This paper proposes an energy-efficient service scheduling mechanism in federated edge cloud (FEC) called ESFEC, which consists of a placement algorithm and three types of reconfiguration algorithms. Unlike traditional approaches, ESFEC places delay-sensitive services on the edge servers in nearby edge domains instead of clouds. In addition, ESFEC schedules services with actual traffic requirements rather than maximum traffic requirements to ensure QoS. This increases the number of services co-located in a single server and thereby reduces the total energy consumed by the services. ESFEC reduces the service migration overhead using a reinforcement learning (RL)-based reconfiguration algorithm, ESFEC-RL, that can dynamically adapt to a changing environment. Additionally, ESFEC includes two different heuristic algorithms, ESFEC-EF (energy first) and ESFEC-MF (migration first), which are more suitable for real-scale scenarios. The simulation results show that ESFEC improves energy efficiency by up to 28% and lowers the service violation rate by up to 66% compared to a traditional approach used in the edge cloud environment.

本文提出了一种名为 ESFEC 的联邦边缘云 (FEC) 中的节能服务调度机制，它由一种放置算法和三种重新配置算法组成。与传统方法不同，ESFEC 将延迟敏感服务放在附近边缘域的边缘服务器上，而不是云上。此外，ESFEC 以实际流量需求而非最大流量需求来调度业务，以保证 QoS。这增加了位于单个服务器中的服务数量，从而减少了服务消耗的总能量。ESFEC 使用基于强化学习 (RL) 的重新配置算法 ESFEC-RL 来降低服务迁移开销，该算法可以动态适应不断变化的环境。此外，ESFEC 包括两种不同的启发式算法，ESFEC-EF（能量优先）和ESFEC-MF（迁移优先），更适合真实规模的场景。仿真结果表明，与边缘云环境中使用的传统方法相比，ESFEC 将能效提高了 28%，并将服务违规率降低了 66%。