penalty for service unavailability is high. By using the spot markets, we show that it is feasible to host an always-on Internet-based service at one-third to one-fifth the cost of hosting the same service in the traditional fashion using dedicated non-revocable servers. To achieve these savings, we devise a cloud scheduler that reduces the cost by intelligently bidding for spot servers. Further, the scheduler uses novel VM migration mechanisms to quickly migrate the service between spot servers and on-demand servers to avoid potential service disruptions due to spot server revocations by the cloud provider. Our work provides the first feasibility study of using cloud spot markets to significantly reduce the cost of hosting always-on Internet-based services without sacrificing service availability.

Literature Review:
The current cloud pricing models can be classified into three types by pricing model: on-demand, reserved, and rebated. In on-demand mode, the provider sets a fixed unit price for each type of VM or unit of a certain resource and charges the user by units of consumption. Users are guaranteed to obtain the required resources and agreed performance, which most auto-scalers assume the target application is adopting. The reserved mode requires the user to pay an upfront fee for cheaper use of a certain amount of resources. If highly utilized, users can save a considerable sum of money than acquiring resources in on-demand mode. Providers create the rebate mode aiming to sell their spare capacity. They are usually significantly cheaper than on-demand resources. There are several ways to offer rebated resources. Amazon employed an auction-like mechanism to sell instances, called spot instances. In this mode, the user is required to submit a bid on the resources. Suppose the bid exceeds the current market price, the bid is fulfilled, and the user is only charged for the current market price. The acquired spot instances are guaranteed to have the same performance of their on-demand counterparts. However, they are reclaimed whenever the market price goes beyond a user's bidding price. Google offers its spare capacity as preemptible VMs. Different from Amazon, it sets a fixed price to the VM, which is 30% of the regular price, and the VM is available at most for 24 hours. Rebated instances are considered not suitable to host web applications that are availability critical as they can be reclaimed by providers at any time. ClusterK 3 and our previous work BIB001 demonstrated that it is feasible to build an auto-scaler utilizing spot instances by exploiting various market behaviors of different spot markets to achieve both high availability and considerable cost savings. Sharma et al. BIB002 and He et al. BIB003 endeavored to build a reliable general-purpose cloud platform upon spot instances using nested virtualization. As these systems lie in the underlying virtualization layer, all types of auto-scaling techniques can be applied to them. Pricing models can also be classified according to their billing period, which is the minimum unit consumption. Providers have set their billing period to every minute, hour, day, week, month, or year. The length of the billing period has a significant impact on the cost efficiency for elasticity. Obviously, the shorter the billing period, the more flexible and cost efficient it is for auto-scaling. If the billing period exceeds the order of hour, there is no use in applying auto-scaling to save resource costs, since provisioning for the peak load of the day incurs the same cost.