PROJECT TITLE :
Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing - 2017
Elasticity is a elementary feature of cloud computing and will be thought-about as a great advantage and a key benefit of cloud computing. One key challenge in cloud elasticity is lack of consensus on a quantifiable, measurable, observable, and calculable definition of elasticity and systematic approaches to modeling, quantifying, analyzing, and predicting elasticity. Another key challenge in cloud computing is lack of effective ways that for prediction and optimization of performance and cost in an elastic cloud platform. This paper makes the subsequent vital contributions. 1st, we gift a new, quantitative, and formal definition of elasticity in cloud computing, i.e., the likelihood that the computing resources provided by a cloud platform match the present workload. Our definition is applicable to any cloud platform and will be simply measured and monitored. Furthermore, we have a tendency to develop an analytical model to check elasticity by treating a cloud platform as a queueing system, and use an eternal-time Markov chain (CTMC) model to exactly calculate the elasticity value of a cloud platform by using an analytical and numerical method primarily based on just some parameters, specifically, the task arrival rate, the service rate, the virtual machine begin-up and shut-down rates. Yet, we tend to formally outline auto-scaling schemes and point out that our model and method can be simply extended to handle arbitrarily sophisticated scaling schemes. Second, we have a tendency to apply our model and method to predict several other important properties of an elastic cloud computing system, like average task response time, throughput, quality of service, average range of VMs, average number of busy VMs, utilization, value, cost-performance ratio, productivity, and scalability. Of course, from a cloud consumer’s point of view, these performance and price metrics are even additional important than the elasticity metric. Our study in this paper has two significance. On one hand, a cloud service provider will predict its performance and cost guarantee using the results developed in this paper. On the other hand, a cloud service provider will optimize its elastic scaling theme to deliver the simplest price-performance ratio. To the simplest of our knowledge, this is often the primary paper that analytically and comprehensively studies elasticity, performance, and value in cloud computing. Our model and method considerably contribute to the understanding of cloud elasticity and management of elastic cloud computing systems.
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