Computation Offloading and Band Selection for IoT Devices in Multi-Access Edge Computing

Abstract

The advent of Multi-Access Edge Computing (MEC) has enabled service providers to mitigate high network latencies often encountered in accessing cloud services. The key idea of MEC involves service providers deploying containerized application services on MEC servers situated near Internet-of-Things (IoT) device users. The users access these services via wireless base stations with ultra low latency. Computation tasks of IoT devices can then either be executed locally on the devices or on the MEC servers. A key cornerstone of the MEC environment is an offloading policy utilized to determine whether to execute computation tasks on IoT devices or to offload the tasks to MEC servers for processing. In this work, we propose a two phase Probabilistic Model Checking based offloading policy catering to IoT device user preferences. The first stage evaluates the trade-offs between local vs server execution while the second stage evaluates the trade-offs between choice of wireless communication bands for offloaded tasks. We present experimental results in practical scenarios on data gathered from an IoT test-bed setup with benchmark applications to show the benefits of an adaptive preference-aware approach over conventional approaches in the MEC offloading context.