Optimizing IoT Cloud Architectures for Pipelining Data through Machine Learning Models
Dipanjan De et al. Dipanjan De et al., TJPRC · International Journal of Computer Networking Wireless and Mobile Communications · 2019
Rapid advancements in Internet of Things enable meaningful solutions to several problems, which were never even thought of, until recently.Machine Learning, Deep Learning and Neural Networks play a vital role in this trend, and most of the IoT platforms, pipeline data through models, for a number of reasons, including but not limited to threat detection, real time analytics, disaster prediction, etc.These models require an enormous amount of computing resources, which typically requires a GPU, and integrating these models with the cloud poses a number of major challenges involving computing paradigm of the cloud.In this paper, we propose several optimized solutions to these problems faced while pipelining enormous amount data through models in real-time.These solutions addresses issues of scalability as well, when the platform provider needs to expand and will require increased number of pipelines in real time.