An Energy-Efficient Sleeping Strategy for Multi-Access Edge Computing

In this paper, we focus on the scenario with offloading of computationally intensive tasks with delay constraints from users equipment (UEs) to multi-access edge computing (MEC) servers. To avoid user's dissatisfaction with offered quality of service, the computing resources should be able to handle even peak hours. As a result, a dense deployment of MEC servers should be considered in order to bring sufficient computing resources close to the UEs, thus enabling a low delay services. However, at the same time, the dense deployment of powerful MEC servers results, among others, in a high energy consumption. In this paper, we address the high energy consumption problem via a smart sleeping of the MEC servers while preserving quality of service for the UEs. To this end, we determine a set of MEC servers that should stay active and provide computation resources for the offloaded tasks while still meeting UEs requirements on delay. We formulate the problem of selecting the MEC server that can be set into sleep mode to save energy as a minimum set cover problem. Then, we propose a solution to minimize the energy consumption based on branch-and-bound algorithm to activate the MEC servers for computation ensuring the UEs requirement on delay. The effectiveness of the proposed solution is demonstrated through simulations showing that the proposal allows to save up to 34.9% of energy compared to state-of-the-art works while even slightly improving the ratio of offloaded tasks processed within required delay.