| dc.description.abstract | The potential cost savings and the high
scalability behind the spot instances benefit the cloud
customers compared to On-demand instances fit for
workloads that need uninterrupted compute power.
However, the termination of instances by the cloud
provider whether exceeds the customer's bid or the
unpredictable availability based on supply and demand
needs remedies such as appropriate forecasting to
optimize bidding strategies. To handle the expansion in
instance types and regions as vast datasets, effectiveness
in using the history data of different research exhibits a
significant range of instability in prediction accuracy. The
demand for profit optimization and resource analysis
entices to upgrade the traditional methods to improve
accuracy. Resolving these challenges and limitations, this
research investigates the Amazon Spot instance
forecasting adopting different Deep Neural Networks
(DNNs) considering multi-factor approach involving
encoding techniques for improvement. Multi-factor
strategy ensured compatibility and optimized model
selection. Encoding converts those categorical features
into unique integers and ensures consistent data representation. The cloud provider 's expansion leads to changing datasets and difficult handling of DNN model
running mandating a user-friendly application for the
customers. To facilitate model interaction and enhance
accessibility, a graphical user interface (GUI) is
developed where customers select his/her required
resources with the date and time they demand, and then
the predicted price will be displayed. In both past and
future contexts, bidirectional processing shows superior
performance in BiLSTM. We present this research that
assists the user in finding the predicted price in a userfriendly environment with the outperforming DNN | en_US |
