PtrTasking: Pointer Network Based Task Scheduling for Multi-Connectivity Enabled MEC Services

Interactive services of mobile edge computing demand low latency in task handling, which cannot be easily satisfied due to limited per-user computing power on an edge server. Fortunately, a user can establish links with multiple base stations and the co-located edge servers in 5 G and beyond. By leveraging multi-connectivity, tasks of a computing service can be dispatched to multiple edge servers. This approach can also improve the quality of services, since tasks allocated to edge servers can be prioritized according to link quality. To exploit the benefits of multi-connectivity, a task scheduling problem is formulated to minimize latency and maximize reliability of the application, subject to task dependency and resource constraints. This problem is hard to solve due to NP-hardness and time-varying conditions. To this end, a pointer network based scheme called PtrTasking is developed to obtain a deep-learning model for the schedules and then infer new schedules on-line. To support time-varying conditions (e.g., a new application or evident change of computing load), a training strategy called PtrTrain is designed to retrain PtrTasking in a fast and efficient way. Experiments based on real-world datasets demonstrate that PtrTasking significantly improves latency and reliability, as compared to the baseline schemes.