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Mobile edge computing (MEC) is one of the promising solutions to processcomputational-intensive tasks within short latency for emergingInternet-of-Things (IoT) use cases, e.g., virtual reality (VR), augmentedreality (AR), autonomous vehicle. Due to the coexistence of heterogeneousservices in MEC system, the task arrival interval and required execution timecan vary depending on services. It is challenging to schedule computationresource for the services with stochastic arrivals and runtime at an edgeserver (ES). In this paper, we propose a flexible computation offloadingframework among users and ESs. Based on the framework, we propose aLyapunov-based algorithm to dynamically allocate computation resource forheterogeneous time-critical services at the ES. The proposed algorithmminimizes the average timeout probability without any prior knowledge on taskarrival process and required runtime. The numerical results show that, comparedwith the standard queuing models used at ES, the proposed algorithm achieves atleast 35% reduction of the timeout probability, and approximated utilizationefficiency of computation resource to non-cause queuing model under variousscenarios.

Computation Resource Allocation for Heterogeneous Time-Critical IoT Services in MEC