# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 2 Related Works  
### Resource Management in a MEC System  
To satisfy the quality of service requirements of the tasks offloaded to a MEC system in a resource-efficient manner, various methods have been proposed for diverse problems. The authors in [11] investigate a DNN model splitting problem in a hybrid mobile edge environment. They formulate a utility maximization problem with SLA, priority, and fairness constraints. After transforming the optimization problem into the multi-choice knapsack problem, they propose a feasible algorithm called HiTDL to solve the transformed problem. HiTDL achieves an optimal system throughput by making an informed decision on DNN model splitting and resource allocation while achieving SLA, priority, and fairness goals. In [12], the demand response problem in edge cloud is explored. After deriving the operational costs of the cloudlet operator and the service providers, the authors formulate a long-term social cost minimization problem. Then, they propose an online auction algorithm called EdgeDR for performing the power emergency demand response and the computing demand response at the edge cloud. The network selection and service placement problem are inspected in [13]. By incorporating the queuing delay, switching cost, and communication delay, an offline network selection and service placement optimization problem is formulated. The authors decompose the long-term optimization problem into a series of one-shot subproblems. A two-phase algorithm based on the matching game and the coalitional game is proposed to solve the subproblems. The proposed algorithm improves the QoS by balancing the queuing delay, communication delay, and service switching cost. The authors in [14] tackle the multi-task transfer learning problem on the edge. They propose a task importance metric that measures the contribution of a task to the improvement of the decision-making performance. Then, they formulate the task allocation problem for the multi-task transfer learning on the edge by considering the task importance, the execution time, and resource limitations. To solve the formulated problem, a data-driven cooperative task allocation (DCTA) method is proposed by using reinforcement learning. DCTA reduces the processing time and saves energy consumption. In [15], the authors probe into the container caching problem in a MEC system. By considering the startup delay of a container, service latency cost, and container retention cost, they formulate a system cost minimization problem as a linear integer problem. To resolve the problem, they propose an online algorithm named O-RDC. To reduce the system cost, O-RDC opportunistically distributes requests by considering the resource capacities and network delays between edge nodes. An admission control policy for time-sensitive services is investigated in [16]. To maximize the revenue for the service provider while guaranteeing QoS for the accepted service requests, a service throughput maximization problem is formulated. A threshold structure is carefully defined and an optimal admission control mechanism called OA2 is devised