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

TL;DR

This paper introduces a Lyapunov-based dynamic resource allocation algorithm for MEC systems, effectively managing heterogeneous IoT services with stochastic task arrivals to minimize timeout probability and improve resource utilization.

Contribution

It presents a novel flexible offloading framework and a Lyapunov-based algorithm that adaptively allocates resources without prior knowledge of task arrivals or runtimes.

Findings

At least 35% reduction in timeout probability.

Improved resource utilization efficiency.

Effective management of heterogeneous IoT services.

Abstract

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.