Resource-Efficient Deep Packet Inspection and Dashboard for Activity and Energy Sensing Supporting Eco-Friendly ICT Infrastructure

An organization typically has various enterprise apps and IoTs systems. An organization typically has various enterprise apps and Internet of Things (IoT) systems. The usages of these systems are typically well reflected by the packets transmitted to the network. This paper presents a unique approach to strive for energy savings in organizations by exploiting fact observables in networks. DPI is employed to dissect passing network packets and predict their protocols. Data Plan Development Kit (DPDK) is adopted to push the hardware limit and speed up the inspection. A webbased customizable dashboard is incorporated to allow human observation according to the characteristics of the organization. Several API endpoints are provided to extend the functionalities, such as ML/AI integration. An energy-sensing model is proposed to measure the energy usage or generation by various connected systems, such as IoTs, smart ACs, and solar panel controllers. The results demonstrate that the working prototype improves processing efficiency, enabling the system to handle large volumes of data up to 10GB, achieving an average packet processing efficiency of 99.991%. The system can also accurately identify various protocols, mapping the cybersecurity risks and anomalies, with an average packet loss rate of only 0.83%. The standardized UAT confirms the system’s usability, reliability, and robust security. The findings of this study are expected to provide a practical, reliable, efficient, and user-friendly network monitoring solution while contributing to the development of open-source and flexible network traffic monitoring and green technology.

Data Plane Development Kit (DPDK); Deep Packet Inspection (DPI); Energy efficiency; Green networking; nDPI

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