An Energy-Efficient Service Scheduling Algorithm in Federated Edge Cloud

Abstract

Federated edge cloud (FEC) is an edge cloud environment where multiple edge servers in a single administrative domain collaborate together to provide real-time services. This environment reduces the possibility of violating the quality of service (QoS) requirements of target services by locating delay-sensitive services at nearby edge servers instead of deploying them on the cloud. However, as the number of edge servers increases, the amount of energy consumed by servers and network switches also increases. This creates another challenge for how to schedule delay-sensitive services over FEC, while minimizing the total energy consumption and reducing the QoS violation of a service at the same time. This paper proposes an energy-efficient service scheduling algorithm in FEC. The proposed algorithm is based on an observation that as the number of edge servers along the service path is reduced, the total energy consumption can be minimized. Traditional approaches place services using their maximum traffic requirements to ensure QoS without considering the actual traffic change. In contrast, the proposed algorithm schedules them with actual traffic requirements to increase the number of services co-located in a single server. This maximizes the consolidation of services in a single server and thus minimizes the energy consumption. Moreover, when edge servers are overloaded, the proposed algorithm reconfigures the service path such that service migration overhead and energy consumption are minimized while guaranteeing the QoS requirements of services. The simulation results show that the proposed algorithm improves energy efficiency by up to 21% and lowers the service violation rate by up to 80% against existing approaches.