Abstract
An auction-based cloud model is followed in the spot pricing mechanism, where the spot instances charge changes with time. The user is bound to pay for the time that is initially initiated. If the user terminates before the sessional hourly completion, then the customer will be billed on the entire hourly session. In case Amazon terminates the instance then the customer would not be billed for the partial hour. When the current spot price reduces to bid price without any notification the cloud provider terminates the spot instance, it is a big disadvantage to the time of the availability factor, which is highly important. Therefore, it is crucial for the bidder to forecast before engaging the bids for spot prices. This paper represents a technique to analyze and predict the spot prices for instances using machine learning. It also discusses implementation, explored factors in detail, and outcomes on numerous instances of Amazon Elastic Compute Cloud (EC2). This technique reduces efforts and errors for forecasting prices.
Highlights
Computation of cloud offers shared resources reachable over the Internet connectivity
The different models of cloud services are- Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Network as a Service (NaaS)
As when compared to the ARIMA model, the results show an average reduction in mean absolute percent error (MAPE), this happens by at least 95%
Summary
Computation of cloud offers shared resources reachable over the Internet connectivity. Software and Hardware are both comprised of cloud computing resources. The different models of cloud services are- Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Network as a Service (NaaS). The cloud platform has various deployment models, they are-public cloud, private cloud, community cloud and hybrid cloud. Resources are enabled by the cloud on the basis of pay as you go model. The major provider for the computation of cloud-related resources is Amazon
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