An Elastic Resource Scaling System For Iaas Cloud Essay

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In this section, we review three recent research articles that address different aspects of the elasticity problem in the cloud, which can be used to improve cloud elasticity. In the first paper, Islam et al. [3] proposed ways to quantify the elasticity concept in a cloud for a consumer. In the second paper, Nguyen et al. [13] proposed an elastic resource scaling system for IaaS cloud providers to predict the future resource needs of a cloud application. In the third paper, Han et al. [14] proposed an elastic scaling approach to detect and analyze bottlenecks within multi-tier cloud applications. In the following sections, we summarize and critique these articles and then synthesize the articles. 3.1 How a consumer can measure elasticity for cloud platform 3.1.1 Summary In the paper [3], the authors propose a way for consumers to measure the elasticity properties of different cloud platforms. The objective of the research is to help a consumer to measure and compare elasticity of cloud providers (e.g., how elastic is Amazon EC2 compared to Microsoft Azure) using the available information provided through cloud platform’s API. The authors defined the elasticity metrics based on financial penalties for over-provisioning and under-provisioning of the cloud resources. Over-provisioning is a state when a consumer is paying more than necessary for the allocated resources to support a workload while the costs for under-provisioning is the result of unacceptable latency or unmet
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