Edge intelligence is an emerging paradigm in edge computing that deploys Deep Neural Network (DNN) models on edge servers with limited storage and computation capacities to provide inference services for high mobility and real-time applications, such as autonomous driving or smart surveillance, with varying accuracy and delay requirements. Adapting application configurations (e.g., image resolution or video frame rate) while selecting different DNN models and deployment locations can provide high-accuracy, low-delay inference services that meet user requirements. However, the configurations and DNN models of various inference services are highly heterogeneous. As balancing inference accuracy, resource cost, and delay is a multi-objective programming problem, it is a great challenge to obtain the optimal solution. To address this challenge, we propose a novel online framework to jointly optimize the configuration adaption, DNN model selection, and deployment for heterogeneous inference services. Specifically, we first formulate this joint optimization problem as an integer linear programming problem and prove it is NP-hard. Then, we further model the problem as a Partial Observable Markov Decision Process (POMDP) and solve it by developing a Heterogeneous-Agent Reinforcement Learning (HARL) based algorithm, named Heterogeneous Inference Service ProvidER (HISPER). It allows agents to have different action spaces corresponding to different types of configurations and DNN models. Finally, extensive experiments demonstrate that the proposed algorithm outperforms other state-of-the-art counterparts.

Edge-enabled Vehicular Metaverse (EVM) is a new paradise supported by various compute-intensive Virtual Vehicle Services (VVSs), where users can immerse and enjoy their spiritual world. User immersion is critical during VVS provisioning in the EVM, yet it can be weakened or curtailed by a sense of disengagement caused by unknown failures. Providing redundant backups VVSs (BVVSs) and keeping the Age of Backup Information (AoBI) could effectively resist and avoid this disengagement when failures occur. However, the trajectories of mobile vehicles are unknown and dynamic, which makes it challenging to optimally migrate VVSs and BVVSs or adjust the update frequency of backup information in real-time, so as to ensure service reliability and AoBI while minimizing the cost of accepting VVS-based metaverse services. In this paper, the above long-term issue is first decomposed into discrete single-slot sub-problems that are modeled as integer linear programming problems. Then, a comprehensive resource explorer named spotlighter is designed, where the first and second parts are a metaverse service home prediction algorithm based on deep learning and a VVS migration algorithm based on randomized rounding, respectively. By tracking the dynamical locations of service homes based on current and historical information, the former can help the latter to adaptively minimize migration costs on VVS re-instantiation and traffic transmission among services and moving vehicles. Finally, a cost-adaptive AoBI guarantee algorithm is merged in spotlighter to ensure the freshness of backup status, by trading-off synchronization cost on BVVS migration, backup update, and backup synchronization. Theoretical analyses and experiments based on real databases show that our algorithms are promising compared with baseline algorithms.