Autonomic energy management with Fog Computing. (July 2021) - Sampaio, Hugo Vaz; Westphall, Carlos Becker; Koch, Fernando; do Nascimento Boing, Ricardo; Santa Cruz, René Nolio
Abstract: We introduce an Autonomic System to perform management of energy consumption in Internet of Things (IoT) devices and Fog Computing, including an advanced orchestration mechanisms to manage dynamic duty cycles for extra energy savings. The solution works by adjusting Home (H) and Away (A) cycles based on contextual information, like environmental conditions, user behavior, behavior variation, regulations on energy and network resources utilization, among others. Performance analysis through a proof-of-concept implementation presents average energy savings of up to 61.51% when augmenting with a scheduling system and variable long sleep cycles (LS), and potential for 75.9% savings in specific conditions. We also concluded that there is no linear relation between increasing LS time and additional savings. The significance of this research is to promote autonomic management as a solution to develop more energy efficient buildings and smarter cities, towards sustainable goals. Graphical abstract: Highlights: Internet of Things energy management in large scale environment with Fog Computing. Fog (and Mist) network performance analysis with queueing theory. Network delay and device's duty-cycle real-time system's limitations. Network energy savings with Autonomic device's dynamic duty-cycle operations.