# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 2 Related Works
### Energy Saving in a MEC System  
Various technical approaches have been adopted to reduce the energy consumption of MEC systems. The authors of [5] proposed a method that minimizes the long-term energy consumption of a backhaul network connecting MECSs. To achieve this, they adopted the Lyapunov-based approach and periodically decided whether to migrate tasks from one MECS to another. The Lyapunov optimization approach was also adopted in [7] to incorporate a MEC system into dense cellular networks. However, the authors considered only the task offloading between MECSs and a cloud server when they made joint computation offloading and MECS sleep decisions. Deep reinforcement learning (DRL)-based methods were developed in [20; 21]. The authors in [20] formulated an optimization problem that minimizes the energy consumption required to process all tasks imposed on a MEC system while satisfying the delay constraints of each UE. They presented conventional decomposition and deep Q-learning-based methods that solved the formulated problem by determining the offloading ratio of each UE and the optimal frequency of a central processing unit that a MECS should allocate to each UE. However, they considered only a simple environment, in which multiple UEs offload their tasks to a single MECS. In [21], the authors considered a NOMA-based MEC system that allows the use of computational resources of idle devices through machine-to-machine (M2M) task-offloading. They formulated an optimization problem for minimizing the energy consumption of the system under task-delay constraints and proposed a DRL-based sleep scheduling method for M2M devices. In [22], the authors investigated the effectiveness of a queuing model compatible with the dynamics of traffic flow over a long-time scale. They employed the \(M^{x}/D/N\) queuing system and derived the minimum number of active processing units that can guarantee the desired upper bound on the average queuing delay. Through simulation studies using real-world traffic traces, they demonstrated that the queuing model underestimates the delay and imparts aggressiveness to the sleeping strategy with respect to energy consumption. The authors in [23] defined network cost by determining operational cost and a response delay. They subsequently proposed path selection and sleep scheduling methods by adopting a game-theoretic approach to minimize the network cost. However, acquiring the information needed to determine the path from an AP to a MECS is difficult. Theseinclude the aggregate load on each router and each MECS in the system and the edges in the selected path. In addition, multiple iterations are required between the central coordinator and all APs. In [24], minority game theory was used to devise a server activation scheme that can support the requirements associated with the quality of experience of users in an energy-efficient manner. However, the authors assumed that the task arrival pattern follows a Poisson distribution, which may not reflect real-world scenarios. In [25], the Bayesian learning automata was used for a MEC server to make autonomous sleep decisions. The reputation of a MECS is developed according to the service quality evaluation presented by the users. However, a MECS is required to obtain feedback from the MEC environment regarding its decisions. More specifically, global information should be shared among the MEC servers to make sleep decisions. Additionally, their method requires a human-based evaluation function that is subjective for the computing system. Furthermore, MEC servers are assumed to be synchronized in terms of decision time and information shared among them. In [6], the authors formulated a joint optimization problem to minimize the energy consumption of mobile-edge computing and caching (MEC)-enabled software-defined mobile networks. To resolve the problem, they decomposed the original problem into content source selection and bandwidth provisioning problems using dual decomposition.