Optimizing Service Migration in IoT Edge Networks: Digital Twin-Based Computation and Energy-Efficient Approach

The integration of the Internet of Things (IoT) with Mobile Edge Computing (MEC) has the potential to deliver ubiquitous connectivity and low-latency computational services. However, the growing proliferation of IoT devices and the burden of computational services requested at the network edge present significant challenges in enhancing the Quality of Service (QoS). More specifically, the service migration problem is particularly crucial for the timely data processing of latency-constrained services. Service migration involves the dynamic movement of computation services between MEC servers in order to prevent MEC servers from being overloaded. To handle the service migration problem, digital twin, as one of the promising technologies, simulates and predicts IoT edge network behaviors to find the best assignment between IoT devices and MEC servers. In this regard, this paper proposes a digital twin-assisted service migration model in IoT edge networks. Taking the computation time and energy consumption as QoS metrics, we formulate a service migration optimization problem to minimize both average latency and energy consumption on MEC servers. Then, we design a service migration algorithm to define a list of migration candidates and find the best assignment between IoT devices and MEC servers to balance the traffic load on MEC servers. Through comprehensive evaluations, we demonstrate the effectiveness of the proposed digital twin-based computation and energy-efficient model, optimize service migration decision by achieving lower computation time and energy consumption.