Dynamic Offloading in Mobile Edge Computing With Traffic-Aware Network Slicing and Adaptive TD3 Strategy

Network slicing and computation offloading play a pivotal role in enabling edge service providers to handle dynamic service demands effectively. However, traffic fluctuations and resource diversity pose significant challenges, often constrained by static configurations lacking flexibility. To overcome these limitations, this letter presents FlexSlice, a dynamic offloading framework designed to optimize resource allocation in mobile edge networks. Our approach leverages a sparse multi-head graph attention mechanism for precise traffic prediction, capturing complex spatio-temporal dependencies to enhance network slicing decisions. Additionally, we present an adaptive offloading strategy based on the twin delayed deep deterministic policy gradient algorithm, which incorporates twin critics and prioritized experience replay to improve decision-making under dynamic conditions. Simulation results confirm FlexSlice’s outstanding performance and adaptability in diverse operational scenarios, achieving higher profits and reliable quality of service.