# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
## 2 Related Works
### Sleep Control Methods  
Various methods have been proposed to increase the energy efficiency of an MEC system by controlling the working mode of the MECSs. These methods can be broadly categorized into distributed methods and centralized methods. Distributed methods use meta-heuristic optimizations, a bio-inspired method, and game theory. Centralized methods use the Lyapunov optimization framework, machine learning, and deep learning methods. The authors in [33] use the particle swarm optimization (PSO) algorithm to control the operation mode of MECSs. They formulate the MECS sleep control problem as a two-dimensional optimization problem. Then, they propose a user connection matrix-based AP sleeping method by using PSO. In the work presented in [12], a bio-inspired method for controlling the sleep states of MECSs is proposed, drawing inspiration from the inter-cell signaling mechanism. At the end of each time slot, each MECS engages in periodic load information exchanges with its neighboring MECSs. Through a distributed process involving the comparison of relative load levels with those of neighboring MECSs, each MECS autonomously determines its operational mode. In the study presented in [34], the authors employ the minority game theory to decide the operational mode of an MECS. They tackle the distributed computation offloading problem, taking into account the determination of the MECS operation mode. The minority game is utilized to seek an equilibrium state that optimally balances the latency of user tasks and the energy consumption within the MEC system. In these distributed methods, each MECS iteratively determines its sleep mode until the optimal sleep MECSs from the perspective of the MEC system is obtained. Since the iterative process takes time until a consensus among MECSs are reached, distributed methods are slow. In addition, these techniques result in an elevated signaling overhead due to the necessity of control message exchanges among MECSs. In centralized methods, each MECS or a central server determines the sleep MECSs based on the service quality provided by the MEC system, and the amount of energy consumed in the system in an on-demand manner. The Lyapunov optimization framework is frequently employed to address optimization problems involving unknown future values in the context of long-term average cost. It transforms the long-term average cost optimization problem into a per-time-slot cost optimization problem, enabling the development of an online algorithm that utilizes only currently available information. In [35], the problem of minimizing long-term average delay under power consumption constraints is formulated. The Lyapunov optimization framework is applied to solve the formulated problem by optimizing the sleep and offloading decisions of MECSs. In [36], the problem of minimizing long-term average total energy consumption under reliability constraints is established. An online algorithm is devised using the Lyapunov optimization framework, placing MECSs into a sleep state whenever possible. In [9], the authors formulate the energy optimization problem under delay constraints. Leveraging the Lyapunov optimization framework, they convert the long-term energy minimization problem into a per-time-slot problem and propose an online sleep control method under non-uniform traffic distribution in an MEC system. Various machine learning and deep learning methodologies have been proposed to address the MECS sleep control problem. In [13], an online MECS mode switching algorithm is introduced, leveraging a linear regression method to predict user distribution and service requests. By calculating the utility value of each MECS based on the predicted values, MECSs are selected for sleep during the next time slot by comparing their utility values with a predefined threshold. The authors in [14] present a method that optimizes the number of active MECSs using a deep learning model.