Energy Aware Strategy for Discrete Event Systems using Inhibitor P-Time Petri Nets and Deep Reinforcement Learning

Energy considerations become a critical issue for modern man-made systems and the need for efficient ecoresponsible solutions ensuring energy savings is crucial. This paper develops an energy aware method allowing to optimize the energy consumption of data centers systems thanks to the introduction of Inhibitor P-Time Petri nets (IP-TPN) and Deep Reinforcement Learning techniques. Indeed, thanks to a schedulability analysis method and being given an energy cost function, the global energy consumed for a particular behavior of the system considered can be computed. Furthermore, Petri nets are a good framework for deep reinforcement learning, on the one hand because the cost function we introduce in this paper will naturally train an agent to minimize the total cost according to random inputs, and on the other hand because the observation space containing the tokens as well as their age in the square is high dimensional, which makes traditional algorithms less efficient than deep reinforcement learning.