Energy-delay and energy-age trade-off in sleep-wake scheduling of queuing systems

Abstract

Minimizing energy consumption while satisfying Quality of Service (QoS) requirements has become a critical objective for the efficient and successful deployment of next-generation networks (NGNs). Sleep-wake scheduling stands out as a prominent approach among various mechanisms that aim to enhance energy efficiency. This mechanism allows the node to save energy by temporarily turning off the components of its transmission module, albeit at the cost of potential QoS degradation. Numerous sleep-wake scheduling strategies have been proposed in both academic and industrial contexts to manage the transition between Sleep and Active operational modes. Most of the existing methods focus on a narrow set of QoS metrics while employing conventional algorithmic approaches. As communication systems grow in complexity and diversity, there is an increasing need for more advanced mechanisms that can accommodate a broader range of QoS considerations. In parallel, the advances in computational capabilities support the integration of sophisticated control systems, such as model predictive control (MPC), which offer greater adaptability to dynamic environments. Motivated by these developments, this thesis explores the application of innovative sleep-wake scheduling strategies in communication systems. In particular, we consider (i) delay (ii) information freshness, as the QoS metrics for the analysis and control of sleep-wake scheduling in two separate settings. In the first setting, we introduce a novel open-loop dynamic coalescing method for Energy Efficient Ethernet (EEE), which leverages queuing theory and is inspired by MPC. Our first proposed method, referred to as MPC-mean, aims to reduce the energy consumption of Ethernet links while maintaining constraints on the average queue waiting time. Addition ally, we propose another MPC inspired approach, called MPC-tail, which focuses on controlling the tail distribution of queue waiting times. Beyond Ethernet interfaces, we investigate information freshness in Internet of Things (IoT) sensor networks, where nodes alternate between Sleep (or Low-power) and Active modes. We analyze the trade-off between energy consumption and the Age of Incorrect Information (AoII) using timer-based sleep-wake scheduling. To validate the performance and efficiency of our proposed techniques, we provide extensive numerical simulations.