IoT-Pi: A machine learning-based lightweight framework for cost-effective distributed computing using IoT

It is possible to develop intelligent and self-adaptive application on the edge nodes with rapid increase in computational capability of Internet of Things (IoT) devices. With the rapid growth of cloud technologies, the demand for hybrid architecture with cloud and IoT has also been boosted as well. To satisfy the critical and comprehensive requirements in the architecture evolution, we proposed a lightweight framework called IoT-Pi to provide a 3-phase (sample, learn, adapt) life cycle management of cloud resources with machine learning prediction working on IoT edge nodes using Raspberry Pi device. Compared to the traditional interference by human beings in the field of system administration, the accuracy rate of machine learning prediction in the proposed technique for some algorithms reached over 70%, which demonstrates the feasibility and effectiveness of running cloud resource management on an IoT devices such as Raspberry Pi.

Automated summary

This article discusses the possibility of developing intelligent and self-adaptive applications on edge nodes in the Internet of Things (IoT) with the increasing computational capability. The proposed lightweight framework, called IoT-Pi, provides a 3-phase life cycle management of cloud resources on IoT edge nodes using machine learning prediction. The accuracy rate of machine learning prediction for certain algorithms reached over 70%, demonstrating the feasibility and effectiveness of running cloud resource management on IoT devices like Raspberry Pi.