A Data as a Service (DaaS) Model for GPU-based Data Analytics

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.Comment: Accepted, 23 December 2017, by the IEEE IFIP NTMS Workshop on Big Data and Emerging Trends WBD-ET 2018; it was later withdrawn because of funding issues. An extended/enhanced version will be published in future dates in related journal