# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
###### Abstract  
The energy consumption of a multi-access edge computing (MEC) system must be reduced to save operational costs. Determining a set of active MEC servers (MECSs) that can minimize the energy consumption of the MEC system while satisfying the service delay requirements of the tasks is an NP-complete problem. To solve this problem, we take a bio-inspired approach. We note that the sleep control problem of the MECS differentiates the operational mode among neighboring MECSs. Therefore, by mimicking the cell differentiation process in a biological system, we designed a distributed sleep control method. Each MECS periodically gathers the utilization and delta levels of the neighboring MECSs. Subsequently, by using the gathered information and the Delta-Notch inter-cell signaling model, a MECS autonomously decides whether to sleep. We evaluated the performance of our method through extensive simulations. Compared with a conventional method, the proposed method reduces energy consumption in a MEC system by more than 13% while providing a comparable service delay. In addition, our method reduces the variations in the service delay by more than 35%. MEC energy saving; MECS sleep control; Task QoS; inter-cell signaling model; bio-inspired approach +
Footnote †: journal: Applied Sciences  
The sleep control problem in a MEC system is a combinatorial problem that determines the set of MECSs that must be in the sleep state for a given task distribution in a MEC system. Optimization-based methods have been proposed to address the energy-saving issue in MEC systems [5; 6; 7]. These methods formulate an optimization problem and transform the original problem into a set of solvable mathematical forms that can be used to design an algorithm to solve the problem. However, the complexities of optimization-based algorithms increase exponentially with the increase in the number of MECS. In addition, rapidly adjusting to the dynamics of a MEC system is difficult because these algorithms are based on a network snapshot. Therefore, to ensure the scalability and adaptability of a MEC system, a lightweight distributed approach is required to reduce energy consumption while satisfying the service delay requirement. To achieve this objective, we adopted a bio-inspired model and devised a method for a MECS to make an automatic sleep decision. Bio-inspired approaches have drawn considerable attention in the communications and networking domains for resolving the scalability issue in an adaptive and distributed manner [8; 9]. Bio-inspired modeling techniques exploit the inherent adaptability of biological systems by mimicking the behavior of objects in such systems to solve engineering problems. We regarded the MECS sleep decision process as a state differentiation process among the MECSs. More specifically, each MECS differentiates the operational state, i.e., sleep or active, from the neighboring MECSs by considering the relative load to those of the neighbors. We subsequently designed a sleep decision process using the Delta-Notch lateral inhibition model that explains the process by which the fate of an endothelial cell is determined during sprouting angiogenesis using the delta protein level of the cell relative to those of its surrounding cells. Additionally, we devised a MECS activation scheme. The load on a MECS varies with time and space [10]. As the number of tasks offloaded to an active MECS increases, the MECS is likely to violate the service delay requirement. To avoid this situation, we enabled an active MECS to wake up neighboring MECSs that are sleeping before the service time exceeds a predetermined threshold value. Through extensive simulations, we analyzed the performance of the proposed method in terms of energy consumption and the queuing delay of a MEC system.