  Cloud-based services with resources to be provisioned for consumers are
increasingly the norm, especially with respect to Big data, spatiotemporal data
mining and application services that impose a user's agreed Quality of Service
(QoS) rules or Service Level Agreement (SLA). Considering the pervasive nature
of data centers and cloud system, there is a need for a real-time analytics of
the systems considering cost, utility and energy. This work presents an overlay
model of GPU system for Data As A Service (DaaS) to give a real-time data
analysis of network data, customers, investors and users' data from the
datacenters or cloud system. Using a modeled layer to define a learning
protocol and system, we give a custom, profitable system for DaaS on GPU. The
GPU-enabled pre-processing and initial operations of the clustering model
analysis is promising as shown in the results. We examine the model on
real-world data sets to model a big data set or spatiotemporal data mining
services. We also produce results of our model with clustering, neural
networks' Self-organizing feature maps (SOFM or SOM) to produce a distribution
of the clustering for DaaS model. The experimental results thus far show a
promising model that could enhance SLA and or QoS based DaaS.
