Junfei Xie’s research while affiliated with San Diego State University and other places

Extreme Edge Computing (EEC) promotes sustainable computing by reducing reliance on centralized data centres and decreasing their environmental impact. By using extreme edge devices to handle computing requests, the EEC reduces the energy demands for data transmission and execution, thereby reducing carbon footprints. However, EEC introduces challenges due to the mobile, heterogeneous, and resource-limited nature of these devices. Additionally, tasks are often complex and interdependent, complicating offloading and workload orchestration. The dynamicity of EEC systems, where both task generation and resources can be mobile, alongside task inter-dependencies, escalates the complexity of task offloading and workload management. To tackle these complexities, task partitioning emerges as a viable strategy. Moreover, in dynamic edge computing scenarios, resource demand remains unpredictable, emphasizing the critical need to optimize resource utilization efficiently. In this paper, we investigate the problem of tasks with inter-dependencies offloading in an EEC environment where mobile and resource-constrained edge devices are employed as computing resources. In this regard, a partitioning-based Deep Reinforcement Learning (DRL) for Dependent sub-Task Orchestration (DeTOrch) model is proposed. DeTOrch uses a state-of-the-art partitioning method for decomposing tasks and proposes a novel mobility task-orchestration mechanism to minimize the task completion time and maximize the use of edge devices’ resource. The simulation results show that the proposed model can significantly improve the task success rate and decrease task completion time. In addition, in various scenarios with different levels of mobility, the proposed model outperforms the baselines while utilizing the resource of edge devices.

Vulnerable road users (VRUs) such as pedestrians, cyclists, motorcyclists, and animals are at the highest risk in the road traffic environment since they move in the environment without any protection. Various applications and architectures that are applicable to Intelligence Transportation Systems (ITS) must be designed by considering this regard. Task offloading is a well-known approach in various ITS applications. Task of-floading in edge computing refers to the process of transferring certain computing tasks or workloads from a local device to edge nodes or servers located closer to the device. Orchestrating workload in an environment where both the task generator and destination device can be mobile is challenging while it is crucial for VRUs' safety. For example, when a user of a blind navigation assistant offloads a task, the success of that task is extremely important. Failure could potentially cause harm or negative consequences. This paper proposes a mobility-aware workload orchestration model for VRUs safety applications. To guarantee a high success rate and reduce the risk of task failure due to mobility, this model uses reinforcement learning to adapt to the dynamic edge environment. This model also employs a heuristic algorithm for device trajectory prediction from basic device location data. In addition, a novel technique is developed for task transferring to avoid task failure of mobile resources. The results show the proposed model outperforms in increasing the task success rate and decreasing the task failure rate due to mobility compared to the baselines.