  Mobile edge computing (MEC) is one of the promising solutions to process
computational-intensive tasks within short latency for emerging
Internet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmented
reality (AR), autonomous vehicle. Due to the coexistence of heterogeneous
services in MEC system, the task arrival interval and required execution time
can vary depending on services. It is challenging to schedule computation
resource for the services with stochastic arrivals and runtime at an edge
server (ES). In this paper, we propose a flexible computation offloading
framework among users and ESs. Based on the framework, we propose a
Lyapunov-based algorithm to dynamically allocate computation resource for
heterogeneous time-critical services at the ES. The proposed algorithm
minimizes the average timeout probability without any prior knowledge on task
arrival process and required runtime. The numerical results show that, compared
with the standard queuing models used at ES, the proposed algorithm achieves at
least 35% reduction of the timeout probability, and approximated utilization
efficiency of computation resource to non-cause queuing model under various
scenarios.
