At present, a promising solution to address this challenge is cloud computing [ 3- 5], which exploits the full potential of multiple computers and delivers computation and storage as dynamically allocated virtual resources via the Internet [ 6].
Moreover, several key optimization strategies of computation and storage based on the compute unified device architecture (CUDA) were adopted to accelerate the 3D stationary-phase QPSTM algorithm.
Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of ''being near" to the users or terminal devices.
Cloud computing gives users much freedom on where they host their computation and storage.
The prototype integrates in-house solvent and process design tools with external distributed computation and storage resources as well as web-databases and computer networks.
Cloud computing has been the enabling technology for shifting mass scale computation and storage requirements from individually owned clients towards an on-demand and utility styled alternative that provides many services.
Cloud computing, on the other hand leverages the capability of IoT by providing computation and storage power to each smart object.
As computation and storage continues to move from desktops to large internet services, computing platforms running such services are transforming into warehouse scale computers.
The Galaxy framework encapsulates high-end computational tools and provides intuitive user interface while hiding the details of computation and storage management [ 40].
Currently, such resources are mostly being provided by cloud service providers, where the computation and storage capacity exists.
However, certain redundancies in the training templates can be reduced to improve computation and storage efficiency.
